提升人脸替换质量，让换脸更自然逼真。
MotionSwap
- 在生成器中加入自注意力与交叉注意力机制。
- 训练40万轮后，身份保留更好，FID分数更低。
- 适合需要高质量换脸的视频创作与影视应用。
人脸替换技术在学术研究与商业应用中备受关注。本文对SimSwap这一高效高保真人脸替换框架进行了实现与优化。通过在生成器架构中引入自注意力与交叉注意力机制、动态损失权重及余弦退火学习率调度，显著提升了身份保留性、属性一致性与整体视觉质量。实验基于40万次训练迭代，结果显示生成器与判别器性能持续提升，新模型在身份相似度、FID分数上均优于基线，且定性结果更优。消融实验证明各项改进均具关键作用。未来方向包括集成StyleGAN3、改善唇部同步、引入3D面部建模及视频应用中的时序一致性。
原文摘要 · Abstract (English)
Face swapping technology has gained significant attention in both academic research and commercial applications. This paper presents our implementation and enhancement of SimSwap, an efficient framework for high fidelity face swapping. We introduce several improvements to the original model, including the integration of self and cross-attention mechanisms in the generator architecture, dynamic loss weighting, and cosine annealing learning rate scheduling. These enhancements lead to significant improvements in identity preservation, attribute consistency, and overall visual quality. Our experimental results, spanning 400,000 training iterations, demonstrate progressive improvements in generator and discriminator performance. The enhanced model achieves better identity similarity, lower FID scores, and visibly superior qualitative results compared to the baseline. Ablation studies confirm the importance of each architectural and training improvement. We conclude by identifying key future directions, such as integrating StyleGAN3, improving lip synchronization, incorporating 3D facial modeling, and introducing temporal consistency for video-based applications.
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