metadata
license: mit
pipeline_tag: other
tags:
- semantic-correspondence
- 3d
- keypoints
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}
}