5 papers
Ranking-aware Reinforcement Learning for Ordinal Ranking
Aiming Hao, Chen Zhu, Jiashu Zhu +2
Ordinal regression and ranking are challenging due to inherent ordinal dependencies that conventional methods struggle to model. We propose Ranking-Aware Reinforcement Learning (RA…
Latent Temporal Discrepancy as Motion Prior: A Loss-Weighting Strategy for Dynamic Fidelity in T2V
Meiqi Wu, Bingze Song, Ruimin Lin +5
Video generation models have achieved notable progress in static scenarios, yet their performance in motion video generation remains limited, with quality degrading under drastic d…
Artifact-Aware Evaluation for High-Quality Video Generation
Chen Zhu, Jiashu Zhu, Yanxun Li +6
With the rapid advancement of video generation techniques, evaluating and auditing generated videos has become increasingly crucial. Existing approaches typically offer coarse vide…
ImagerySearch: Adaptive Test-Time Search for Video Generation Beyond Semantic Dependency Constraints
Meiqi Wu, Jiashu Zhu, Xiaokun Feng +7
Video generation models have achieved remarkable progress, particularly excelling in realistic scenarios; however, their performance degrades notably in imaginative scenarios. Thes…
VMBench: A Benchmark for Perception-Aligned Video Motion Generation
Xinran Ling, Chen Zhu, Meiqi Wu +7
Video generation has advanced rapidly, improving evaluation methods, yet assessing video's motion remains a major challenge. Specifically, there are two key issues: 1) current moti…