mlsanityData to 14 Oct 2026

TourPhysics: Bringing Physics to World Models for Exploration and Manipulation from a Single Image

arXiv 2609.04911 ↗First posted 4 Sep 2026cs.CVcited by 2

On the map: World models → Latent predictive world models

Abstract (arXiv)

Interactive visual world models must distinguish observation from physical intervention. Camera motion reveals new surfaces, whereas intervention changes object motion, contact, and deformation. Current video world models are largely driven by appearance priors and often lose physical or spatial consistency over long horizons. We present TourPhysics, an online framework initialized from a single image and a declarative physical configuration. TourPhysics extends PhysOmni, our ACM Multimedia 2026 work, from finite physics-grounded video synthesis to persistent exploration and manipulation. TourPhysics combines deterministic simulation with video generation while assigning separate roles to simulator state, geometric evidence, generator controls, and appearance memory. For each action, the simulator computes a finite physical and camera trajectory before the corresponding observation is generated. Accepted observations publish the terminal state and update the appearance memory and subsequent generator controls, while the committed state and simulator geometry remain fixed throughout synthesis and retry. We further separate the simulator geometry used for projection and visibility from the relative depth used to condition the generator. A reference-anchored memory retrieves accepted static appearance through geometric cross-view correspondence and incorporates it through a bounded residual that reverts to the native path when no valid correspondence exists. On simulator-defined camera tours and object manipulations, TourPhysics follows prescribed camera and object trajectories more closely than the evaluated baselines, preserves the input scene, and reduces appearance drift during long-horizon revisits.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Latent predictive world models, one of 11 directions in World models. Builds on 75 of its 95 references that are in the map. First posted on arXiv 4 Sep 2026.

Builds on

75 of 95 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signals26 May
  2. 2024PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generation19 Apr

Other references · 73

  1. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  2. 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,708
  3. 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,581
  4. 2024HunyuanVideo: A Systematic Framework For Large Video Generative Modelscited by 1,660
  5. 2025VGGT: Visual Geometry Grounded Transformercited by 1,539
  6. 2022Diffusion Models in Vision: A Surveycited by 1,015
  7. 2025SAM 3: Segment Anything with Conceptscited by 906
  8. 2024Structured 3D Latents for Scalable and Versatile 3D Generationcited by 770
  9. 2021Resolution-robust Large Mask Inpainting with Fourier Convolutionscited by 738
  10. 2020A Survey on Visual Transformercited by 654
  11. 2023MotionCtrl: A Unified and Flexible Motion Controller for Video Generationcited by 580
  12. 2021Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldscited by 575
38 more
  1. 2019Deep Learning for 3D Point Clouds: A Surveycited by 558
  2. 2025Continuous 3D Perception Model with Persistent Statecited by 548
  3. 2023Learning Interactive Real-World Simulatorscited by 458
  4. 2024ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesiscited by 403
  5. 2025GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Controlcited by 341
  6. 2024MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervisioncited by 299
  7. 2024Diffusion Models Are Real-Time Game Enginescited by 296
  8. 2025SAM 3D: 3Dfy Anything in Imagescited by 247
  9. 2024PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generationcited by 174
  10. 2024Tora: Trajectory-oriented Diffusion Transformer for Video Generationcited by 164
  11. 2025VideoPhy-2: A Challenging Action-Centric Physical Commonsense Evaluation in Video Generationcited by 149
  12. 2024CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videoscited by 128
  13. 2024GameGen-X: Interactive Open-world Game Video Generationcited by 128
  14. 2020A Review on Deep Learning Techniques for Video Predictioncited by 114
  15. 2018MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancementcited by 112
  16. 2025CAST: Component-Aligned 3D Scene Reconstruction from an RGB Imagecited by 106
  17. 2018Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understandingcited by 101
  18. 2025CameraCtrl II: Dynamic Scene Exploration via Camera-controlled Video Diffusion Modelscited by 98
  19. 2023Generative Image Dynamicscited by 94
  20. 2024V3D: Video Diffusion Models are Effective 3D Generatorscited by 89
  21. 2025Deep Forcing: Training-Free Long Video Generation with Deep Sink and Participative Compressioncited by 85
  22. 2024GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warpingcited by 73
  23. 2023SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collectionscited by 72
  24. 2025PhysGen3D: Crafting a Miniature Interactive World from a Single Imagecited by 66
  25. 2025Amodal3R: Amodal 3D Reconstruction from Occluded 2D Imagescited by 54
  26. 2021Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlookcited by 46
  27. 2024AI Flow at the Network Edgecited by 46
  28. 2024PhysMotion: Physics-Grounded Dynamics From a Single Imagecited by 45
  29. 2025AI Flow: Perspectives, Scenarios, and Approachescited by 44
  30. 2025PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generationcited by 41
  31. 2018Physics-Based Generative Adversarial Models for Image Restoration and Beyondcited by 37
  32. 2020GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimationcited by 36
  33. 2022Deep Depth Completion from Extremely Sparse Data: A Surveycited by 33
  34. 2023Cascade-Zero123: One Image to Highly Consistent 3D with Self-Prompted Nearby Viewscited by 33
  35. 2021Multiway Non-rigid Point Cloud Registration via Learned Functional Map Synchronizationcited by 30
  36. 2023Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Modelscited by 30
  37. 2022Playable Environments: Video Manipulation in Space and Timecited by 29
  38. 2025LongVie: Multimodal-Guided Controllable Ultra-Long Video Generationcited by 22

50 most cited of 73 All 73 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025PhysWorld: From Real Videos to World Models of Deformable Objects via Physics-Aware Demonstration Synthesis
  2. 2026WorldBench: Benchmarking Physical Understanding of World Models by Isolating Physics Concepts
  3. 2026MPMWorlds: Material-Point-Method Simulations for Inferring and Extrapolating Physical Dynamics
  4. 2026World Models' Last Exam in Physics
  5. 2026Learning Explicit Physical Parameter Control and Benchmarking for Video Generation
  6. 2026GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models
  7. 2026PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models
  8. 2026STRIKE: Learning Visual State Transitions for Physical World Modeling