Update README.md

README.md

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 ---
 license: apache-2.0
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+This repository contains a pruned and partially reorganized version of [CHAMP](https://fudan-generative-vision.github.io/champ/#/).
+
+```
+@misc{zhu2024champ,
+      title={Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance}, 
+      author={Shenhao Zhu and Junming Leo Chen and Zuozhuo Dai and Yinghui Xu and Xun Cao and Yao Yao and Hao Zhu and Siyu Zhu},
+      year={2024},
+      eprint={2403.14781},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/64429aaf7feb866811b12f73/wZku1I_4L4VwWeXXKgXqb.mp4"></video>
+
+Video credit: [Polina Tankilevitch, Pexels](https://www.pexels.com/video/a-young-woman-dancing-hip-hop-3873100/)
+
+Image credit: [Andrea Piacquadio, Pexels](https://www.pexels.com/photo/man-in-black-jacket-wearing-black-headphones-3831645/)
+
+# Usage
+
+First, install the CHAMP package into your python environment. If you're creating a new environment for CHAMP, be sure you also specify the version of torch you want with CUDA support, or else this will try to run only on CPU.
+
+```sh
+pip install git+https://github.com/painebenjamin/champ.git
+```
+
+Now, you can create the pipeline, automatically pulling the weights from this repository, either as individual models:
+
+```py
+from champ import CHAMPPipeline
+pipeline = CHAMPPipeline.from_pretrained(                                                                                                                                               
+    "benjamin-paine/champ",                                                                                                                                                              
+    torch_dtype=torch.float16,                                                                                                                                                           
+    variant="fp16",                                                                                                                                                                      
+    device="cuda",                                                                                                                                                                       
+).to("cuda", dtype=torch.float16)   
+```
+
+Or, as a single file:
+
+```py
+pipeline = CHAMPPipeline.from_pretrained(                                                                                                                                               
+    "benjamin-paine/champ",                                                                                                                                                              
+    torch_dtype=torch.float16,                                                                                                                                                           
+    variant="fp16",                                                                                                                                                                      
+    device="cuda",                                                                                                                                                                       
+).to("cuda", dtype=torch.float16)   
+```
+
+Follow this format for execution:
+
+```
+result = pipeline(
+  reference: PIL.Image.Image,
+  guidance: Dict[str, List[PIL.Image.Image]],
+  width: int,
+  height: int,
+  video_length: int,
+  num_inference_steps: int,
+  guidance_scale: float
+).videos
+# Result is a list of PIL Images
+```
+
+Starting values for `num_inference_steps` and `guidance_scale` are `20` and `3.5`, respectively.
+
+Guidance keys include `depth`, `normal`, `dwpose` and `semantic_map` (densepose.) This guide does not provide details on how to obtain those samples, but examples are available in [the git repository.](https://github.com/painebenjamin/champ/tree/master/example)