发表机构
Johns Hopkins University（约翰霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FracGen是一种断裂感知视频生成模型，利用物理信息损失和联合预测物理场，从单张图像生成可控且物理合理的断裂动态，优于现有基线。
AI 中文摘要
我们介绍了FracGen，一种断裂感知的视频生成模型，它能够根据完整物体的单张图像，在物理信号的条件下，生成合理且可控的断裂动态。为了训练FracGen，我们构建了FracSim，一个断裂感知的模拟框架，该框架通过连续损伤模型增强了物质点法（MPM）模拟，从而在标准渲染之外无需额外成本即可生成配对的断裂视频和密集的、像素对齐的物理场。FracGen以两种方式利用这些映射：它被训练为与RGB视频一起联合预测这些映射，鼓励模型捕捉物理状态而非表面外观；并且它通过物理信息损失进行监督，以促进预测映射之间的一致性。因此，FracGen能够捕捉不同材料特有的断裂行为，而无需昂贵的测试时模拟或逐场景调整，同时提供对物体撕裂位置、裂纹扩展速度以及失效前变形程度的精细控制。我们进一步引入了一个基准，用于评估生成的断裂视频的物理合理性，并通过大量实验表明，FracGen在物理和视觉保真度方面均优于现有的视频生成基线。结果最好在我们的项目网站上查看：此https URL。
英文摘要
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 them alongside RGB video, encouraging the model to capture physical state rather than surface appearance; and 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 expensive test-time simulation or per-scene tuning, while offering fine-grained control over where an object tears, how fast the crack propagates, and how much deformation precedes failure. We further introduce a benchmark for evaluating the physical plausibility of generated fracture video, and show through extensive experiments that FracGen outperforms existing video generation baselines in both physical and visual fidelity. Results are best viewed in our project website: https://fracgen.github.io/.
