mlsanityData to 14 Oct 2026

Phase-aware video generation for physics-grounded dynamics and interactions

arXiv 2610.11791 ↗First posted 8 Oct 2026cs.CVcited by 0

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Abstract (arXiv)

Generating physically plausible videos for solid-gas dynamics is challenging as different phases exhibit distinct dynamics yet remain coupled through physical interactions. We present PAVG, a Phase-Aware Video Generator for solid-gas dynamics and interactions. It employs a dual-branch architecture to explicitly model the distinct dynamics of solids and gases, while spatiotemporal cross-attention captures their physical interactions. This design enables PAVG to preserve phasespecific motion characteristics while producing physically consistent responses across phases. To facilitate this task, we further construct a simulation corpus comprising over 700K physical trajectories across diverse solid, gas, and solid-gas interaction scenarios. Extensive evaluations demonstrate that our PAVG produces videos with improved motion adherence, physical plausibility, and visual quality compared with existing approaches.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 102 references that are in the map. First posted on arXiv 8 Oct 2026.

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24 of 102 references.

References · 24

  1. 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,708
  2. 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,581
  3. 2024CogVideoX: Text-to-Video Diffusion Models with An Expert Transformercited by 2,261
  4. 2024SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusioncited by 364
  5. 2023DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectorycited by 264
  6. 2024VideoPhy: Evaluating Physical Commonsense for Video Generationcited by 228
  7. 2024PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generationcited by 191
  8. 2025Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Controlcited by 185
  9. 2025HunyuanVideo 1.5 Technical Reportcited by 160
  10. 2024DragAnything: Motion Control for Anything using Entity Representationcited by 159
  11. 2025VideoPhy-2: A Challenging Action-Centric Physical Commonsense Evaluation in Video Generationcited by 149
  12. 20243DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video Generationcited by 71
12 more
  1. 2025PhysGen3D: Crafting a Miniature Interactive World from a Single Imagecited by 66
  2. 2024PhysMotion: Physics-Grounded Dynamics From a Single Imagecited by 45
  3. 2025ATI: Any Trajectory Instruction for Controllable Video Generationcited by 43
  4. 2024ObjCtrl-2.5D: Training-free Object Control with Camera Posescited by 18
  5. 2023Learning Lagrangian Fluid Mechanics with E(3)-Equivariant Graph Neural Networkscited by 12
  6. 2026NEWTON: Agentic Planning for Physically Grounded Video Generationcited by 12
  7. 2025Physics-Grounded Motion Forecasting via Equation Discovery for Trajectory-Guided Image-to-Video Generationcited by 4
  8. 2026Neural Latent Arbitrary Lagrangian-Eulerian Grids for Fluid-Solid Interactioncited by 3
  9. 2026PhysStream: Streaming Physics-Grounded Video Generation with Structured Scene Memory and Fine-Grained Motion Controlcited by 2
  10. 2026Streaming Video Generation with Streaming Force Controlcited by 1
  11. 2026Physics-Grounded Fluid Video Generation with a Simulation Dataset and Dual-Stream Optical-Flow Supervisioncited by 1
  12. 2026Physics-Aware Video Generation via Agentic Planning and Graph-Guided Optimizationcited by 1

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