Summary of S3editor: a Sparse Semantic-disentangled Self-training Framework For Face Video Editing, by Guangzhi Wang et al.
S3Editor: A Sparse Semantic-Disentangled Self-Training Framework for Face Video Editing
by Guangzhi Wang, Tianyi Chen, Kamran Ghasedi, HsiangTao Wu, Tianyu Ding, Chris Nuesmeyer, Ilya Zharkov, Mohan Kankanhalli, Luming Liang
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 A novel face attribute editing framework called S3Editor is introduced in this paper, which addresses challenges in preserving identity, editing faithfulness, and temporal consistency. The proposed Sparse Semantic-disentangled Self-training (S3Editor) framework employs a self-training paradigm to enhance the training process through semi-supervision, a semantic disentangled architecture with dynamic routing, and a structured sparse optimization schema that identifies and deactivates malicious neurons. S3Editor is model-agnostic and compatible with various editing approaches. The results show significant improvements in identity preservation, editing fidelity, and temporal consistency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Face attribute editing is important for many applications. However, existing methods have trouble getting good results while keeping the person’s identity intact, making sure the edit looks real, and ensuring the video stays consistent over time. This paper solves these problems with a new framework called S3Editor. It uses a self-training method to make the training process better, a special architecture that separates semantic features from other attributes, and an optimization technique that finds and removes bad neurons. The results show that this approach works well. |
Keywords
» Artificial intelligence » Optimization » Self training » Semi supervision
