Author

Shikun Feng

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Preprint Aug 2026

UniVVT: A Unified End-to-End Framework for High-Fidelity Video Virtual Try-on

UniVVT is presented, a unified end-to-end framework that reframes VVT as semantically conditioned video generation, eliminating mask, pose, and warping modules at inference and validating implicit semantic guidance as a simple and effective alternative to fragile geometric preprocessing for end-to-end virtual try-on.

Yushe Cao, Shikun Feng, Fei Shen et al. · 0 citations

#artificial intelligence Preprint Aug 2026

LiveVVT: High-Fidelity Video Virtual Try-On in Real Time

LiveVVT is introduced, a rolling streaming diffusion framework that preserves bounded bidirectional modeling within causal recurrent generation, and a progressive distillation framework integrating bidirectional VVT learning, teacher-trajectory regression for causal few-step adaptation, and Collaborative Matching Distillation, which couples teacher-distribution matching with rolling flow matching on real videos to align optimization with recurrent inference.

Yushe Cao, Shikun Feng, Ru-Xiang Duan et al. · 0 citations