mlsanityData to 7 Oct 2026

Physical Simulator In-the-Loop Video Generation

arXiv 2603.06408 ↗First posted 6 Mar 2026cs.CV cs.AI cs.GRcited by 12

On the map: Diffusion models → Video generation

Abstract (arXiv)

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/

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 41 of its 52 references that are in the map. First posted on arXiv 6 Mar 2026.

Builds on

41 of 52 references.

Closest ancestors · 10

Same direction, newest first

  1. 2025PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generation24 Sep
  2. 2025PISA Experiments: Exploring Physics Post-Training for Video Diffusion Models by Watching Stuff Drop12 Mar
  3. 2025Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise14 Jan
  4. 2024DiTCtrl: Exploring Attention Control in Multi-Modal Diffusion Transformer for Tuning-Free Multi-Prompt Longer Video Generation24 Dec
  5. 2024PhyT2V: LLM-Guided Iterative Self-Refinement for Physics-Grounded Text-to-Video Generation30 Nov
  6. 2024CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer12 Aug
  7. 2024MotionCraft: Physics-based Zero-Shot Video Generation22 May
  8. 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets25 Nov
  9. 2023GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning21 Nov
  10. 2022Video Diffusion Models7 Apr

Other references · 31

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,094
  4. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,274
  5. 2024SAM 2: Segment Anything in Images and Videoscited by 2,784
  6. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
  7. 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,755
  8. 2024HunyuanVideo: A Systematic Framework For Large Video Generative Modelscited by 1,597
  9. 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,437
  10. 2022XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Modelcited by 436
  11. 2024InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Modelscited by 421
  12. 2023PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamicscited by 368
19 more
  1. 2024How Far is Video Generation from World Model: A Physical Law Perspectivecited by 204
  2. 2024Motion Prompting: Controlling Video Generation with Motion Trajectoriescited by 176
  3. 2024PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generationcited by 169
  4. 2025ViPE: Video Pose Engine for 3D Geometric Perceptioncited by 162
  5. 2024DragAnything: Motion Control for Anything using Entity Representationcited by 159
  6. 2025Do generative video models understand physical principles?cited by 151
  7. 2024MotionClone: Training-Free Motion Cloning for Controllable Video Generationcited by 104
  8. 2025Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Controlcited by 95
  9. 2024SG-I2V: Self-Guided Trajectory Control in Image-to-Video Generationcited by 68
  10. 2025PhysGen3D: Crafting a Miniature Interactive World from a Single Imagecited by 65
  11. 2024Image Conductor: Precision Control for Interactive Video Synthesiscited by 62
  12. 2025TokenVerse: Versatile Multi-concept Personalization in Token Modulation Spacecited by 51
  13. 2025Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signalscited by 51
  14. 2025WonderPlay: Dynamic 3D Scene Generation from a Single Image and Actionscited by 43
  15. 2025PhysAnimator: Physics-Guided Generative Cartoon Animationcited by 40
  16. 2023Distribution-Aligned Diffusion for Human Mesh Recoverycited by 27
  17. 2023AI-Generated Content (AIGC) for Various Data Modalities: A Surveycited by 25
  18. 2023Neural Stress Fields for Reduced-order Elastoplasticity and Fracturecited by 24
  19. 2025VHOI: Controllable Video Generation of Human-Object Interactions from Sparse Trajectories via Motion Densificationcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 12.

12 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025VLIPP: Towards Physically Plausible Video Generation with Vision and Language Informed Physical Prior
  2. 2026PhyWorld: Physics-Faithful World Model for Video Generation
  3. 2026PhysAlign: Physics-Coherent Image-to-Video Generation through Feature and 3D Representation Alignment
  4. 2026PhyCo: Learning Controllable Physical Priors for Generative Motion
  5. 2024PhysMotion: Physics-Grounded Dynamics From a Single Image
  6. 2024Ctrl-V: Higher Fidelity Video Generation with Bounding-Box Controlled Object Motion
  7. 2024DreamPhysics: Learning Physics-Based 3D Dynamics with Video Diffusion Priors
  8. 2026MPMWorlds: Material-Point-Method Simulations for Inferring and Extrapolating Physical Dynamics