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# This code is based on https://github.com/Mathux/ACTOR.git
import torch
import utils.rotation_conversions as geometry
from models.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
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