| ---
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| license: mit
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| pipeline_tag: other
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| tags:
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| - semantic-correspondence
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| - 3d
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| - keypoints
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| ---
|
|
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| # SemanticTransfer Pretrained Weights
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|
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| Pretrained checkpoints for [Semantic Correspondence via 2D-3D-2D Cycle](https://arxiv.org/abs/2004.09061) (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.
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|
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| Code: https://github.com/qq456cvb/SemanticTransfer
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|
|
| ## Contents
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|
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| | File | Network | Size |
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| |---|---|---|
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| | `marrnet1.pt` | MarrNet-1: 2.5D sketch (depth/normal/silhouette) estimation | 123 MB |
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| | `shapehd.pt` | ShapeHD: 3D shape completion from 2.5D sketches | 277 MB |
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| | `best.pt` | Viewpoint (azimuth/elevation) estimation network | 435 MB |
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|
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| ## Usage
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|
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| Download into the repository's `weights/` folder:
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|
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| ```bash
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| hf download qq456cvb/SemanticTransfer marrnet1.pt shapehd.pt best.pt --local-dir weights
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| ```
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|
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| Then run the demo:
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|
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| ```bash
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| python demo.py
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| ```
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|
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| ## Citation
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|
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| ```bibtex
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| @article{you2020semantic,
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| title={Semantic Correspondence via 2D-3D-2D Cycle},
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| author={You, Yang and Li, Chengkun and Lou, Yujing and Cheng, Zhoujun and Ma, Lizhuang and Lu, Cewu and Wang, Weiming},
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| journal={arXiv preprint arXiv:2004.09061},
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| year={2020}
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| }
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| ```
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|
|
