Flow-based Vision-Language-Action (VLA) policies are inherently stochastic: different noise seeds can produce action chunks of varying quality from the same observation. At test time, however, the policy typically commits to a single sample without an explicit estimate of its quality. In fine-grained manipulation, exec...
Xue-Li Sun, Yuxiang Lu, Yan-Tong Liu et al.· 0 citations
The quality and diversity of instruction-based video editing datasets are steadily improving, yet existing datasets mainly focus on single editing operations and fall short in supporting compositional instruction-guided video editing. In particular, multiple editing intents must be jointly understood and faithfully exe...
Fuchen Long, Cong Wang, Zitao Gao et al.· 1 citation
The OmniEdit-Bench provides a comprehensive and reliable testbed for evaluating instruction-based video editing and offers insights into future research directions, including an accuracy-aware penalty mechanism that conditions other scores on accuracy, preventing visually plausible but incorrect edits from receiving in...
Chenxuan Miao, Yutong Feng, Yi Lu et al.· 1 citation
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