PhysAgent: Reflective Agentic Physics Control for Physically Plausible Video Generation

• URL: http://arxiv.org/abs/2607.16355v1
• Date: Fri, 17 Jul 2026 08:55:09 GMT
• Title: PhysAgent: Reflective Agentic Physics Control for Physically Plausible Video Generation
• Abstract summary: PhysAgent is a framework that closes the loop among physical program generation, physics simulation, stage-specific verification, and targeted program repair.<n>Our framework produces more physically plausible videos, achieves better prompt alignment, and generalizes more effectively across diverse physical scenarios.
• Score: 68.13035350819901
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in physics-grounded video generation leverage physics simulation as a physical prior to guide video synthesis toward physically plausible outcomes. The simulation process is controlled by physical specifications, which are typically generated by a vision-language model in a single pass. Such one-shot prediction often fails to accurately translate user intent into executable simulations, particularly for fine-grained object dynamics, complex motion trajectories, and temporally structured interactions. In this paper, we propose PhysAgent, a reflective agentic framework that closes the loop among physical program generation, physics simulation, stage-specific verification, and targeted program repair. Beyond improving the control of coupled physical parameters, our framework enables the agent to progressively realize complex trajectories, multi-stage interactions, and precise event outcomes by treating each physical program as an executable hypothesis. In addition, we design a set of physics-control APIs to support more stable and complex motion behaviors. Extensive experiments demonstrate that PhysAgent produces more physically plausible videos, achieves better prompt alignment, and generalizes more effectively across diverse physical scenarios.

Related papers

• Learning Explicit Physical Parameter Control and Benchmarking for Video Generation [21.331326205371628]
  We present PhyParam, a physics-guided image-to-video diffusion model that conditions on object-level forces, masses, friction, restitution, and scene-level gravity.<n>We also establish PhyParam-Bench, a benchmark for physical-law consistency in image-to-video generation.
  arXiv  Detail & Related papers  (2026-07-21T10:06:55Z)
• Coding Agent Is Good As World Simulator [5.161736405778465]
  We present an agentic framework constructing physics-based world models through executable simulation code.<n>The framework coordinates planning, code generation, visual review, and physics analysis agents.<n> Experimental results show that our framework outperforms advanced video-based models in physical accuracy, instruction fidelity and visual quality.
  arXiv  Detail & Related papers  (2026-05-14T05:33:41Z)
• PhyCo: Learning Controllable Physical Priors for Generative Motion [55.59209981836171]
  We present PhyCo, a framework that introduces continuous, interpretable, and physically grounded control into video generation.<n>Our approach integrates three key components: (i) a large-scale dataset of over 100K photorealistic simulation videos where friction, restitution, deformation, and force are systematically varied across diverse scenarios; (ii) physics-supervised fine-tuning of a pretrained diffusion model using a ControlNet conditioned on pixel-aligned physical property maps; and (iii) VLM-guided reward optimization, where a fine-tuned vision-language model evaluates generated videos with targeted physics queries and provides differentiable feedback.
  arXiv  Detail & Related papers  (2026-04-30T17:53:03Z)
• PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models [100.65199317765608]
  Physical principles are fundamental to realistic visual simulation, but remain a significant oversight in transformer-based video generation.<n>We introduce a physics-aware reinforcement learning paradigm for video generation models that enforces physical collision rules directly in high-dimensional spaces.<n>We extend this paradigm to a unified framework, termed Mimicry-Discovery Cycle (MDcycle), which allows substantial fine-tuning.
  arXiv  Detail & Related papers  (2026-01-16T08:40:10Z)
• ProPhy: Progressive Physical Alignment for Dynamic World Simulation [55.456455952212416]
  ProPhy is a Progressive Physical Alignment Framework that enables explicit physics-aware conditioning and anisotropic generation.<n>We show that ProPhy produces more realistic, dynamic, and physically coherent results than existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-12-05T09:39:26Z)
• PhysChoreo: Physics-Controllable Video Generation with Part-Aware Semantic Grounding [50.454084539837005]
  PhysChoreo is a novel framework that can generate videos with diverse controllability and physical realism from a single image.<n>Our method consists of two stages: first, it estimates the static initial physical properties of all objects in the image through part-aware physical property reconstruction.<n>Then, through temporally instructed and physically editable simulation, it synthesizes high-quality videos with rich dynamic behaviors and physical realism.
  arXiv  Detail & Related papers  (2025-11-25T17:59:04Z)
• PhysCorr: Dual-Reward DPO for Physics-Constrained Text-to-Video Generation with Automated Preference Selection [10.498184571108995]
  We propose PhysCorr, a unified framework for modeling, evaluating, and optimizing physical consistency in video generation.<n>Specifically, we introduce PhysicsRM, the first dual-dimensional reward model that quantifies both intra-object stability and inter-object interactions.<n>Our approach is model-agnostic and scalable, enabling seamless integration into a wide range of video diffusion and transformer-based backbones.
  arXiv  Detail & Related papers  (2025-11-06T02:40:57Z)
• PhysWorld: From Real Videos to World Models of Deformable Objects via Physics-Aware Demonstration Synthesis [52.905353023326306]
  We propose PhysWorld, a framework that synthesizes physically plausible and diverse demonstrations to learn efficient world models.<n>Experiments show that PhysWorld has competitive performance while enabling inference speeds 47 times faster than the recent state-of-the-art method, i.e., PhysTwin.
  arXiv  Detail & Related papers  (2025-10-24T13:25:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.