PC-Talk — CVPR 2026 Checkpoints
Runtime checkpoints for PC-Talk: Precise Facial Animation Control for Audio-Driven Talking Face Generation.
Download
Clone the source code and download the checkpoints directly into their runtime location:
git clone https://github.com/BQ-Wang0511/PC-Talk.git
cd PC-Talk
pip install -U huggingface_hub
hf download doubi-killer/PC-Talk --local-dir pctalk/checkpoints
The repository contains 12 model files (about 920 MB), preserving the layout expected by PC-Talk:
audio_encoder.pth
lac.pth
emc.pth
style_encoder.pth
liveportrait/
├── landmark.onnx
├── base_models/
│ ├── appearance_feature_extractor.pth
│ ├── motion_extractor.pth
│ ├── spade_generator.pth
│ └── warping_module.pth
└── retargeting_models/
└── stitching_retargeting_module.pth
insightface/models/buffalo_l/
├── 2d106det.onnx
└── det_10g.onnx
lac.pth includes both the LAC model and its refinement model. EMC is used for
emotion control; the style encoder is used for reference-style input. The
LivePortrait and face-analysis weights are included for the inference backend.
The original SHA256SUMS manifest uses paths relative to the source-code
repository root. After downloading, verify the files from that root:
sha256sum -c pctalk/checkpoints/SHA256SUMS
These are downloadable inference assets, not a Transformers-format model. Use the PC-Talk source code to run inference. See its README for installation, image/video examples, emotion controls and articulation settings.
Usage terms and safety
The source-code license does not automatically license every model weight. Third-party components retain their upstream terms:
In particular, the InsightFace model assets are restricted to non-commercial research use and must be replaced for commercial deployment. Consult the upstream licenses and the source repository's third-party notices before use. Only load PyTorch checkpoints from trusted sources.
Citation
@inproceedings{wang2026pc,
title={PC-Talk: Precise Facial Animation Control for Audio-Driven Talking Face Generation},
author={Wang, Baiqin and Zhu, Xiangyu and Shen, Fan and Xu, Hao and Lei, Zhen},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={25153--25162},
year={2026}
}