arXiv:2605.04515v2 Announce Type: replace
Abstract: Video Large Language Models (Video-LLMs) excel in general video understanding but often base physical judgments on event expectations rather than o...
By Zicheng Zhao, Chaofan Gan, Shijie Li, Weiyao Lin
CompAdapt is a physics-consistent text-to-video generation framework that extends diffusion-based models to handle composite physical behaviors such as coupled motions, multi-stage transitions, and multi-object collisions. It translates natural language prompts into structured physical semantics, enabling end-to-end specification of motion types, temporal relations, and initial parameters. The system introduces dynamics-aware prior matching for one-shot adaptation to new physical environments and a physics-aware latent feature fusion module to enhance visual fidelity during fast, complex motion, outperforming existing physics-constrained baselines on physics-focused T2V benchmarks.
By Haoran Qin (Harbin Institute of Technology, China), Renlong Wu (Harbin Institute of Technology, China), Tianyu Huang (Harbin Institute of Technology, China), Yukang Ding (Taobao, Alibaba Group, China), Hui Li (Harbin Institute of Technology, China), Wangmeng Zuo (Harbin Institute of Technology, China)
EvoWorld introduces a framework that couples a vision‑language model with a segment‑autoregressive video renderer to enable generative video models to reason about world evolution beyond visual fidelity. The system maintains an evolving account of the world, infers event consequences, and renders causally plausible outcomes even when events occur out of sight. The accompanying EvoWorld‑Data and EvoWorld‑Bench datasets provide training and evaluation for event realization, causal reactions, and revisit consistency.
By Yequan Bie, Dengyang Jiang, Zhixuan Chen, Xiaozhuan Liang, Haoxuan Che, Quande Liu, Xi Chen, Hao Chen
Video generation models achieve high visual quality but often struggle to generate physics-aware videos. Unlike rigid-body motion, which can be described by explicit trajectories or formulas, complex deformation dynamics remain challenging to synthesize.
arXiv:2606. 18586v1 Announce Type: cross Abstract: Physical events are not understood by their names alone, but by the causal state changes that compose them.
By Shang Wu, Haoran Lu, Songling Liu, Chenwei Xu, Lie Lu, Pranav Maneriker, Fan Du, Manling Li, Zhaoran Wang, Han Liu
PhyEvolve is a multi-agent framework that improves physics-aware video generation by enhancing natural-language prompts through structured state-evolution planning. It employs three agents—a Director, a Physical Evolver, and a Composer—to ground scenes, identify physical mechanisms, and construct causally linked state transitions that guide video generation. The method works with existing generators via prompt replacement and achieves state‑of‑the‑art performance, outperforming the prior agentic baseline by an average of 7.9 percentage points on four major benchmarks.