Reinforcement learning (RL) for video generation usually assigns one scalar reward to an entire sampled video. Yet a video is not uniformly flawed: some visual tokens may already satisfy the prompt, whereas others require correction. A scalar reward cannot localize errors, causing optimization to perturb satisfactory t...
Yi-Fan Wang, G. Qian, Yan-Yu Li et al.· 0 citations
This work introduces Projected Distribution Matching Distillation (PDMD) to filter critic errors, and shows how this projection stabilizes training and improves sample quality where DMD degrades and develops unnatural textures.
Zi-Mo Wang, Junkun Yuan, Ang-Tian Wang et al.· 0 citations
An event-aware evaluation protocol that separately measures post-trigger editing quality, pre-trigger preservation, and false-trigger robustness is introduced.
Jinjie Mai, G. Qian, W. Menapace et al.· arXiv.org· 1 citation
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