Summary
HiPhy (Hierarchical Physical Alignment) is a reinforcement learning framework that grounds video generation models in physical laws. While modern video generators achieve striking visual fidelity, they frequently violate physics, especially in realistic scenes where multiple principles must act simultaneously—such as a balloon rising under buoyancy while steam flows upward from a pot. HiPhy addresses this with a dual-level objective: a local level that enforces the temporal dynamics of individual physical principles, and a global level that ensures physics and semantic coherence across the whole scene. To support multi-principle generation, the authors built a dataset of 50,000 prompts and introduced MultiPhyBench, a benchmark covering diverse co-occurring physical events. Experiments show HiPhy substantially outperforms prior methods and baselines, improving physical common sense and semantic alignment across multiple benchmarks, with the largest gains precisely in the multi-principle scenarios where competing approaches degrade most. Paper: arXiv 2610.02197, by Kazimi, Borse, Hayat, Porikli, and Yanardag.
Overview
Field: Computer Vision
Authors: Tahira Kazimi, Shubhankar Borse, Munawar Hayat, Fatih Porikli, Pinar Yanardag
Published: 2026-10-01
arXiv: 2610.02197
Summary
Video generation models have achieved remarkable visual fidelity and have strong potential to become general-purpose world simulators. Despite this progress, they still fail to generate videos which adhere to laws of physics. The problem becomes even more apparent in realistic settings where multiple physical principles must work together within the same video; for example, "a balloon floating upward while steam rises from a pot" requires buoyancy and fluid dynamics to unfold coherently and simultaneously. Yet existing methods largely ignore multi-principle interactions, focusing on a single principle per video.
HiPhy (Hierarchical Physical Alignment) is a reinforcement learning framework that grounds video generation in physical laws through a dual-level objective:
- Local level: enforces the temporal dynamics of individual physical principles.
- Global level: ensures physical and semantic coherence of the entire scene.
To support multi-principle generation, the authors constructed a dataset of 50,000 prompts and introduced a prompt-based benchmark,
MultiPhyBench, covering diverse co-occurring physical events.
Results
Experiments show that HiPhy significantly outperforms previous methods and baselines, substantially improving physical common sense and semantic alignment across multiple benchmarks. The largest gains occur in multi-principle concurrent scenarios, where competing methods degrade the most.
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