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Running
on
Zero
Running
on
Zero
| import torch | |
| import numpy as np | |
| import os | |
| from .mesh.io import save_obj, to_mesh | |
| from .mesh.smpl2mesh import SMPL2Mesh | |
| from .skeleton import SkeletonAMASS, convert2humanml | |
| from .skeleton2smpl.skeleton2smpl import Skeleton2Obj | |
| import json | |
| def save_mesh(vertices, faces, npy_file): | |
| def npy_path_to_obj_path(npy_path: str) -> str: | |
| return os.path.join(os.path.dirname(npy_path) , f"{npy_path}_obj") | |
| results_dir = npy_path_to_obj_path(npy_file) | |
| os.makedirs(results_dir, exist_ok=True) | |
| for frame_i in range(vertices.shape[-1]): | |
| file_path = os.path.join(results_dir, f"frame{frame_i:03d}.obj") | |
| mesh = to_mesh(vertices[..., frame_i], faces) | |
| save_obj(mesh, file_path) | |
| print(f"Saved obj files to [{results_dir}]") | |
| def main(): | |
| num_smplify_iters = 20 # This is what requires most time. It can be decreased or increasd depending on the output quality we want (or how quick we canr each it) | |
| device = "cuda" | |
| # get observation smpl params | |
| json_file_path = "./smpl_params.json" | |
| with open(json_file_path, "r") as json_file: | |
| loaded_data = json.load(json_file) | |
| person_idx = 0 | |
| smpl_dict_last_obs = loaded_data[-1] | |
| smpl_dict_last_obs = {k: torch.from_numpy(np.array(v)).float().to(device) for k,v in smpl_dict_last_obs.items()} | |
| input_kpts = smpl_dict_last_obs['joints3d'] | |
| input_kpts = torch.stack([input_kpts[..., 0], input_kpts[..., 2], -input_kpts[..., 1]], dim=-1) | |
| input_kpts = input_kpts/1000 | |
| input_kpts = input_kpts - input_kpts[..., 0:1, :] | |
| # get predictions | |
| pred_motions = torch.from_numpy(np.load( "./joints3d.npy", allow_pickle=True)).to(device) | |
| pred_motions = torch.stack([pred_motions[..., 0], pred_motions[..., 2], -pred_motions[..., 1]], dim=-1) | |
| # remove bacth dimension, add a zero hip joint | |
| pred_motions = pred_motions.squeeze(0) | |
| pred_motions = torch.cat([torch.zeros(*pred_motions.shape[:2], 1, 3).to(device), pred_motions], dim=-2) | |
| # select just some of the motions | |
| # TO DO use the previous code with the limb length variance error to choose the sample | |
| # Or pick the most diverse | |
| pred_motions = pred_motions[:1] | |
| pred_motions = pred_motions.view(-1, 22, 3) | |
| skeleton = SkeletonAMASS | |
| pred_motions = convert2humanml(pred_motions, skeleton.LANDMARKS, skeleton.TO_HUMANML_NAMES) | |
| init_params = {} | |
| init_params["betas"] = smpl_dict_last_obs["betas"][person_idx].unsqueeze(0).expand(pred_motions.shape[0], -1) | |
| init_params["pose"] = smpl_dict_last_obs["body_pose"][person_idx].view(-1, 3) | |
| assert init_params["pose"].shape[0] == 24, "the body pose should have 24 joints, it is the output of NLF" | |
| init_params["pose"] = init_params["pose"].unsqueeze(0).expand(pred_motions.shape[0], -1, -1).view(pred_motions.shape[0], -1).to(device) | |
| init_params["cam"] = smpl_dict_last_obs["transl"][person_idx].unsqueeze(0).unsqueeze(-2).expand(pred_motions.shape[0], -1, -1).to(device) | |
| skeleton2obj = Skeleton2Obj( | |
| device=device, num_smplify_iters=num_smplify_iters, | |
| smpl_model_dir="./body_models/", #path to smpl body models | |
| gmm_model_dir="./joint2smpl_models/", #path to gmm model | |
| ) | |
| rot_motions, smpl_dict = skeleton2obj.convert_motion_2smpl(pred_motions, hmp=True, init_params=init_params, fix_betas=True) | |
| smpl2mesh = SMPL2Mesh(device) | |
| vertices, faces = smpl2mesh.convert_smpl_to_mesh(rot_motions, pred_motions) | |
| pred_files = [('./hanyu')] | |
| vertices = vertices.reshape(*vertices.shape[:2], len(pred_files), -1) | |
| for v, npy_file in zip(np.moveaxis(vertices, 2, 0), pred_files): | |
| save_mesh(v, faces, npy_file) | |
| if __name__ == "__main__": | |
| main() | |