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| # This code is based on https://github.com/Mathux/ACTOR.git | |
| import torch | |
| import utils.rotation_conversions as geometry | |
| from model.smpl import SMPL, JOINTSTYPE_ROOT | |
| # from .get_model import JOINTSTYPES | |
| JOINTSTYPES = ["a2m", "a2mpl", "smpl", "vibe", "vertices"] | |
| class Rotation2xyz: | |
| def __init__(self, device, dataset='amass'): | |
| self.device = device | |
| self.dataset = dataset | |
| self.smpl_model = SMPL().eval().to(device) | |
| def __call__(self, x, mask, pose_rep, translation, glob, | |
| jointstype, vertstrans, betas=None, beta=0, | |
| glob_rot=None, get_rotations_back=False, **kwargs): | |
| if pose_rep == "xyz": | |
| return x | |
| if mask is None: | |
| mask = torch.ones((x.shape[0], x.shape[-1]), dtype=bool, device=x.device) | |
| if not glob and glob_rot is None: | |
| raise TypeError("You must specify global rotation if glob is False") | |
| if jointstype not in JOINTSTYPES: | |
| raise NotImplementedError("This jointstype is not implemented.") | |
| if translation: | |
| x_translations = x[:, -1, :3] | |
| x_rotations = x[:, :-1] | |
| else: | |
| x_rotations = x | |
| x_rotations = x_rotations.permute(0, 3, 1, 2) | |
| nsamples, time, njoints, feats = x_rotations.shape | |
| # Compute rotations (convert only masked sequences output) | |
| if pose_rep == "rotvec": | |
| rotations = geometry.axis_angle_to_matrix(x_rotations[mask]) | |
| elif pose_rep == "rotmat": | |
| rotations = x_rotations[mask].view(-1, njoints, 3, 3) | |
| elif pose_rep == "rotquat": | |
| rotations = geometry.quaternion_to_matrix(x_rotations[mask]) | |
| elif pose_rep == "rot6d": | |
| rotations = geometry.rotation_6d_to_matrix(x_rotations[mask]) | |
| else: | |
| raise NotImplementedError("No geometry for this one.") | |
| if not glob: | |
| global_orient = torch.tensor(glob_rot, device=x.device) | |
| global_orient = geometry.axis_angle_to_matrix(global_orient).view(1, 1, 3, 3) | |
| global_orient = global_orient.repeat(len(rotations), 1, 1, 1) | |
| else: | |
| global_orient = rotations[:, 0] | |
| rotations = rotations[:, 1:] | |
| if betas is None: | |
| betas = torch.zeros([rotations.shape[0], self.smpl_model.num_betas], | |
| dtype=rotations.dtype, device=rotations.device) | |
| betas[:, 1] = beta | |
| # import ipdb; ipdb.set_trace() | |
| out = self.smpl_model(body_pose=rotations, global_orient=global_orient, betas=betas) | |
| # get the desirable joints | |
| joints = out[jointstype] | |
| x_xyz = torch.empty(nsamples, time, joints.shape[1], 3, device=x.device, dtype=x.dtype) | |
| x_xyz[~mask] = 0 | |
| x_xyz[mask] = joints | |
| x_xyz = x_xyz.permute(0, 2, 3, 1).contiguous() | |
| # the first translation root at the origin on the prediction | |
| if jointstype != "vertices": | |
| rootindex = JOINTSTYPE_ROOT[jointstype] | |
| x_xyz = x_xyz - x_xyz[:, [rootindex], :, :] | |
| if translation and vertstrans: | |
| # the first translation root at the origin | |
| x_translations = x_translations - x_translations[:, :, [0]] | |
| # add the translation to all the joints | |
| x_xyz = x_xyz + x_translations[:, None, :, :] | |
| if get_rotations_back: | |
| return x_xyz, rotations, global_orient | |
| else: | |
| return x_xyz | |