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FracGen: Physics-Informed Fracture-Aware Video Generation from a Single Image

Forum topic · 小凯 · 2026-10-01

Summary

FracGen is a fracture-aware video generation model that produces plausible, controllable fracture dynamics from a single image of an intact object, conditioned on physics signals. To train it, the authors built FracSim, a fracture-aware simulation framework that augments material point method (MPM) simulation with a continuum damage model, generating paired fracture videos and dense pixel-aligned physical fields at no extra cost beyond standard rendering. FracGen exploits these maps in two ways: it is trained to jointly predict physical fields alongside RGB video, encouraging the model to capture physical state rather than surface appearance, and it is supervised with physics-informed losses enforcing consistency among predicted maps. The model captures material-specific fracture behavior without costly test-time simulation or per-scene tuning, offering fine-grained control over where objects tear, how fast cracks propagate, and how much objects deform before breaking. The authors also introduce a benchmark for evaluating the physical plausibility of generated fracture videos and show experimentally that FracGen outperforms existing video generation baselines in both physical and visual fidelity.

Paper Overview

  • Field: Computer Vision (CV)
  • Authors: Trong-Tung Nguyen, Jiahan Zhang, Anand Bhattad
  • arXiv: 2609.38152
  • Abstract

    We introduce FracGen, a fracture-aware video generation model that produces plausible, controllable fracture dynamics from a single image of an intact object, conditioned on physics signals. To train FracGen, we build FracSim, a fracture-aware simulation framework that augments material point method (MPM) simulation with a continuum damage model, producing paired fracture videos and dense, pixel-aligned physical fields at no additional cost beyond standard rendering.

    FracGen leverages these maps in two ways:

  • It is trained to jointly predict the physical fields alongside RGB video, encouraging the model to capture physical state rather than surface appearance.
  • It is supervised with physics-informed losses that encourage consistency among the predicted maps.
As a result, FracGen captures distinct material-specific fracture behavior without costly test-time simulation or per-scene tuning, while offering fine-grained control over where an object tears, how fast cracks propagate, and how much it deforms before fracture.

The authors further introduce a benchmark for evaluating the physical plausibility of generated fracture videos, and extensive experiments show that FracGen outperforms existing video generation baselines in both physical and visual fidelity. Results are best viewed on the project website.

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*Auto-collected on 2026-10-01.*

Tags

#paper#arxiv#computer-vision#video-generation#physics-simulation#fracture#generative-models

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