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title update

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  1. LICENSE +1 -1
  2. README.md +2 -2
  3. environment.yaml +1 -1
  4. lib/common/render.py +4 -10
  5. lib/dataset/mesh_util.py +1 -1
LICENSE CHANGED
@@ -40,7 +40,7 @@ You acknowledge that the Data & Software is a valuable scientific resource and a
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  Citation:
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  @inproceedings{xiu2023econ,
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- title = {{ECON: Explicit Clothed humans Obtained from Normals}},
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  author = {Xiu, Yuliang and Yang, Jinlong and Cao, Xu and Tzionas, Dimitrios and Black, Michael J.},
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  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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  month = {June},
 
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  Citation:
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  @inproceedings{xiu2023econ,
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+ title = {{ECON: Explicit Clothed humans Optimized via Normal integration}},
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  author = {Xiu, Yuliang and Yang, Jinlong and Cao, Xu and Tzionas, Dimitrios and Black, Michael J.},
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  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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  month = {June},
README.md CHANGED
@@ -2,7 +2,7 @@
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  <p align="center">
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- <h1 align="center">ECON: Explicit Clothed humans Obtained from Normals</h1>
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  <p align="center">
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  <a href="http://xiuyuliang.cn/"><strong>Yuliang Xiu</strong></a>
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  ·
@@ -152,7 +152,7 @@ python -m apps.avatarizer -n <filename>
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  ```bibtex
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  @inproceedings{xiu2023econ,
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- title = {{ECON: Explicit Clothed humans Obtained from Normals}},
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  author = {Xiu, Yuliang and Yang, Jinlong and Cao, Xu and Tzionas, Dimitrios and Black, Michael J.},
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  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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  month = {June},
 
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  <p align="center">
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+ <h1 align="center">ECON: Explicit Clothed humans Optimized via Normal integration</h1>
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  <p align="center">
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  <a href="http://xiuyuliang.cn/"><strong>Yuliang Xiu</strong></a>
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  ·
 
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  ```bibtex
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  @inproceedings{xiu2023econ,
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+ title = {{ECON: Explicit Clothed humans Optimized via Normal integration}},
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  author = {Xiu, Yuliang and Yang, Jinlong and Cao, Xu and Tzionas, Dimitrios and Black, Michael J.},
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  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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  month = {June},
environment.yaml CHANGED
@@ -9,7 +9,7 @@ channels:
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  - defaults
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  dependencies:
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  - python=3.8
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- - pytorch-cuda=11.7
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  - pytorch=1.13.0
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  - nvidiacub
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  - torchvision
 
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  - defaults
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  dependencies:
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  - python=3.8
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+ - pytorch-cuda=11.6
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  - pytorch=1.13.0
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  - nvidiacub
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  - torchvision
lib/common/render.py CHANGED
@@ -315,7 +315,7 @@ class Render:
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  save_path,
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  fourcc,
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  self.fps,
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- (width, int(height * 1.5)),
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  )
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  pbar = tqdm(range(len(self.meshes)))
@@ -358,15 +358,9 @@ class Render:
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  img_cloth = blend_rgb_norm(
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  (torch.stack(mesh_renders)[num_obj:, cam_id] - 0.5) * 2.0, data
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  )
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-
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- top_img = cv2.resize(
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- torch.cat([img_raw, img_smpl],
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- dim=-1).squeeze(0).permute(1, 2, 0).numpy().astype(np.uint8),
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- (width, height // 2)
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- )
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- final_img = np.concatenate(
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- [top_img, img_cloth.squeeze(0).permute(1, 2, 0).numpy().astype(np.uint8)], axis=0
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- )
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  video.write(final_img[:, :, ::-1])
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  video.release()
 
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  save_path,
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  fourcc,
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  self.fps,
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+ (width*3, int(height)),
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  )
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  pbar = tqdm(range(len(self.meshes)))
 
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  img_cloth = blend_rgb_norm(
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  (torch.stack(mesh_renders)[num_obj:, cam_id] - 0.5) * 2.0, data
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  )
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+ final_img = torch.cat(
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+ [img_raw, img_smpl, img_cloth], dim=-1).squeeze(0).permute(1, 2, 0).numpy().astype(np.uint8)
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+
 
 
 
 
 
 
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  video.write(final_img[:, :, ::-1])
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  video.release()
lib/dataset/mesh_util.py CHANGED
@@ -388,7 +388,7 @@ def poisson(mesh, obj_path, depth=10, decimation=True):
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  pcl = o3d.io.read_point_cloud(pcd_path)
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  with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Error) as cm:
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  mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(
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- pcl, depth=depth, n_threads=6
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  )
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  # only keep the largest component
 
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  pcl = o3d.io.read_point_cloud(pcd_path)
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  with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Error) as cm:
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  mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(
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+ pcl, depth=depth, n_threads=-1
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  )
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  # only keep the largest component