6 papers
Making MLLM Latents Matter: Factor-Aware Predictive Learning for Cinematic Video Generation
Songlin Yang, Zhe Wang, Xiaotong Zhao +8
Cinematic video generation requires both temporal inference from limited inputs (i.e., sparse rather than frame-by-frame guidance) and factor-aware control of character, scene, act…
OmniVBench: A Benchmark and Large-Scale Dataset for Omni Reference-to-Video Generation
Wenxue Li, Peiyan Guan, Haoyang Jiang +15
Omni reference-to-video (R2V) generation is emerging as a general paradigm that enables flexible video customization from diverse and compositional visual references. However, exis…
WorldCycle: Self-Verifiable Reinforcement Learning for Long-Horizon Video World Models
Bohai Gu, Yueyang Yuan, Taiyi Wu +9
Interactive video world models are essential for long-horizon planning and exploration, yet they suffer from compounding errors. Post-training methods such as reinforcement learnin…
WorldCraft: From Camera Navigation to Object Manipulation in Interactive Video World Models
Bohai Gu, Taiyi Wu, Yueyang Yuan +9
Recent video world models enable interactive camera navigation but provide limited control over individual objects. We study persistent object-state control in autoregressive video…
ShotVerse: Advancing Cinematic Camera Control for Text-Driven Multi-Shot Video Creation
Songlin Yang, Zhe Wang, Xuyi Yang +7
Text-driven video generation has democratized film creation, but camera control in cinematic multi-shot scenarios remains a significant block. Implicit textual prompts lack precisi…
Place-it-R1: Unlocking Environment-aware Reasoning Potential of MLLM for Video Object Insertion
Bohai Gu, Taiyi Wu, Dazhao Du +5
Video object insertion is fundamental to video editing, yet existing diffusion methods often produce visually plausible but physically inconsistent results. We present Place-it-R1,…