ClothTransformer is a unified latent-space Transformer for cloth simulation that handles body-driven garments, robotic manipulation, and free-fall collisions in one model with 4-9x lower error than prior methods and mesh-resolution independence.
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4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
YASPS is a symbolic differentiable framework that uses JOIN and UNION operators to enable extensible high-performance IPC simulation on GPUs with automatic sparsity derivation and JIT kernel compilation.
HyperBones trains a reduced-space neural dynamics model with bone-driven coarse simulation and CNN-based wrinkle recovery to produce plausible garment motion at 300+ FPS using physics supervision without an external simulator.
A polynomial kernel with local support and Laplacian regularization in IMLS yields higher-fidelity meshes and textures from multi-view images than prior exponential-kernel formulations.
citing papers explorer
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ClothTransformer: Unified Latent-Space Transformers for Scalable Cloth Simulation
ClothTransformer is a unified latent-space Transformer for cloth simulation that handles body-driven garments, robotic manipulation, and free-fall collisions in one model with 4-9x lower error than prior methods and mesh-resolution independence.
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YASPS: A Symbolic Framework for Extensible, High-Performance IPC Simulation
YASPS is a symbolic differentiable framework that uses JOIN and UNION operators to enable extensible high-performance IPC simulation on GPUs with automatic sparsity derivation and JIT kernel compilation.
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HyperBones: Realtime Bone-driven Neural Garment Simulation with Hypernetwork Conditioning
HyperBones trains a reduced-space neural dynamics model with bone-driven coarse simulation and CNN-based wrinkle recovery to produce plausible garment motion at 300+ FPS using physics supervision without an external simulator.
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High-Fidelity Surface Splatting-Based 3D Reconstruction from Multi-View Images
A polynomial kernel with local support and Laplacian regularization in IMLS yields higher-fidelity meshes and textures from multi-view images than prior exponential-kernel formulations.