diff --git a/README.md b/README.md index b2de1e3..94db1f3 100644 --- a/README.md +++ b/README.md @@ -24,7 +24,7 @@
PyTorch Lightning -

+ Google Colab

Paper PDF @@ -56,9 +56,6 @@
  • Who needs ICON
    • -
  • - TODO -
  • Installation
    • @@ -99,10 +96,16 @@ ## Who needs ICON? -- Given an RGB image, you could get: - - image (png): segmentation, normal images (body + cloth), overlap result (rgb + normal) - - mesh (obj): SMPL-(X) body, reconstructed clothed human - - video (mp4): self-rotated clothed human +- Given a raw RGB image, you could get: + - image (png): + - segmented human RGB + - normal maps of body and cloth + - pixel-aligned normal-RGB overlap + - mesh (obj): + - SMPL-(X) body from *PyMAF, PIXIE, PARE, HybrIK, BEV* + - 3D clothed human reconstruction + - video (mp4): + - self-rotated clothed human | ![Intermediate Results](assets/intermediate_results.png) | | :------------------------------------------------------------------------: | @@ -121,39 +124,24 @@ | :----------------------------------------------------------------------------------------: | | _3D Clothed Avatar, created from 400+ images using **ICON+SCANimate**, animated by AIST++_ | -## TODO - -- [x] testing code and pretrained models (\*self-implemented version) - - [x] ICON (w/ & w/o global encoder, w/ PyMAF/HybrIK/BEV/PIXIE/PARE as HPS) - - [x] PIFu\* (RGB image + predicted normal map as input) - - [x] PaMIR\* (RGB image + predicted normal map as input, w/ PyMAF/HybrIK/BEV/PIXIE/PARE as HPS) -- [x] colab notebook - Google Colab - -- [x] dataset processing - - [x] RGB/Normal Rendering - - [x] Visibility computing -- [ ] training code - - [x] ICON (w/ & w/o global encoder, w/ SMPL-X) - - [x] PIFu\* (RGB image + predicted normal map as input) - - [ ] PaMIR\* (RGB image + predicted normal map as input, w/ voxelized SMPL) -- [x] evaluation code +- If you want to **Train & Evaluate** on **PIFu/PaMIR/ICON** using your own data, please check [dataset.md](./docs/dataset.md) to prepare dataset, [training.md](./docs/training.md) for training, and [evaluation.md](./docs/evaluation.md) for benchmark evaluation. +

    ## Installation Please follow the [Installation Instruction](docs/installation.md) to setup all the required packages, extra data, and models. ## Dataset -Please follow the [Dataset Instruction](docs/dataset.md) to generate the train/val/test dataset from raw scans (THuman2.0). +Please follow the [Dataset Instruction](docs/dataset.md) to generate the train/val/test dataset from THuman2.0 ## Training -Please follow the [Training Instruction](docs/training.md) to train your own model using THuman2.0. +Please follow the [Training Instruction](docs/training.md) to train your own model using THuman2.0 ## Evaluation -Please follow the [Evaluation Instruction](docs/evaluation.md) to benchmark models on THuman2.0. +Please follow the [Evaluation Instruction](docs/evaluation.md) to benchmark trained models on THuman2.0 ## Add-on 1. [Garment Extraction from Fashion Images](docs/garment-extraction.md), supported by ETH Zürich students as 3DV course project. @@ -221,6 +209,12 @@ Some images used in the qualitative examples come from [pinterest.com](https://w This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.860768 ([CLIPE Project](https://www.clipe-itn.eu)). +
    + +-------------- + +
    + ## License This code and model are available for non-commercial scientific research purposes as defined in the [LICENSE](LICENSE) file. By downloading and using the code and model you agree to the terms in the [LICENSE](LICENSE). diff --git a/apps/ICON.py b/apps/ICON.py index e79b90c..ff8e94b 100644 --- a/apps/ICON.py +++ b/apps/ICON.py @@ -536,13 +536,10 @@ def test_step(self, batch, batch_idx): # export paths mesh_name = batch["subject"][0] mesh_rot = batch["rotation"][0].item() - ckpt_dir = self.cfg.resume_path.split("/")[-2] + ckpt_dir = self.cfg.name - if (self.cfg.dataset.online_smpl) or (self.cfg.optim_body): - for kid, key in enumerate(self.cfg.dataset.noise_type): - ckpt_dir += f"_{key}_{self.cfg.dataset.noise_scale[kid]}" - else: - ckpt_dir += "_perfect_smpl" + for kid, key in enumerate(self.cfg.dataset.noise_type): + ckpt_dir += f"_{key}_{self.cfg.dataset.noise_scale[kid]}" if self.cfg.optim_cloth: ckpt_dir += "_optim_cloth" diff --git a/configs/train/pamir.yaml b/configs/train/pamir.yaml index 990ecf1..c743761 100644 --- a/configs/train/pamir.yaml +++ b/configs/train/pamir.yaml @@ -21,7 +21,7 @@ dataset: zray_type: False semantic_p: False remove_outlier: False - + noise_type: ['pose', 'beta'] noise_scale: [0.05, 0.5] diff --git a/docs/dataset.md b/docs/dataset.md index 6e30c39..264b964 100644 --- a/docs/dataset.md +++ b/docs/dataset.md @@ -11,7 +11,15 @@ Please refer to [THuman2.0-Dataset](https://github.com/ytrock/THuman2.0-Dataset) to download the original scans into `data/thuman2/scans`, and its SMPL-X(male) fits into `data/thuman2/fits`. Then generate `all.txt` by `ls > ../all.txt` under `data/thuman2/scans`, which contains all the subject names (0000~0525). -:eyes: `./sample_data` contains one example of THuman2.0 and shows the data folder structure. +PaMIR only support SMPL instead of SMPL-X, please download THuman2.0's SMPL fits (.obj, .pkl) and put them at `./data/thuman2.0/smpl` + +```bash +wget https://download.is.tue.mpg.de/icon/smpl.zip --no-check-certificate -O ./data/thuman2/smpl.zip +unzip ./data/thuman2/smpl.zip -d ./data/thuman2/ +rm ./data/thuman2/smpl.zip +``` + +:eyes: `./sample_data` contains one example of THuman2.0 which shows the data folder structure. ## Debug Mode diff --git a/docs/evaluation.md b/docs/evaluation.md index 7dc5da8..5b08e61 100644 --- a/docs/evaluation.md +++ b/docs/evaluation.md @@ -4,8 +4,7 @@ Make sure you have already generated all the required synthetic data (refer to [Dataset Instruction](dataset.md)) under `./data/thuman2_{num_views}views`, which includes the rendered RGB (`render/`), normal images(`normal_B/`, `normal_F/`, `T_normal_B/`, `T_normal_F/`), corresponding calibration matrix (`calib/`) and pre-computed visibility arrays (`vis/`). - -:warning: Don't support headless mode currently, `unset PYOPENGL_PLATFORM` before training. (will fix it later...) +:warning: Don't support headless mode currently, `unset PYOPENGL_PLATFORM` before training. ## Command @@ -18,21 +17,11 @@ CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/icon-filter.yam # ICON w/o filter CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/icon-nofilter.yaml -test -# PIFu +# PIFu* (*: re-implementation) CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/pifu.yaml -test -# PaMIR +# PaMIR* (*: re-implementation) CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/pamir.yaml -test ``` -## Intermediate Results - -All the intermediate results are exported at `./results/ckpt_perfect_smpl` - -## Benchmark on THuman2.0 (testset: 0500-0504, 3 views) - -|Metrics|ICON w/ filter|ICON w/o filter|PIFu|PaMIR| -|---|---|---|---|---| -|Chamfer|1.068|1.218|1.711|-| -|P2S|1.068|1.210|1.159|-| -|NC|0.061|0.073|0.075|-| \ No newline at end of file +The qualitative results are located at `./results/` \ No newline at end of file diff --git a/docs/training.md b/docs/training.md index 6a6d5fd..17bfb49 100644 --- a/docs/training.md +++ b/docs/training.md @@ -5,15 +5,21 @@ Make sure you have already generated all the required synthetic data (refer to [Dataset Instruction](dataset.md)) under `./data/thuman2_{num_views}views`, which includes the rendered RGB (`render/`), normal images(`normal_B/`, `normal_F/`, `T_normal_B/`, `T_normal_F/`), corresponding calibration matrix (`calib/`) and pre-computed visibility arrays (`vis/`). :eyes: Test your dataloader with [vedo](https://vedo.embl.es/) + ```bash -python -m lib.dataloader_demo -v + +# visualization for SMPL-X mesh +python -m lib.dataloader_demo -v -c ./configs/train/icon-filter.yaml + +# visualization for voxelized SMPL +python -m lib.dataloader_demo -v -c ./configs/train/pamir.yaml ```

    -:warning: Don't support headless mode currently, `unset PYOPENGL_PLATFORM` before training. (will fix it later...) +:warning: Don't support headless mode currently, `unset PYOPENGL_PLATFORM` before training. ## Command ```bash @@ -25,12 +31,15 @@ CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/icon-filter.yam # ICON w/o filter (name: icon-nofilter) CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/icon-nofilter.yaml -# ICON-MVP (name: icon-mvp), mvp = minimal viable product, simple (used features) yet efficient (GPU) +# ICON-MVP (name: icon-mvp), mvp = minimal viable product, simple (used features) yet efficient (GPU usage) # https://en.wikipedia.org/wiki/Minimum_viable_product CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/icon-mvp.yaml # PIFu (name: pifu) CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/pifu.yaml + +# PaMIR (name: pamir) +CUDA_VISIBLE_DEVICES=0 python -m apps.train -cfg ./configs/train/pamir.yaml ``` ## Tensorboard diff --git a/lib/dataloader_demo.py b/lib/dataloader_demo.py index 68f70bd..d985682 100644 --- a/lib/dataloader_demo.py +++ b/lib/dataloader_demo.py @@ -2,52 +2,57 @@ from lib.common.config import get_cfg_defaults from lib.dataset.PIFuDataset import PIFuDataset -args = get_cfg_defaults() -args.merge_from_file("./configs/train/icon-filter.yaml") +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + parser.add_argument('-v', + '--show', + action='store_true', + help='vis sampler 3D') + parser.add_argument('-s', + '--speed', + action='store_true', + help='vis sampler 3D') + parser.add_argument('-l', + '--list', + action='store_true', + help='vis sampler 3D') + parser.add_argument('-c', + '--config', + default='./configs/train/icon-filter.yaml', + help='vis sampler 3D') + args_c = parser.parse_args() -# loading cfg file -parser = argparse.ArgumentParser() -parser.add_argument('-v', - '--show', - action='store_true', - help='vis sampler 3D') -parser.add_argument('-s', - '--speed', - action='store_true', - help='vis sampler 3D') -parser.add_argument('-l', - '--list', - action='store_true', - help='vis sampler 3D') -args_c = parser.parse_args() + args = get_cfg_defaults() + args.merge_from_file(args_c.config) -dataset = PIFuDataset(args, split='train', vis=args_c.show) -print(f"Number of subjects :{len(dataset.subject_list)}") -data_dict = dataset[0] + dataset = PIFuDataset(args, split='train', vis=args_c.show) + print(f"Number of subjects :{len(dataset.subject_list)}") + data_dict = dataset[0] -if args_c.list: - for k in data_dict.keys(): - if not hasattr(data_dict[k], "shape"): - print(f"{k}: {data_dict[k]}") - else: - print(f"{k}: {data_dict[k].shape}") + if args_c.list: + for k in data_dict.keys(): + if not hasattr(data_dict[k], "shape"): + print(f"{k}: {data_dict[k]}") + else: + print(f"{k}: {data_dict[k].shape}") -if args_c.show: - # for item in dataset: - item = dataset[0] - dataset.visualize_sampling3D(item, mode='occ') + if args_c.show: + # for item in dataset: + item = dataset[0] + dataset.visualize_sampling3D(item, mode='occ') -if args_c.speed: - # original: 2 it/s - # smpl online compute: 2 it/s - # normal online compute: 1.5 it/s - from tqdm import tqdm - for item in tqdm(dataset): - # pass - for k in item.keys(): - if 'voxel' in k: - if not hasattr(item[k], "shape"): - print(f"{k}: {item[k]}") - else: - print(f"{k}: {item[k].shape}") - print("--------------------") + if args_c.speed: + # original: 2 it/s + # smpl online compute: 2 it/s + # normal online compute: 1.5 it/s + from tqdm import tqdm + for item in tqdm(dataset): + # pass + for k in item.keys(): + if 'voxel' in k: + if not hasattr(item[k], "shape"): + print(f"{k}: {item[k]}") + else: + print(f"{k}: {item[k].shape}") + print("--------------------") diff --git a/lib/dataset/PIFuDataset.py b/lib/dataset/PIFuDataset.py index d54cc2f..b048401 100644 --- a/lib/dataset/PIFuDataset.py +++ b/lib/dataset/PIFuDataset.py @@ -1,7 +1,9 @@ from lib.renderer.mesh import load_fit_body from lib.dataset.hoppeMesh import HoppeMesh +from lib.dataset.body_model import TetraSMPLModel from lib.common.render import Render from lib.dataset.mesh_util import SMPLX, projection, cal_sdf_batch, get_visibility +from lib.pare.pare.utils.geometry import rotation_matrix_to_angle_axis from termcolor import colored import os.path as osp import numpy as np @@ -10,9 +12,9 @@ import trimesh import torch import vedo -from ipdb import set_trace from kaolin.ops.mesh import check_sign import torchvision.transforms as transforms +from ipdb import set_trace class PIFuDataset(): @@ -81,10 +83,12 @@ def __init__(self, cfg, split='train', vis=False): if dataset in ['thuman2']: mesh_dir = osp.join(dataset_dir, "scans") smplx_dir = osp.join(dataset_dir, "fits") + smpl_dir = osp.join(dataset_dir, "smpl") self.datasets_dict[dataset] = { "subjects": np.loadtxt(osp.join(dataset_dir, "all.txt"), dtype=str), "smplx_dir": smplx_dir, + "smpl_dir": smpl_dir, "mesh_dir": mesh_dir, "scale": self.scales[dataset_id] } @@ -149,7 +153,7 @@ def get_subject_list(self, split): print(colored(f"total: {len(subject_list)}", "yellow")) random.shuffle(subject_list) - # subject_list = ["thuman2/0499"] + # subject_list = ["thuman2/0008"] return subject_list def __len__(self): @@ -178,6 +182,7 @@ def __getitem__(self, index): 'scale': self.datasets_dict[dataset]["scale"], 'mesh_path': osp.join(self.datasets_dict[dataset]["mesh_dir"], f"{subject}/{subject}.obj"), 'smplx_path': osp.join(self.datasets_dict[dataset]["smplx_dir"], f"{subject}/smplx_param.pkl"), + 'smpl_path': osp.join(self.datasets_dict[dataset]["smpl_dir"], f"{subject}.pkl"), 'calib_path': osp.join(self.root, render_folder, 'calib', f'{rotation:03d}.txt'), 'vis_path': osp.join(self.root, render_folder, 'vis', f'{rotation:03d}.pt'), 'image_path': osp.join(self.root, render_folder, 'render', f'{rotation:03d}.png') @@ -205,12 +210,16 @@ def __getitem__(self, index): data_dict, is_valid=self.split == "val", is_sdf=self.use_sdf)) data_dict.update(self.load_smpl(data_dict, self.vis)) - if (not self.vis) and (self.split != 'test'): - + if self.prior_type == 'pamir': + data_dict.update(self.load_smpl_voxel(data_dict)) + + if (self.split != 'test') and (not self.vis): + del data_dict['verts'] del data_dict['faces'] - - del data_dict['mesh'] + + if not self.vis: + del data_dict['mesh'] path_keys = [ key for key in data_dict.keys() if '_path' in key or '_dir' in key @@ -267,23 +276,30 @@ def add_noise(self, smpl_betas, noise_type, noise_scale, + type, hashcode): np.random.seed(hashcode) - if 'beta' in noise_type: - if beta_num != 11: - smpl_betas += (np.random.rand(beta_num) - - 0.5) * 2.0 * noise_scale[noise_type.index("beta")] + if type == 'smplx': + noise_idx = self.noise_smplx_idx + else: + noise_idx = self.noise_smpl_idx + + if 'beta' in noise_type and noise_scale[noise_type.index("beta")] > 0.0: + smpl_betas += (np.random.rand(beta_num) - + 0.5) * 2.0 * noise_scale[noise_type.index("beta")] smpl_betas = smpl_betas.astype(np.float32) - if 'pose' in noise_type: - smpl_pose[self.noise_smplx_idx] += ( - np.random.rand(len(self.noise_smplx_idx)) - + if 'pose' in noise_type and noise_scale[noise_type.index("pose")] > 0.0: + smpl_pose[noise_idx] += ( + np.random.rand(len(noise_idx)) - 0.5) * 2.0 * np.pi * noise_scale[noise_type.index("pose")] smpl_pose = smpl_pose.astype(np.float32) - - return torch.as_tensor(smpl_pose[None, ...]), torch.as_tensor(smpl_betas[None, ...]) + if type == 'smplx': + return torch.as_tensor(smpl_pose[None, ...]), torch.as_tensor(smpl_betas[None, ...]) + else: + return smpl_pose, smpl_betas def compute_smpl_verts(self, data_dict, noise_type=None, noise_scale=None): @@ -293,13 +309,13 @@ def compute_smpl_verts(self, data_dict, noise_type=None, noise_scale=None): smplx_param = np.load(data_dict['smplx_path'], allow_pickle=True) smplx_pose = smplx_param["body_pose"] # [1,63] smplx_betas = smplx_param["betas"] # [1,10] - smplx_pose, smplx_betas = self.add_noise( smplx_betas.shape[1], smplx_pose[0], smplx_betas[0], noise_type, noise_scale, + type='smplx', hashcode=(hash(f"{data_dict['subject']}_{data_dict['rotation']}")) % (10**8)) smplx_out, _ = load_fit_body(fitted_path=data_dict['smplx_path'], @@ -315,6 +331,56 @@ def compute_smpl_verts(self, data_dict, noise_type=None, noise_scale=None): return smplx_out.vertices, smplx_dict + def compute_voxel_verts(self, + data_dict, + noise_type=None, + noise_scale=None): + + smpl_param = np.load(data_dict['smpl_path'], allow_pickle=True) + smplx_param = np.load(data_dict['smplx_path'], allow_pickle=True) + + smpl_pose = rotation_matrix_to_angle_axis( + torch.as_tensor(smpl_param['full_pose'][0])).numpy() + smpl_betas = smpl_param["betas"] + + smpl_path = osp.join(self.smplx.model_dir, "smpl/SMPL_MALE.pkl") + tetra_path = osp.join(self.smplx.tedra_dir, + "tetra_male_adult_smpl.npz") + + smpl_model = TetraSMPLModel(smpl_path, tetra_path, 'adult') + + smpl_pose, smpl_betas = self.add_noise( + smpl_model.beta_shape[0], + smpl_pose.flatten(), + smpl_betas[0], + noise_type, + noise_scale, + type='smpl', + hashcode=(hash(f"{data_dict['subject']}_{data_dict['rotation']}")) % (10**8)) + + smpl_model.set_params(pose=smpl_pose.reshape(-1, 3), + beta=smpl_betas, + trans=smpl_param["transl"]) + + verts = (np.concatenate([smpl_model.verts, smpl_model.verts_added], + axis=0) * smplx_param["scale"] + smplx_param["translation"] + ) * self.datasets_dict[data_dict['dataset']]['scale'] + faces = np.loadtxt(osp.join(self.smplx.tedra_dir, "tetrahedrons_male_adult.txt"), + dtype=np.int32) - 1 + + pad_v_num = int(8000 - verts.shape[0]) + pad_f_num = int(25100 - faces.shape[0]) + + verts = np.pad(verts, ((0, pad_v_num), (0, 0)), + mode='constant', + constant_values=0.0).astype(np.float32) + faces = np.pad(faces, ((0, pad_f_num), (0, 0)), + mode='constant', + constant_values=0.0).astype(np.int32) + + + return verts, faces, pad_v_num, pad_f_num + def load_smpl(self, data_dict, vis=False): smplx_verts, smplx_dict = self.compute_smpl_verts( @@ -380,6 +446,22 @@ def load_smpl(self, data_dict, vis=False): return return_dict + def load_smpl_voxel(self, data_dict): + + smpl_verts, smpl_faces, pad_v_num, pad_f_num = self.compute_voxel_verts( + data_dict, self.noise_type, + self.noise_scale) # compute using smpl model + smpl_verts = projection(smpl_verts, data_dict['calib']) + + smpl_verts *= 0.5 + + return { + 'voxel_verts': smpl_verts, + 'voxel_faces': smpl_faces, + 'pad_v_num': pad_v_num, + 'pad_f_num': pad_f_num + } + def get_sampling_geo(self, data_dict, is_valid=False, is_sdf=False): mesh = data_dict['mesh'] @@ -543,13 +625,24 @@ def visualize_sampling3D(self, data_dict, mode='vis'): mesh.visual.vertex_colors = [128.0, 128.0, 128.0, 255.0] vis_list.append(mesh) + if 'voxel_verts' in data_dict.keys(): + print(colored("voxel verts", "green")) + voxel_verts = data_dict['voxel_verts'] * 2.0 + voxel_faces = data_dict['voxel_faces'] + voxel_verts[:, 1] *= -1 + voxel = trimesh.Trimesh( + voxel_verts, voxel_faces[:, [0, 2, 1]], process=False, maintain_order=True) + voxel.visual.vertex_colors = [0.0, 128.0, 0.0, 255.0] + vis_list.append(voxel) + if 'smpl_verts' in data_dict.keys(): + print(colored("smpl verts", "green")) smplx_verts = data_dict['smpl_verts'] smplx_faces = data_dict['smpl_faces'] smplx_verts[:, 1] *= -1 smplx = trimesh.Trimesh( smplx_verts, smplx_faces[:, [0, 2, 1]], process=False, maintain_order=True) - smplx.visual.vertex_colors = ((smplx.vertex_normals + 1.0) * 0.5) + smplx.visual.vertex_colors = [128.0, 128.0, 0.0, 255.0] vis_list.append(smplx) # create a picure diff --git a/lib/renderer/mesh.py b/lib/renderer/mesh.py index fab089e..437467f 100755 --- a/lib/renderer/mesh.py +++ b/lib/renderer/mesh.py @@ -88,6 +88,34 @@ def load_fit_body(fitted_path, scale, smpl_type='smplx', smpl_gender='neutral', return smpl_mesh, smpl_joints +def load_ori_fit_body(fitted_path, smpl_type='smplx', smpl_gender='neutral'): + + param = np.load(fitted_path, allow_pickle=True) + for key in param.keys(): + param[key] = torch.as_tensor(param[key]) + + smpl_model = get_smpl_model(smpl_type, smpl_gender) + model_forward_params = dict(betas=param['betas'], + global_orient=param['global_orient'], + body_pose=param['body_pose'], + left_hand_pose=param['left_hand_pose'], + right_hand_pose=param['right_hand_pose'], + jaw_pose=param['jaw_pose'], + leye_pose=param['leye_pose'], + reye_pose=param['reye_pose'], + expression=param['expression'], + return_verts=True) + + smpl_out = smpl_model(**model_forward_params) + + smpl_verts = smpl_out.vertices[0].detach() + smpl_mesh = trimesh.Trimesh(smpl_verts, + smpl_model.faces, + process=False, maintain_order=True) + + return smpl_mesh + + def save_obj_mesh(mesh_path, verts, faces): file = open(mesh_path, 'w') for v in verts: diff --git a/scripts/render_single.py b/scripts/render_single.py index aa68679..f451733 100644 --- a/scripts/render_single.py +++ b/scripts/render_single.py @@ -1,5 +1,5 @@ import lib.renderer.opengl_util as opengl_util -from lib.renderer.mesh import load_fit_body, load_scan, compute_tangent +from lib.renderer.mesh import load_fit_body, load_scan, compute_tangent, load_ori_fit_body import lib.renderer.prt_util as prt_util from lib.renderer.gl.init_gl import initialize_GL_context from lib.renderer.gl.prt_render import PRTRender @@ -53,6 +53,7 @@ mesh_file = os.path.join( f'./data/{dataset}/scans/{subject}', f'{subject}.{format}') +smplx_file = f'./data/{dataset}/smplx/{subject}.obj' tex_file = f'./data/{dataset}/scans/{subject}/material0.jpeg' fit_file = f'./data/{dataset}/fits/{subject}/smplx_param.pkl' @@ -81,6 +82,12 @@ smpl_type='smplx', smpl_gender='male') +os.makedirs(os.path.dirname(smplx_file), exist_ok=True) +ori_smplx = load_ori_fit_body(fit_file, + smpl_type='smplx', + smpl_gender='male') +ori_smplx.export(smplx_file) + vertices *= scale vmin = vertices.min(0) vmax = vertices.max(0)