用统一的Transformer模型实现高精度、可扩展的布料模拟，支持多种场景。
ClothTransformer: Unified Latent-Space Transformers for Scalable Cloth Simulation
- 将布料模拟转化为潜在空间中的自回归序列建模，统一处理多种场景。
- 在所有场景下误差降低4至9倍，且计算与网格分辨率无关。
- 构建了近50万帧的高质量无穿透数据集，支持精准碰撞检测。
统一且可扩展的Transformer近期在计算机图形学诸多领域取得显著进展，如3D视觉特效、渲染过程和视频运动建模。本文探索现代Transformer技术能否解决布料模拟这一难题。为此，我们提出ClothTransformer框架，将布料模拟重新表述为学习到的潜在空间中的自回归序列建模。现有神经布料模拟器多局限于单一场景，与网格离散化强耦合，且缺乏鲁棒的碰撞处理能力。我们的方法通过三项贡献解决这些问题：（1）统一的Transformer架构，可在单个模型中处理人体驱动服装、机器人操作和自由下落碰撞等多种场景，跨所有场景误差较先前最先进方法降低约4至9倍；（2）可扩展的潜在空间形式，将任意分辨率网格压缩为固定大小的潜在标记集合，使时间动态计算独立于网格分辨率；（3）构建了一个涵盖三种场景的多样化高保真无穿透数据集，约493.4万帧，支持可微分的连续碰撞检测（CCD）模块以抑制穿透伪影。
原文摘要 · Abstract (English)
Unified and scalable Transformers have recently achieved remarkable success in modeling diverse phenomena traditionally associated with computer graphics, such as 3D visual effects, rendering processes, and motion in videos. In this work, we take a step further by investigating whether modern Transformer techniques can tackle the challenging task of cloth simulation. To this end, we present ClothTransformer, a framework that reformulates cloth simulation as autoregressive sequence modeling in a learned latent space. Existing neural cloth simulators are largely specialized to single scenarios, intrinsically coupled to the mesh discretization, and lack robust collision handling. Our approach addresses these limitations through three contributions: (1) a unified Transformer architecture that handles diverse scenarios -- body-driven garments, robotic manipulation, and free-fall collisions -- under a single model and achieves approximately $4$--$9{\times}$ lower error than prior state-of-the-art methods across all scenarios; (2) a scalable latent-space formulation that compresses arbitrary-resolution meshes into a fixed-size set of latent tokens, making temporal dynamics computation independent of mesh resolution; and (3) a diverse-scenario high-fidelity penetration-free dataset of ${\sim}$493.4k frames spanning all three settings, which enables a differentiable Continuous Collision Detection (CCD) module to suppress penetration artifacts. Project Page: https://yucrazing.github.io/clothtransformer/
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