PDRVLA is introduced, whose lightweight Physical Dynamics Module extracts physical tokens from vision--language features to condition action generation without online video generation, andlations show improved future prediction with world-model supervision and evaluate how subtask conditioning, future object-pose super...
Han-Qing Wang, Zhao-Tian Cai, Ruichuan An et al.· 0 citations
Detailed video captioning has become an essential semantic interface for multi-modal understanding and generation. While existing methods mainly target short videos, extending them to long videos remains challenging, primarily due to limited context windows of current video understanding models. A naive solution is to...
Xinlong Chen, Wei-Hong Lin, Yuan-Xing Zhang et al.· 0 citations
This work introduces SPACE (Subject-centric Poses, Appearance, and Characteristics Evaluation), a benchmark designed to evaluate subject-centered spatial understanding, and designs a rubric-based reward for Group Relative Policy Optimization that explicitly penalizes structurally critical spatial errors during alignmen...
Bozhou Li, Jia-Hang Zhang, Yue Ding et al.· 0 citations
Flux-OPD is proposed, an OPD paradigm that uses evolving contexts as in-training supervision to capture task preferences in open-ended domains and outperforms existing OPD paradigms, highlighting the potential to combine teacher supervision with evolving contexts.