Physical Simulator In-the-Loop Video Generation
- URL: http://arxiv.org/abs/2603.06408v1
- Date: Fri, 06 Mar 2026 15:48:25 GMT
- Title: Physical Simulator In-the-Loop Video Generation
- Abstract summary: Physical Simulator In-the-loop Video Generation (PSIVG) is a novel framework that integrates a physical simulator into the video diffusion process.<n>PSIVG produces videos that better adhere to real-world physics while preserving visual quality and diversity.
- Score: 96.87054314612142
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Recent advances in diffusion-based video generation have achieved remarkable visual realism but still struggle to obey basic physical laws such as gravity, inertia, and collision. Generated objects often move inconsistently across frames, exhibit implausible dynamics, or violate physical constraints, limiting the realism and reliability of AI-generated videos. We address this gap by introducing Physical Simulator In-the-loop Video Generation (PSIVG), a novel framework that integrates a physical simulator into the video diffusion process. Starting from a template video generated by a pre-trained diffusion model, PSIVG reconstructs the 4D scene and foreground object meshes, initializes them within a physical simulator, and generates physically consistent trajectories. These simulated trajectories are then used to guide the video generator toward spatio-temporally physically coherent motion. To further improve texture consistency during object movement, we propose a Test-Time Texture Consistency Optimization (TTCO) technique that adapts text and feature embeddings based on pixel correspondences from the simulator. Comprehensive experiments demonstrate that PSIVG produces videos that better adhere to real-world physics while preserving visual quality and diversity. Project Page: https://vcai.mpi-inf.mpg.de/projects/PSIVG/
Related papers
- TelePhysics: Physics-Grounded Multi-Object Scene Generation from a Single Image with Real-Time Interaction [51.01447538245441]
We propose TelePhysics, a training-free framework that converts a single image into a physically consistent and controllable video.<n>By representing the full scene geometry in a unified spatial coordinate system, TelePhysics resolves object penetration and alignment ambiguity.<n> Experimental results demonstrate that TelePhysics substantially outperforms prior methods in physical fidelity, spatial coherence, and controllability.
arXiv Detail & Related papers (2026-05-19T08:16:44Z) - 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) - PhysAlign: Physics-Coherent Image-to-Video Generation through Feature and 3D Representation Alignment [23.066204478030627]
Video Models (VDMs) offer a promising approach for simulating dynamic scenes and environments.<n>Existing models often generate temporally incoherent content that violates basic physical intuition.<n>We propose PhysAlign, an efficient framework for physics-coherent image-to-videoI (2V) generation.
arXiv Detail & Related papers (2026-03-14T05:44:18Z) - 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) - PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generation [53.06495362038348]
Existing generation models excel at producing photo-realistic videos from text or images, but often lack physical plausibility and 3D controllability.<n>We introduce PhysCtrl, a novel framework for physics-grounded image-to-video generation with physical parameters and force control.<n> Experiments show that PhysCtrl generates realistic, physics-grounded motion trajectories which, when used to drive image-to-video models, yield high-fidelity, controllable videos.
arXiv Detail & Related papers (2025-09-24T17:58:04Z) - PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation [29.831214435147583]
We present PhysGen, a novel image-to-video generation method.
It produces a realistic, physically plausible, and temporally consistent video.
Our key insight is to integrate model-based physical simulation with a data-driven video generation process.
arXiv Detail & Related papers (2024-09-27T17:59:57Z) - DreamPhysics: Learning Physics-Based 3D Dynamics with Video Diffusion Priors [75.83647027123119]
We propose to learn the physical properties of a material field with video diffusion priors.<n>We then utilize a physics-based Material-Point-Method simulator to generate 4D content with realistic motions.
arXiv Detail & Related papers (2024-06-03T16:05:25Z)
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.