SemanticTransfer Pretrained Weights

Pretrained checkpoints for Semantic Correspondence via 2D-3D-2D Cycle (You et al., 2020), which predicts semantic correspondences by lifting 2D images to 3D and projecting corresponding 3D models back to 2D with semantic labels.

Code: https://github.com/qq456cvb/SemanticTransfer

Contents

File	Network	Size
marrnet1.pt	MarrNet-1: 2.5D sketch (depth/normal/silhouette) estimation	123 MB
shapehd.pt	ShapeHD: 3D shape completion from 2.5D sketches	277 MB
best.pt	Viewpoint (azimuth/elevation) estimation network	435 MB

Usage

Download into the repository's weights/ folder:

hf download qq456cvb/SemanticTransfer marrnet1.pt shapehd.pt best.pt --local-dir weights

Then run the demo:

python demo.py

Citation

@article{you2020semantic,
  title={Semantic Correspondence via 2D-3D-2D Cycle},
  author={You, Yang and Li, Chengkun and Lou, Yujing and Cheng, Zhoujun and Ma, Lizhuang and Lu, Cewu and Wang, Weiming},
  journal={arXiv preprint arXiv:2004.09061},
  year={2020}
}