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Achieving seamless audio-visual identity swapping in talking videos while preserving original dynamics could revolutionize content creation and personalization.
Achieving real-time human animation at nearly 20 FPS while preserving identity and quality over three-minute streams sets a new standard for interactive applications.
Realistic cloth dynamics in animatable avatars can be achieved by explicitly modeling temporal causality, leading to significant improvements in rendering quality and generalization.
Naive matching in diffusion distillation can inadvertently amplify errors due to hidden information in teacher models, leading to a surprising failure mode called Negative Branch Asymmetry.
