From Short Clips to Real-Time Streams: Identity-Aware Supervision and Context-Efficient Generation for Video Face Swapping
Video face swapping transfers the reference identity to a target video while preserving its other attributes and motion.
ProofPaper ↗
Key points
- Despite recent progress in reference-guided flow-based methods, jointly achieving faithful identity transfer, high fidelity, and real-time generation over long video streams remains challenging.
- To address this challenge, we rethink the task-specific structure of reference-guided video face swapping.
- By integrating these designs, we propose StreamingSwap, a reference-guided flow-matching framework for real-time streaming video face swapping.
- Experiments demonstrate that our method achieves competitive identity transfer and visual quality at 20 FPS on a single NVIDIA RTX 4090 GPU, with stable generation over videos up to 10 minutes long.
Sources (2)
- [1]From Short Clips to Real-Time Streams: Identity-Aware Supervision and Context-Efficient Generation for Video Face SwappingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 04:28 PM
Video face swapping transfers the reference identity to a target video while preserving its other attributes and motion.
Despite recent progress in reference-guided flow-based methods, jointly achieving faithful identity transfer, high fidelity, and real-time generation over long video streams remains challenging.
- [2]From Short Clips to Real-Time Streams: Identity-Aware Supervision and Context-Efficient Generation for Video Face SwappingHugging Face Daily Papers · Oct 19, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
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