TryOnReward: Learning Foveated Consistency for Reinforcement Fine-Tuning of Virtual Try-On
This work presents TryOnReward, a fine-grained reward model tailored for VTON that significantly outperforms generic judges in human preference alignment, and when serving as the RFT reward function, it consistently yields human-preferred try-on results across multiple baselines.
Xue-Heng Li, Yong Liu, Xiao-Long Fu et al.
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