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.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
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
-
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.
-
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.
- HyperBones: Realtime Bone-driven Neural Garment Simulation with Hypernetwork Conditioning