txt/2106.07286.txt

This paper has been accepted for publication at the
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, 2021. ©IEEE
Time Lens: Event-based Video Frame Interpolation
Stepan Tulyakov*;1Daniel Gehrig;2Stamatios Georgoulis1Julius Erbach1
Mathias Gehrig2Yuanyou Li1Davide Scaramuzza2
1Huawei Technologies, Zurich Research Center
2Dept. of Informatics, Univ. of Zurich and Dept. of Neuroinformatics, Univ. of Zurich and ETH Zurich
Figure 1: Qualitative results comparing our proposed method, Time Lens, with DAIN [3] and BMBC [29]. Our method can
interpolate frames in highly-dynamic scenes, such as while spinning an umbrella (top row) and bursting a balloon (bottom
row). It does this by combining events (b) and frames (a).
Abstract
State-of-the-art frame interpolation methods generate
intermediate frames by inferring object motions in the
image from consecutive key-frames. In the absence of
additional information, first-order approximations, i.e.
optical flow, must be used, but this choice restricts the
types of motions that can be modeled, leading to errors
in highly dynamic scenarios. Event cameras are novel
sensors that address this limitation by providing auxiliary
visual information in the blind-time between frames. They
asynchronously measure per-pixel brightness changes and
do this with high temporal resolution and low latency.
Event-based frame interpolation methods typically adopt a
synthesis-based approach, where predicted frame residuals
are directly applied to the key-frames. However, while these
approaches can capture non-linear motions they suffer
from ghosting and perform poorly in low-texture regions
with few events. Thus, synthesis-based and flow-based
approaches are complementary. In this work, we introduce
*indicates equal contributionTime Lens , a novel method that leverages the advantages of
both. We extensively evaluate our method on three synthetic
and two real benchmarks where we show an up to 5.21
dB improvement in terms of PSNR over state-of-the-art
frame-based and event-based methods. Finally, we release
a new large-scale dataset in highly dynamic scenarios,
aimed at pushing the limits of existing methods.
Multimedia Material
The High-Speed Event and RGB (HS-ERGB) dataset
and evaluation code can be found at: http://rpg.ifi.
uzh.ch/timelens
1. Introduction
Many things in real life can happen in the blink of an
eye. A hummingbird flapping its wings, a cheetah accel-
erating towards its prey, a tricky stunt with the skateboard,
or even a baby taking its first steps. Capturing these mo-
ments as high-resolution videos with high frame rates typi-arXiv:2106.07286v1  [cs.CV]  14 Jun 2021cally requires professional high-speed cameras, that are in-
accessible to casual users. Modern mobile device producers
have tried to incorporate more affordable sensors with sim-
ilar functionalities into their systems, but they still suffer
from the large memory requirements and high power con-
sumption associated with these sensors.
Video Frame Interpolation (VFI) addresses this problem,
by converting videos with moderate frame rates high frame
rate videos in post-processing. In theory, any number of
new frames can be generated between two keyframes of
the input video. Therefore, VFI is an important problem
in video processing with many applications, ranging from
super slow motion [10] to video compression [42].
Frame-based interpolation approaches relying solely
on input from a conventional frame-based camera that
records frames synchronously and at a fixed rate. There are
several classes of such methods that we describe below.
Warping-based approaches [20, 10, 44, 21, 29] combine
optical flow estimation [8, 16, 36] with image warping [9],
to generate intermediate frames in-between two consecutive
key frames. More specifically, under the assumptions of lin-
ear motion and brightness constancy between frames, these
works compute optical flow and warp the input keyframe(s)
to the target frame, while leveraging concepts, like contex-
tual information [20], visibility maps [10], spatial trans-
former networks [44], forward warping [21], or dynamic
blending filters [29], to improve the results. While most of
these approaches assume linear motion, some recent works
assume quadratic [43] or cubic [5] motions. Although these
methods can address non-linear motions, they are still lim-
ited by their order, failing to capture arbitrary motion.
Kernel-based approaches [22, 23] avoid the explicit mo-
tion estimation and warping stages of warping-based ap-
proaches. Instead, they model VFI as local convolution over
the input keyframes, where the convolutional kernel is esti-
mated from the keyframes. This approach is more robust to
motion blur and light changes. Alternatively, phase-based
approaches [18] pose VFI as a phase shift estimation prob-
lem, where a neural network decoder directly estimates the
phase decomposition of the intermediate frame. However,
while these methods can in theory model arbitrary motion,
in practice they do not scale to large motions due to the lo-
cality of the convolution kernels.
In general, all frame-based approaches assume simplis-
tic motion models (e.g. linear) due to the absence of vi-
sual information in the blind-time between frames, which
poses a fundamental limitation of purely frame-based VFI
approaches. In particular, the simplifying assumptions rely
on brightness and appearance constancy between frames,
which limits their applicability in highly dynamic scenar-
ios such as ( i) for non-linear motions between the input
keyframes, ( ii) when there are changes in illumination or
motion blur, and ( iii) non-rigid motions and new objects ap-pearing in the scene between keyframes.
Multi-camera approaches. To overcome this limita-
tion, some works seek to combine inputs from several
frame-based cameras with different spatio-temporal trade-
offs. For example, [1] combined low-resolution video with
high resolution still images, whereas [25] fused a low-
resolution high frame rate video with a high resolution low
frame rate video. Both approaches can recover the miss-
ing visual information necessary to reconstruct true object
motions, but this comes at the cost of a bulkier form factor,
higher power consumption, and a larger memory footprint.
Event-based approaches. Compared to standard frame-
based cameras, event cameras [14, 4] do not incur the afore-
mentioned costs. They are novel sensors that only report the
per-pixel intensity changes, as opposed to the full intensity
images and do this with high temporal resolution and low
latency on the order of microseconds. The resulting output
is an asynchronous stream of binary “events” which can be
considered a compressed representation of the true visual
signal. These properties render them useful for VFI under
highly dynamic scenarios (e.g. high-speed non-linear mo-
tion, or challenging illumination).
Events-only approaches reconstruct high frame rate
videos directly from the stream of incoming events using
GANs [38], RNNs [32, 33, 34], or even self-supervised
CNNs [28], and can be thought of as a proxy to the VFI
task. However, since the integration of intensity gradients
into an intensity frame is an ill-posed problem, the global
contrast of the interpolated frames is usually miscalculated.
Moreover, as in event cameras intensity edges are only ex-
posed when they move, the interpolation results are also de-
pendent on the motion.
Events-plus-frames approaches. As certain event cam-
eras such as the Dynamic and Active VIsion Sensor
(DA VIS) [4] can simultaneously output the event stream and
intensity images – the latter at low frame rates and prone
to the same issues as frame-based cameras (e.g. motion
blur) – several works [26, 41, 11, 37] use both streams of
information. Typically, these works tackle VFI in conjunc-
tion with de-blurring, de-noising, super-resolution, or other
relevant tasks. They synthesize intermediate frames by
accumulating temporal brightness changes, represented by
events, from the input keyframes and applying them to the
key frames. While these methods can handle illumination
changes and non-linear motion they still perform poorly
compared to the frame-based methods (please see § 3.2),
as due to the inherent instability of the contrast threshold
and sensor noise, not all brightness changes are accurately
registered as events.
Our contributions are as follows
1. We address the limitations of all aforementioned
methods by introducing a CNN framework, named
Time Lens , that marries the advantages of warping-Figure 2: Proposed event-based VFI approach.
and synthesis-based interpolation approaches. In our
framework, we use a synthesis-based approach to
ground and refine results of high-quality warping-
based approach and provide the ability to handle illu-
mination changes and new objects appearing between
keyframes (refer Fig. 7),
2. We introduce a new warping-based interpolation ap-
proach that estimates motion from events, rather than
frames and thus has several advantages: it is more ro-
bust to motion blur and can estimate non-linear mo-
tion between frames. Moreover, the proposed method
provides a higher quality interpolation compared to
synthesis-based methods that use events when event
information is not sufficient or noisy.
3. We empirically show that the proposed Time Lens
greatly outperforms state-of-the-art frame-based and
event-based methods, published over recent months,
on three synthetic and two real benchmarks where we
show an up to 5.21 dB improvement in terms of PSNR.
2. Method
Problem formulation. Let us assume an event-based
VFI setting, where we are given as input the left I0and
rightI1RGB key frames, as well as the left E0!and right
E!1event sequences , and we aim to interpolate (one or
more) new frames ^Iat random timesteps in-between the
key frames. Note that, the event sequences ( E0!,E!1)
contain all asynchronous events that are triggered from the
moment the respective (left I0or rightI1) key RGB frame
is synchronously sampled, till the timestep at which we
want to interpolate a new frame ^I. Fig. 2 illustrates the
proposed event-based VFI setting.
System overview. To tackle the problem under consid-
eration we propose a learning-based framework, namely
Time Lens , that consists of four dedicated modules that
serve complementary interpolation schemes, i.e. warping-
based and synthesis-based interpolation. In particular, (1)
thewarping-based interpolation module estimates a new
frame by warping the boundary RGB keyframes using op-
tical flow estimated from the respective event sequence; (2)
thewarping refinement module aims to improve this esti-
mate by computing residual flow; (3) the interpolation by
synthesis module estimates a new frame by directly fusing
the input information from the boundary keyframes and the
event sequences; finally (4) the attention-based averaging
module aims to optimally combine the warping-based andsynthesis-based results. In doing so, Time Lens marries
the advantages of warping- and synthesis-based interpola-
tion techniques, allowing us to generate new frames with
color and high textural details while handling non-linear
motion, light changes, and motion blur. The workflow of
our method is shown in Fig. 3a.
All modules of the proposed method use the same back-
bone architecture, which is an hourglass network with skip
connections between the contracting and expanding parts,
similar to [10]. The backbone architecture is described
in more detail in the supplementary materials. Regarding
the learning representation [7] used to encode the event
sequences, all modules use the voxel grid representation.
Specifically, for event sequence E0!endwe compute a
voxel gridV0!endfollowing the procedure described
in [46]. In the following paragraphs, we analyze each mod-
ule and its scope within the overall framework.
Interpolation by synthesis , as shown in Fig. 3b, directly
regresses a new frame ^Isyngiven the left I0and rightI1
RGB keyframes and events sequences E0!andE!1re-
spectively. The merits of this interpolation scheme lie in
its ability to handle changes in lighting, such as water re-
flections in Fig. 6 and a sudden appearance of new objects
in the scene, because unlike warping-based method, it does
not rely on the brightness constancy assumption. Its main
drawback is the distortion of image edges and textures when
event information is noisy or insufficient because of high
contrast thresholds, e.g. triggered by the book in Fig. 6.
Warping-based interpolation , shown in Fig. 3d, first
estimates the optical flow F!0andF!1between a la-
tent new frame ^Iand boundary keyframes I0andI1using
eventsE!0andE!1respectively. We compute E!0,
by reversing the event sequence E0!, as shown in Fig. 4.
Then our method uses computed optical flow to warp the
boundary keyframes in timestep using differentiable in-
terpolation [9], which in turn produces two new frame esti-
mates ^Iwarp
0!and^Iwarp
1!.
The major difference of our approach from the tradi-
tional warping-based interpolation methods [20, 10, 21, 43],
is that the latter compute optical flow between keyframes
using the frames themselves and then approximate opti-
cal flow between the latent middle frame and boundary
by using a linear motion assumption. This approach does
not work when motion between frames is non-linear and
keyframes suffer from motion blur. By contrast, our ap-
proach computes the optical flow from the events, and thus
can naturally handle blur and non-linear motion. Although
events are sparse, the resulting flow is sufficiently dense as
shown in Fig. 3d, especially in textured areas with dominant
mostion, which is most important for interpolation.
Moreover, the warping-based interpolation approach re-
lying on events also works better than synthesis-based
method in the scenarios when event data is noisy or not(a) Overview of the proposed method.
 (b) Interpolation by synthesis module.
(c) Attention-based averaging module.
(d) Warping-based interpolation module.
(e) Warping refinement module.
Figure 3: Structure of the proposed method. The overall workflow of the method is shown in Fig. 3a and individual modules
are shown in Fig. 3d, 3b, 3e and 3c. In the figures we also show loss function that we use to train each module. We show
similar modules in the same color across the figures.
Figure 4: Example of an event sequence reversal.
sufficient due to high contrast thresholds, e.g. the book in
Fig. 6. On the down side, this method still relies on the
brightness constancy assumption for optical flow estimation
and thus can not handle brightness changes and new ob-
jects appearing between keyframes, e.g. water reflections
in Fig. 6.
Warping refinement module computes refined interpo-
lated frames, ^Irefine
0!and^Irefine
1!, by estimating residual op-
tical flow, 
F!0and
F!1respectively, between the
warping-based interpolation results, ^Iwarp
0!and^Iwarp
1!, and
the synthesis result ^Isyn
. It then proceeds by warping ^Iwarp
0!
and^Iwarp
1!for a second time using the estimated residual
optical flow, as shown in Fig. 3e. The refinement module
draws inspiration from the success of optical flow and dis-
parity refinement modules in [8, 27], and also by our ob-
servation that the synthesis interpolation results are usually
perfectly aligned with the ground-truth new frame. Besides
computing residual flow, the warping refinement module
also performs inpainting of the occluded areas, by fillingthem with values from nearby regions.
Finally, the attention averaging module, shown in
Fig. 3c, blends in a pixel-wise manner the results of synthe-
sis^Isyn
and warping-based interpolation ^Irefine
0!and^Irefine
1!to
achieve final interpolation result ^I. This module leverages
the complementarity of the warping- and synthesis-based
interpolation methods and produces a final result, which is
better than the results of both methods by 1.73 dB in PSNR
as shown in Tab. 1 and illustrated in Fig. 6.
A similar strategy was used in [21, 10], however these
works only blended the warping-based interpolation results
to fill the occluded regions, while we blend both warping
and synthesis-based results, and thus can also handle light
changes. We estimate the blending coefficients using an at-
tention network that takes as an input the interpolation re-
sults, ^Irefine
0!,^Irefine
1!and^Isyn, the optical flow results F!0
andF!1and bi-linear coefficient , that depends on the
position of the new frame as a channel with constant value.
2.1. High Speed Events-RGB (HS-ERGB) dataset
Due to the lack of available datasets that combine
synchronized, high-resolution event cameras and standard
RGB cameras, we build a hardware synchronized hybrid
sensor which combines a high-resolution event camera withEvent Camera
Prophesee Gen4M 720p
Resolution 1280   720 
RGB Camera
FLIR BlackFly S 
Resolution: 1440   1080
2.5 cm baselineFigure 5: Illustration of the dual camera setup. It comprises
a Prophesee Gen4 720p monochrome event camera (top)
and a FLIR BlackFly S RGB camera (bottom). Both cam-
eras are hardware synchronized with a baseline of 2:5 cm
.
a high resolution and high-speed color camera. We use this
hybrid sensor to record a new large-scale dataset which we
term the High-Speed Events and RGB (HS-ERGB) dataset
which we use to validate our video frame interpolation ap-
proach. The hybrid camera setup is illustrated in Fig. 5.
It features a Prophesee Gen4 (1280 720) event camera
(Fig. 5 top) and a FLIR BlackFly S global shutter RGB cam-
era (1440 1080) (Fig. 5 bottom), separated by a baseline
of2:5 cm . Both cameras are hardware synchronized and
share a similar field of view (FoV). We provide a detailed
comparison of our setup against the commercially available
DA VIS 346 [4] and the recently introduced setup [40] in
the appendix.Compared to both [4] and [40] our setup is
able to record events at much higher resolution (1280 720
vs. 240 180 or 346 260) and standard frames at much
higher framerate (225 FPS vs. 40 FPS or 35 FPS) and with
a higher dynamic range (71.45 dB vs. 55 dB or 60 dB).
Moreover, standard frames have a higher resolution com-
pared to the DA VIS sensor (1440 1080 vs. 240 180) and
provide color. The higher dynamic range and frame rate,
enable us to more accurately compare event cameras with
standard cameras in highly dynamic scenarios and high dy-
namic range. Both cameras are hardware synchronized and
aligned via rectification and global alignment. For more
synchronization and alignment details see the appendix.
We record data in a variety of conditions, both indoors
and outdoors. Sequences were recorded outdoors with ex-
posure times as low as 100µsor indoors with exposure
times up to 1000 µs. The dataset features frame rates of
160 FPS, which is much higher than previous datasets, en-
abling larger frame skips with ground truth color frames.
The dataset includes highly dynamic close scenes with non-
linear motions and far-away scenes featuring mainly cam-
era ego-motion. For far-away scenes, stereo rectification is
sufficient for good per-pixel alignment. For each sequence,
alignment is performed depending on the depth either by
stereo rectification or using feature-based homography esti-
mation.To this end, we perform standard stereo calibration
between RGB images and E2VID [32] reconstructions and
rectify the images and events accordingly. For the dynamic
close scenes, we additionally estimate a global homogra-
phy by matching SIFT features [17] between these two im-ages. Note that for feature-based alignment to work well,
the camera must be static and objects of interest should only
move in a fronto-parallel plane at a predetermined depth.
While recording we made sure to follow these constraints.
For a more detailed dataset overview we refer to the sup-
plementary material.
3. Experiments
All experiments in this work are done using the Py-
Torch framework [30]. For training, we use the Adam
optimizer[12] with standard settings, batches of size 4 and
learning rate 104, which we decrease by a factor of 10ev-
ery 12 epoch. We train each module for 27 epoch. For the
training, we use large dataset with synthetic events gener-
ated from Vimeo90k septuplet dataset [44] using the video to
events method [6], based on the event simulator from [31].
We train the network by adding and training modules
one by one, while freezing the weights of all previously
trained modules. We train modules in the following or-
der: synthesis-based interpolation, warping-based interpo-
lation, warping refinement, and attention averaging mod-
ules. We adopted this training because end-to-end training
from scratch does not converge, and fine-tuning of the en-
tire network after pretraining only marginally improved the
results. We supervise our network with perceptual [45] and
L1losses as shown in Fig. 3b, 3d, 3e and 3c. We fine-tune
our network on real data module-by-module in the order
of training. To measure the quality of interpolated images
we use structural similarity (SSIM) [39] and peak signal to
noise ratio (PSNR) metrics.
Note, that the computational complexity of our interpo-
lation method is among the best: on our machine for image
resolutions of 640480, a single interpolation on the GPU
takes 878 ms for DAIN [3], 404 ms for BMBC [29], 138 ms
for ours, 84 ms for RRIN [13], 73 ms for Super SloMo [10]
and 33 ms for LEDVDI [15] methods.
3.1. Ablation study
To study the contribution of every module of the pro-
posed method to the final interpolation, we investigate the
interpolation quality after each module in Fig. 3a, and re-
port their results in Tab. 1. The table shows two notable re-
sults. First, it shows that adding a warping refinement block
after the simple warping block significantly improves the
interpolation result. Second, it shows that by attention aver-
aging synthesis-based and warping-based results, the inter-
polations are improved by 1.7 dB in terms of PSNR. This
is because the attention averaging module combines the ad-
vantages of both methods. To highlight this further, we il-
lustrate example reconstructions from these two modules in
Fig. 6. As can be seen, the warping-based module excels at
reconstructing textures in non-occluded areas (fourth col-
umn) while the synthesis module performs better in regionswith difficult lighting conditions (fifth column). The atten-
tion module successfully combines the best parts of both
modules (first column).
Figure 6: Complementarity of warping- and synthesis-
based interpolation.
Table 1: Quality of interpolation after each module on
Vimeo90k (denoising) validation set. For SSIM and PSNR
we show mean and one standard deviation. The best result
is highlighted.
Module PSNR SSIM
Warping interpolation 26.68 3.68 0.926 0.041
Interpolation by synthesis 34.10 3.98 0.964 0.029
Warping refinement 33.02 3.76 0.963 0.026
Attention averaging (ours) 35.83 3.70 0.976 0.019
3.2. Benchmarking
Synthetic datasets. We compare the proposed
method, which we call Time Lens , to four state-of-the-art
frame-based interpolation methods DAIN [3],RRIN [13],
BMBC [29], SuperSloMo [10], event-based video recon-
struction method E2VID [33] and two event and frame-
based methods EDI [26] and LEDVDI [15] on pop-
ular video interpolation benchmark datasets, such as
Vimeo90k (interpolation) [44], Middlebury [2]. During
the evaluation, we take original video sequence, skip 1
or 3 frames respectively, reconstruct them using interpola-
tion method and compare to ground truth skipped frames.
Events for event-based methods we simulate using [6] from
the skipped frames. We do not fine-tune the methods for
each dataset but simply use pre-trained models provided by
the authors. We summarise the results in Tab. 2.
As we can see, the proposed method outperforms other
method across datasets in terms of average PSNR (up to
8.82 dB improvement) and SSIM scores (up to 0.192 im-
provement). As before these improvements stem from the
use of auxiliary events during the prediction stage which
allow our method to perform accurate frame interpolation,
event for very large non-linear motions. Also, it has signif-
icantly lower standard deviation of the PSNR (2.53 dB vs.
4.96 dB) and SSIM (0.025 vs. 0.112) scores, which sug-
gests more consistent performance across examples. Also,we can see that PSNR and SSIM scores of the proposed
method degrades to much lesser degree than scores of the
frame-based methods (up to 1.6 dB vs. up to 5.4 dB), as we
skip and attempt to reconstruct more frames. This suggests
that our method is more robust to non-linear motion than
frame-based methods.
High Quality Frames (HQF) dataset. We also evalu-
ate our method on High Quality Frames (HQF) dataset [35]
collected using DA VIS240 event camera that consists of
video sequences without blur and saturation. During eval-
uation, we use the same methodology as for the synthetic
datasets, with the only difference that in this case we use
real events. In the evaluation, we consider two versions of
our method: Time Lens-syn , which we trained only on syn-
thetic data, and Time Lens-real , which we trained on syn-
thetic data and fine-tuned on real event data from our own
DA VIS346 camera. We summarise our results in Tab. 3.
The results on the dataset are consistent with the re-
sults on the synthetic datasets: the proposed method outper-
forms state-of-the-art frame-based methods and produces
more consistent results over examples. As we increase the
number of frames that we skip, the performance gap be-
tween the proposed method and the other methods widens
from 2.53 dB to 4.25 dB, also the results of other methods
become less consistent which is reflected in higher devia-
tion of PSNR and SSIM scores. For a more detailed dis-
cussion about the impact of frame skip length and perfor-
mance, see the appendix. Interestingly, fine-tuning of the
proposed method on real event data, captured by another
camera, greatly boosts the performance of our method by
an average of 1.94 dB. This suggest that existence of large
domain gap between synthetic and real event data.
High Speed Event-RGB dataset. Finally, we evaluate
our method on our dataset introduced in § 2.1. As clear from
Tab. 4, our method, again significantly outperforms frame-
based and frame-plus-event-based competitors. In Fig. 7 we
show several examples from the HS-ERGB test set which
show that, compared to competing frame-based method,
our method can interpolate frames in the case of nonlin-
ear (“Umbrella” sequence) and non-rigid motion (“Water
Bomb”), and also handle illumination changes (“Fountain
Schaffhauserplatz” and “Fountain Bellevue”).
4. Conclusion
In this work, we introduce Time Lens, a method that
can show us what happens in the blind-time between
two intensity frames using high temporal resolution in-
formation from an event camera. It works by leveraging
the advantages of synthesis-based approaches, which can
handle changing illumination conditions and non-rigid
motions, and flow-based approach, relying on motion
estimation from events. It is therefore robust to motion blur
and non-linear motions. The proposed method achievesTable 2: Results on standard video interpolation benchmarks such as Middlebury [2],Vimeo90k (interpolation) [44] and
GoPro [19]. In all cases, we use a test subset of the datasets. To compute SSIM and PSNR, we downsample the original
video and reconstruct the skipped frames. For Middlebury and Vimeo90k (interpolation), we skip 1 and 3 frames, and for
GoPro we skip 7 and 15 frames due its its high frame rate of 240 FPS. Uses frames andUses events indicate if a method uses
frames and events for interpolation. For event-based methods we generate events from the skipped frames using the event
simulator [6]. Color indicates if a method works with color frames. For SSIM and PSNR we show mean and one standard
deviation. Note, that we can not produce results with 3 skips on the Vimeo90k dataset, since it consists of frame triplet. We
show the best result in each column in bold and the second-best using underscore text.
Method Uses frames Uses events Color PSNR SSIM PSNR SSIM
Middlebury [2] 1 frame skip 3 frames skips
DAIN [3] 4 8 4 30.875.38 0.899 0.110 26.674.53 0.838 0.130
SuperSloMo [10] 4 8 4 29.755.35 0.880 0.112 26.43 5.30 0.823 0.141
RRIN [13] 4 8 4 31.085.55 0.8960.112 27.18 5.57 0.8370.142
BMBC [29] 4 8 4 30.836.01 0.897 0.111 26.86 5.82 0.834 0.144
E2VID [32] 8 4 8 11.262.82 0.427 0.184 26.86 5.82 0.834 0.144
EDI [26] 4 4 8 19.722.95 0.725 0.155 18.44 2.52 0.669 0.173
Time Lens (ours) 4 4 4 33.273.11 0.929 0.027 32.13 2.81 0.908 0.039
Vimeo90k (interpolation) [44] 1 frame skip 3 frames skips
DAIN [3] 4 8 4 34.204.43 0.962 0.023 - -
SuperSloMo [10] 4 8 4 32.934.23 0.948 0.035 - -
RRIN [13] 4 8 4 34.724.40 0.9620.029 - -
BMBC [29] 4 8 4 34.564.40 0.962 0.024 - -
E2VID [32] 8 4 8 10.082.89 0.395 0.141 - -
EDI [26] 4 4 8 20.743.31 0.748 0.140 - -
Time Lens (ours) 4 4 4 36.313.11 0.962 0.024 - -
GoPro [19] 7 frames skip 15 frames skips
DAIN [3] 4 8 4 28.814.20 0.876 0.117 24.39 4.69 0.7360.173
SuperSloMo [10] 4 8 4 28.984.30 0.875 0.118 24.38 4.78 0.747 0.177
RRIN [13] 4 8 4 28.964.38 0.876 0.119 24.324.80 0.749 0.175
BMBC [29] 4 8 4 29.084.58 0.8750.120 23.68 4.69 0.736 0.174
E2VID [32] 8 4 8 9.742.11 0.549 0.094 9.75 2.11 0.549 0.094
EDI [26] 4 4 8 18.792.03 0.670 0.144 17.45 2.23 0.603 0.149
Time Lens (ours) 4 4 4 34.811.63 0.959 0.012 33.21 2.00 0.942 0.023
Table 3: Benchmarking on the High Quality Frames (HQF) DA VIS240 dataset. We do not fine-tune our method and other
methods and use models provided by the authors. We evaluate methods on all sequences of the dataset. To compute SSIM
and PSNR, we downsample the original video by skip 1 and 3 frames, reconstruct these frames and compare them to the
skipped frames. In Uses frames andUses events columns we specify if a method uses frames and events for interpolation.
In the Color column, we indicate if a method works with color frames. In the table, we present two versions of our method:
Time Lens-syn , which we trained only on synthetic data, and Time Lens-real , which we trained on synthetic data and fine-
tuned on real event data from our own DA VIS346 camera. For SSIM and PSNR, we show mean and one standard deviation.
We show the best result in each column in bold and the second-best using underscore text.
Method Uses frames Uses events Color PSNR SSIM PSNR SSIM
1 frame skip 3 frames skips
DAIN [3] 4 8 4 29.826.91 0.875 0.124 26.10 7.52 0.782 0.185
SuperSloMo [10] 4 8 4 28.766.13 0.861 0.132 25.54 7.13 0.761 0.204
RRIN [13] 4 8 4 29.767.15 0.874 0.132 26.11 7.84 0.778 0.200
BMBC [29] 4 8 4 29.967.00 0.8750.126 26.327.78 0.7810.193
E2VID [32] 8 4 8 6.702.19 0.315 0.124 6.70 2.20 0.315 0.124
EDI [26] 4 4 8 18.76.53 0.574 0.244 18.8 6.88 0.579 0.274
Time Lens-syn (our) 4 4 4 30.575.01 0.903 0.067 28.98 5.09 0.873 0.086
Time Lens-real (ours) 4 4 4 32.494.60 0.927 0.048 30.57 5.08 0.900 0.069Figure 7: Qualitative results for the proposed method and its closes competitor DAIN [3] on our Dual Event and Color Camera
Dataset test sequences: “Fountain Schaffhauserplatz” (top-left), “Fountain Bellevue” (bottom-left) “Water bomb” (top-right)
and “Umbrella” (bottom-right). For each sequence, the figure shows interpolation results on the left (the animation can be
viewed in Acrobat Reader) and close-up interpolation results on the right. The close-ups, show input left and right frame and
intermediate interpolated frames.
Table 4: Benchmarking on the test set of the High Speed Event and RGB camera (HS-ERGB) dataset. We report PSNR and
SSIM for all sequences by skipping 5 and 7 frames respectively, and reconstructing the missing frames with each method. By
design LEDVDI [15] can interpolate only 5 frames. Uses frames andUses events indicate if a method uses frames or events
respectively. Color indicates whether a method works with color frames. For SSIM and PSNR the scores are averaged over
the sequences. Best results are shown in bold and the second best are underlined.
Method Uses frames Uses events Color PSNR SSIM PSNR SSIM
Far-away sequences 5 frame skip 7 frames skips
DAIN [3] 4 8 4 27.921.55 0.7800.141 27.131.75 0.7480.151
SuperSloMo [10] 4 8 4 25.666.24 0.727 0.221 24.16 5.20 0.692 0.199
RRIN [13] 4 8 4 25.265.81 0.738 0.196 23.73 4.74 0.703 0.170
BMBC [29] 4 8 4 25.626.13 0.742 0.202 24.13 4.99 0.710 0.175
LEDVDI [15] 4 4 8 12.501.74 0.393 0.174 n/a n/a
Time Lens (ours) 4 4 4 33.132.10 0.877 0.092 32.31 2.27 0.869 0.110
Close planar sequences 5 frame skip 7 frames skips
DAIN [3] 4 8 4 29.034.47 0.807 0.093 28.50 4.54 0:8010:096
SuperSloMo [10] 4 8 4 28.354.26 0.788 0.098 27.27 4.26 0:7750:099
RRIN [13] 4 8 4 28.694.17 0.813 0.083 27.46 4.24 0.800 0.084
BMBC [29] 4 8 4 29.224.45 0.8200.085 27.994.55 0.808 0.084
LEDVDI [15] 4 4 8 19.464.09 0.602 0.164 n/a n/a
Time Lens (ours) 4 4 4 32.194.19 0.839 0.090 31.68 4.18 0.835 0.091
an up to 5.21 dB improvement over state-of-the-art
frame-based and event-plus-frames-based methods on both
synthetic and real datasets. In addition, we release the
first High Speed Event and RGB (HS-ERGB) dataset,
which aims at pushing the limits of existing interpola-
tion approaches by establishing a new benchmark for both
event- and frame-based video frame interpolation methods.5. Acknowledgement
This work was supported by Huawei Zurich Research
Center; by the National Centre of Competence in Re-
search (NCCR) Robotics through the Swiss National Sci-
ence Foundation (SNSF); the European Research Coun-
cil (ERC) under the European Union’s Horizon 2020 re-
search and innovation programme (Grant agreement No.
864042).6. Video Demonstration
This PDF is accompanied with a video showing advan-
tages of the proposed method compared to state-of-the-art
frame-based methods published over recent months, as well
as potential practical applications of the method.
7. Backbone network architecture
Figure 8: Backbone hourglass network that we use in all
modules of the proposed method.
For all modules in the proposed method, we use the same
backbone architecture which is an hourglass network with
shortcut connections between the contracting and the ex-
panding parts similar to [10] which we show in Fig. 8.
8. Additional Ablation Experiments
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35
Percentage of locationsSynthesisWarped successiveWarp preceeding
Figure 9: Percentage of pixels each interpolation method
contributes on average to the final interpolation result for
Vimeo90k (denoising) validation set. Note, that all meth-
ods contribute almost equally to the final result and thus are
equally important.
Table 5: Importance of inter-frame events on Middlebury
test set. To compute SSIM and PSNR, we skip one frame
of the original video, reconstruct it and compare to the
skipped frame. One version of the proposed method has
access to the events synthesized from the skipped frame
and another version does not have inter-frame information.
We also show performance of frame-based SuperSloMo
method [10], that is used in event simulator for reference.
We highlight the best performing method.
Method PSNR SSIM
With inter-frame events (ours) 33.27 3.11 0.929 0.027
Without inter-frame events 29.03 4.85 0.866 0.111
SuperSloMo [10] 29.75 5.35 0.880 0.112
Importance of inter-frame events . To study the im-
portance of additional information provided by events, we
skip every second frame of the original video and attempt
to reconstruct it using two versions of the proposed method.One version has access to the events synthesized from the
skipped frame and another version does not have inter-
frame information. As we can see from the Tab. 5, the
former significantly outperforms the later by a margin of
4.24dB. Indeed this large improvements can be explained
by the fact that the method with inter-frame events has im-
plicit access to the ground truth image it tries to recon-
struct, albeit in the form of asynchronous events. This high-
lights that our network is able to efficiently decode the asyn-
chronous intermediate events to recover the missing frame.
Moreover, this shows that the addition of events has a sig-
nificant impact on the final task performance, proving the
usefulness of an event camera as an auxiliary sensor.
Importance of each interpolation method. To study
relative importance of synthesis-based andwarping-based
interpolation methods, we compute the percentage of pixels
that each method contribute on average to the final interpo-
lation result for the Vimeo90k (denoising) validation dataset
and show the result in Fig. 9. As it is clear from the figure,
all the methods contribute almost equally to the final result
and thus are all equally important.
1 2 3 4 5 6 7
Frame Index2224262830PSNR [dB]
BMBC
DAINRRIN
SuperSlowMoOurs
Figure 10: “Rope plot” showing interpolation quality as a
function of distance from input boundary frames on High
Quality Frames dataset. We skip all but every 7th frame and
restore them using events and remaining frames. For each
skip position, we compute average PSNR of the restored
frame over entire dataset. We do not fine-tune the proposed
and competing methods on the HQF dataset and simply use
pre-trained models provided by the authors. Note, that the
proposed method have the highest PSNR. Also, its PSNR
decreases much slower than PSNR of other methods we
move away from the input boundary frames.
“Rope” plot. To study how the interpolation quality de-
creases with the distance to the input frames, we skip all but
every 7th frame in the input videos from the High Quality
Frames dataset, restore them using our method and compare
to the original frames. For each skipped frame position, we
compute average PSNR of the restored frame over entire
dataset and show results in Fig. 10. As clear from the fig-ure, the proposed method has the highest PSNR. Also, its
PSNR decreases much slower than PSNR of the competing
methods as we move away from the boundary frames.
9. Additional Benchmarking Results
To makes sure that the fine-tuning does not af-
fect our general conclusions, we fine-tuned the pro-
posed method and RRIN method [13] on subset of
High Quality Frames dataset and test them on the
remaining part (“poster pillar 1”, “slow andfastdesk”,
“bike bayhdr” and “desk” sequences). We choose RRIN
method for this experiment, because it showed good perfor-
mance across synthetic and real datasets and it is fairly sim-
ple. As clear from the Tab. 6, after the fine-tuning, perfor-
mance of the proposed method remained very strong com-
pared to the RRIN method.
10. High Speed Events and RGB Dataset
In this section we describe the sequences in the High-
Speed Event and RGB (HS-ERGB) dataset. The commer-
cially available DA VIS 346 [4] already allows the simul-
taneous recording of events and grayscale frames, which
are temporally and spatially synchronized. However, it has
some shortcomings as the relatively low resolution of only
346260 pixels of both frames and events. This is far
below the resolution of typical frame based consumer cam-
eras. Additionally, the DA VIS 346 has a very limited dy-
namic range of 55 db and a maximum frame of 40 FPS.
Those properties render it not ideal for many event based
methods, which aim to outperform traditional frame based
cameras in certain applications. The setup described in [40]
shows improvements in the resolution of frames and dy-
namic range, but has a reduced event resolution instead. The
lack of publicly available high resolution event and color
frame datasets and of the shelf hardware motivated the de-
velopment of our dual camera setup. It features high reso-
lution, high frame rate, high dynamic range color frames
combined with high resolution events. A comparison of
our setup with the DA VIS346[4] and the setup with beam
splitter in [40] is shown in 7. With this new setup we col-
lect new High Speed Events and RGB (HS-ERGB) Dataset
that we summarize in Tab. 8. We show several fragments
from the dataset in Fig. 12. In the following paragraphs we
describe temporal synchronization and spatial alignment of
frame and event data that we performed for our dataset.
Synchronization In our setup, two cameras are hard-
ware synchronized through the use of external triggers.
Each time the standard camera starts and ends exposure, a
trigger is sent to the event camera which records an exter-
nal trigger event with precise timestamp information. This
information allows us to assign accurate timestamps to the
standard frames, as well as group events during exposure orbetween consecutive frames.
Alignment In our setup event and RGB cameras are ar-
ranged in stereo configuration, therefore event and frame
data in addition to temporal, require spatial alignment. We
perform the alignment in three steps: (i)stereo calibration,
(ii)rectification and (iii) feature-based global alignment.
We first calibrate the cameras using a standard checker-
board pattern. The recorded asynchronous events are con-
verted to temporally aligned video reconstructions using
E2VID[32, 33]. Finally, we find the intrinsic and extrin-
sics by applying the stereo calibration tool Kalibr[24] to the
video reconstructions and the standard frames recorded by
the color camera. We then use the found intrinsics and ex-
trinsics to rectify the events and frames.
Due to the small baseline and similar fields of view
(FoV), stereo rectification is usually sufficient to align the
output of both sensors for scenes with a large average depth
(>40 m ). This is illustrated in Fig. 11 (a).
For close scenes, however, events and frames are mis-
aligned (Fig. 11 (b)). For this reason we perform the sec-
ond step of global alignment using a homography which
we estimate by matching SIFT features [17] extracted on
the standard frames and video reconstructions. The homog-
raphy estimation also utilizes RANSAC to eliminate false
matches. When the cameras are static, and the objects of
interest move within a plane, this yields accurate alignment
between the two sensors (Fig.11 (c)).
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Sequence Name Subset Camera Settings Description
Close planar sequences
Water bomb air (Fig. 12a)
Train163 FPS, 1080 µsexposure, 1065 frames accelerating object, water splash
Lighting match 150 FPS, 2972 µsexposure, 666 frames illumination change, fire
Fountain Schaffhauserplatz 1 150 FPS, 977µsexposure, 1038 frames illumination change, fire
Water bomb ETH 2 (Fig. 12c) 163 FPS, 323µsexposure, 3494 frames accelerating object, water splash
Waving arms 163 FPS, 3476 µsexposure, 762 frames non-linear motion
Popping air balloon
Test150 FPS, 2972 µsexposure, 335 frames non-linear motion, object disappearance
Confetti (Fig. 12e 150 FPS, 2972 µsexposure, 832 frames non-linear motion, periodic motion
Spinning plate 150 FPS, 2971 µsexposure, 1789 frames non-linear motion, periodic motion
Spinning umbrella 163 FPS, 3479 µsexposure, 763 frames non-linear motion
Water bomb floor 1 (Fig. 12d) 160 FPS, 628µsexposure, 686 frames accelerating object, water splash
Fountain Schaffhauserplatz 2 150 FPS, 977µsexposure, 1205 frames non-linear motion, water
Fountain Bellevue 2 (Fig. 12b) 160 FPS, 480µsexposure, 1329 frames non-linear motion, water, periodic movement
Water bomb ETH 1 163 FPS, 323µsexposure, 3700 frames accelerating object, water splash
Candle (Fig. 12f) 160 FPS, 478µsexposure, 804 frames illumination change, non-linear motion
Far-away sequences
Kornhausbruecke letten x 1
Train163 FPS, 266µsexposure, 831 frames fast camera rotation around z-axis
Kornhausbruecke rot x 5 163 FPS, 266µsexposure, 834 frames fast camera rotation around x-axis
Kornhausbruecke rot x 6 163 FPS, 266µsexposure, 834 frames fast camera rotation around x-axis
Kornhausbruecke rot y 3 163 FPS, 266µsexposure, 833 frames fast camera rotation around y-axis
Kornhausbruecke rot y 4 163 FPS, 266µsexposure, 833 frames fast camera rotation around y-axis
Kornhausbruecke rot z 1 163 FPS, 266µsexposure, 857 frames fast camera rotation around z-axis
Kornhausbruecke rot z 2 163 FPS, 266µsexposure, 833 frames fast camera rotation around z-axis
Sihl 4 163 FPS, 426µsexposure, 833 frames fast camera rotation around z-axis
Tree 3 163 FPS, 978µsexposure, 832 frames camera rotation around z-axis
Lake 4 163 FPS, 334µsexposure, 833 frames camera rotation around z-axis
Lake 5 163 FPS, 275µsexposure, 833 frames camera rotation around z-axis
Lake 7 163 FPS, 274µsexposure, 833 frames camera rotation around z-axis
Lake 8 163 FPS, 274µsexposure, 832 frames camera rotation around z-axis
Lake 9 163 FPS, 274µsexposure, 832 frames camera rotation around z-axis
Bridge lake 4 163 FPS, 236µsexposure, 836 frames camera rotation around z-axis
Bridge lake 5 163 FPS, 236µsexposure, 834 frames camera rotation around z-axis
Bridge lake 6 163 FPS, 235µsexposure, 832 frames camera rotation around z-axis
Bridge lake 7 163 FPS, 235µsexposure, 832 frames camera rotation around z-axis
Bridge lake 8 163 FPS, 235µsexposure, 834 frames camera rotation around z-axis
Kornhausbruecke letten random 4
Test163 FPS, 266µsexposure, 834 frames random camera movement
Sihl 03 163 FPS, 426µsexposure, 834 frames camera rotation around z-axis
Lake 01 163 FPS, 335µsexposure, 784 frames camera rotation around z-axis
Lake 03 163 FPS, 334µsexposure, 833 frames camera rotation around z-axis
Bridge lake 1 163 FPS, 237µsexposure, 833 frames camera rotation around z-axis
Bridge lake 3 163 FPS, 236µsexposure, 834 frames camera rotation around z-axis(a) Water bomb air (b) Fountain Bellevue
(c) Water bomb ETH 2 (d) Water bomb floor 1
(e) Confetti (f) Candle
Figure 12: Example sequences of the HS-ERGB dataset. This figure contains animation that can be viewed in Acrobat
Reader.