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 existing generators without retraining. Experiments on four major benchmarks show that PhyEvolve outperforms the prior agentic baseline by an average of 7.9 percentage points, demonstrating that explicit causal planning boosts physical consistency and state continuity in generated videos.
By Yang-Hao Zhou, Lixing Tan, Zhiqi Li, Guang Yang, Xiao Yang
arXiv:2607. 16355v1 Announce Type: cross Abstract: Recent advances in physics-grounded video generation leverage physics simulation as a physical prior to guide video synthesis toward physically plausible outcomes.
By Qirui Li, Jinkun Hao, Yibo Li, Ran Yi, Paul L. Rosin, Yu-Kun Lai
The paper introduces SMART, a Self‑evolving Multi‑Agent Refinement Framework for conversational text‑to‑motion generation. It enables users to iteratively refine specific spatio‑temporal regions of a motion sequence based on natural language feedback, preserving the rest of the motion. The framework incorporates a semantic‑guided masked editing mechanism and a memory‑based self‑evolving strategy to reuse past editing knowledge and reduce redundant exploration.
By Jianan Li, Jiawei Zhao, Yangtao Zhou, Zhifu Zhao, Shaoxin Wu, Yutao Wang, Dezhen Wang, Hua Chu, Fu Li
The paper introduces Physically Plausible Video Generation (PPVG), a method that generates videos consistent with physical laws by treating physical evolution as a chain of causally connected events. It employs three modules: Physics-driven Event Chain Reasoning to decompose phenomena into scene-graph events, Transition-aware Routed Keyframe Conditioning to guide keyframe synthesis for smooth transitions, and Physics-injected Contrastive Semantic Guidance to steer generation toward plausible dynamics. Experiments on multiple physics benchmarks show improved physical plausibility compared to prior approaches.
By Zixuan Wang, Yixin Hu, Wen Li, Feng Chen, Yan Liu, Duo Peng, Yinjie Lei
VideoEvolve is a framework that automatically evolves agent harnesses for video temporal grounding, a task that localizes events in videos based on natural-language queries. It introduces a Cloze-Structured Harness Representation to keep stage interfaces stable while allowing agent workflows and instructions to change, and uses Branch-Guided Harness Evolution to preserve promising code branches, guide local edits with execution feedback, and validate improvements. Experiments show that this automated evolution improves grounding performance across multiple benchmarks, with instruction refinement consistently yielding gains.
By Bingjun Luo, Yuhuan Fan, Jialin Guo, Siqi Li
arXiv:2607. 01766v1 Announce Type: new Abstract: LLM agents are increasingly used to translate natural language into 3D scenes in a procedural way, but existing systems focus on static output.
By Chunjiang Liu, Xiaoyuan Wang, Haoyu Chen, Yizhou Zhao, Ming-Hsuan Yang, L\'aszl\'o A. Jeni