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

arXiv: https://arxiv.org/abs/2610.11791 · HTML: https://mlsanity.com/paper/2610.11791/

## 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.

## Builds on (most cited first, 24 of 24)

- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion](https://mlsanity.com/paper/2403.12008/) (2024)
- [DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory](https://mlsanity.com/paper/2308.08089/) (2023)
- [VideoPhy: Evaluating Physical Commonsense for Video Generation](https://mlsanity.com/paper/2406.03520/) (2024)
- [PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generation](https://mlsanity.com/paper/2404.13026/) (2024)
- [Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Control](https://mlsanity.com/paper/2501.03847/) (2025)
- [HunyuanVideo 1.5 Technical Report](https://mlsanity.com/paper/2511.18870/) (2025)
- [DragAnything: Motion Control for Anything using Entity Representation](https://mlsanity.com/paper/2403.07420/) (2024)
- [VideoPhy-2: A Challenging Action-Centric Physical Commonsense Evaluation in Video Generation](https://mlsanity.com/paper/2503.06800/) (2025)
- [3DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video Generation](https://mlsanity.com/paper/2412.07759/) (2024)
- [PhysGen3D: Crafting a Miniature Interactive World from a Single Image](https://mlsanity.com/paper/2503.20746/) (2025)
- [PhysMotion: Physics-Grounded Dynamics From a Single Image](https://mlsanity.com/paper/2411.17189/) (2024)
- [ATI: Any Trajectory Instruction for Controllable Video Generation](https://mlsanity.com/paper/2505.22944/) (2025)
- [ObjCtrl-2.5D: Training-free Object Control with Camera Poses](https://mlsanity.com/paper/2412.07721/) (2024)
- [Learning Lagrangian Fluid Mechanics with E(3)-Equivariant Graph Neural Networks](https://mlsanity.com/paper/2305.15603/) (2023)
- [NEWTON: Agentic Planning for Physically Grounded Video Generation](https://mlsanity.com/paper/2605.18396/) (2026)
- [Physics-Grounded Motion Forecasting via Equation Discovery for Trajectory-Guided Image-to-Video Generation](https://mlsanity.com/paper/2507.06830/) (2025)
- [Neural Latent Arbitrary Lagrangian-Eulerian Grids for Fluid-Solid Interaction](https://mlsanity.com/paper/2603.00792/) (2026)
- [PhysStream: Streaming Physics-Grounded Video Generation with Structured Scene Memory and Fine-Grained Motion Control](https://mlsanity.com/paper/2609.17521/) (2026)
- [Streaming Video Generation with Streaming Force Control](https://mlsanity.com/paper/2606.07508/) (2026)
- [Physics-Grounded Fluid Video Generation with a Simulation Dataset and Dual-Stream Optical-Flow Supervision](https://mlsanity.com/paper/2607.25321/) (2026)
- [Physics-Aware Video Generation via Agentic Planning and Graph-Guided Optimization](https://mlsanity.com/paper/2609.13006/) (2026)

## 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.