Summary of Vividface: a Diffusion-based Hybrid Framework For High-fidelity Video Face Swapping, by Hao Shao et al.
VividFace: A Diffusion-Based Hybrid Framework for High-Fidelity Video Face Swapping
by Hao Shao, Shulun Wang, Yang Zhou, Guanglu Song, Dailan He, Shuo Qin, Zhuofan Zong, Bingqi Ma, Yu Liu, Hongsheng Li
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel diffusion-based framework for video face swapping, addressing the limitations of existing static image-focused methods. The framework combines abundant static image data with temporal video sequences through an image-video hybrid training approach. This allows the model to better maintain temporal coherence in generated videos. To disentangle identity and pose features, the authors construct the Attribute-Identity Disentanglement Triplet (AIDT) Dataset, which includes face images with similar poses or identities. The dataset is enhanced with occlusion augmentation for robustness against partial occlusions. Additionally, 3D reconstruction techniques are integrated as input conditioning to handle large pose variations. Experimental results demonstrate superior performance in identity preservation, temporal consistency, and visual quality compared to existing methods, while requiring fewer inference steps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to swap faces in videos more accurately than before. Right now, most face swapping methods only work with still images, but this new approach uses both image and video data to create more realistic results. The researchers also created a special dataset that helps the model understand what’s important – whether it’s a person’s identity or their pose (how they’re standing or sitting). This allows the model to generate videos that are more consistent with real-life situations. To make things even better, the team added some extra features like handling occlusions and large pose variations. Overall, this approach is much better at preserving identities, maintaining temporal consistency, and producing high-quality results. |
Keywords
» Artificial intelligence » Diffusion » Inference
