[论文] PhysiFormer: Learning to Simulate Mechanics in World Space

论文概要

研究领域: CV
作者: Yiming Chen, Yushi Lan, Andrea Vedaldi
发布时间: 2026-06-27
arXiv: 2606.27364

中文摘要

We present PhysiFormer, a diffusion transformer for physically-plausible 3D object motion. Unlike video world models that operate in view-dependent pixel space, PhysiFormer represents objects as 3D meshes expressed in world coordinates. Given the initial vertex positions and velocities, as well as o...

原文摘要

We present PhysiFormer, a diffusion transformer for physically-plausible 3D object motion. Unlike video world models that operate in view-dependent pixel space, PhysiFormer represents objects as 3D meshes expressed in world coordinates. Given the initial vertex positions and velocities, as well as object material type, rigid or elastic, the model samples future vertex trajectories. While related neural physics approaches build on ad-hoc latent spaces or explicitly enforce rigidity and causality,...

自动采集于 2026-06-27

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