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ARAPDiffusion: ARAP Regularization for Diffusion-Based Deformable Shape Space Learning

arXiv:2606.06887v1 Announce Type: new Abstract: This paper introduces ARAPDiffusion, a latent diffusion model to learn the underlying continuous shape space of a deformation shape collection. The key innovation is in injecting the as-rigid-as-possible (ARAP) deformation model as regularization losses into latent diffusion (LD), releasing the requirement of having abundant 3D training data for learning generative models. In contrast to the standard LD, we show how the ARAP model can be used...

arXiv CS 2d ago

Locality-Aware Automatic Differentiation on the GPU for Mesh-Based Computations

arXiv:2509.00406v3 Announce Type: replace Abstract: We present a GPU-based system for automatic differentiation (AD) of functions defined on triangle meshes, designed to exploit the locality and sparsity in mesh-based computation. Our system evaluates derivatives using per-element forward-mode AD, confining all computation to registers and shared memory and assembling global gradients, sparse Jacobians, and sparse Hessians directly on the GPU. By avoiding global computation graphs,...

arXiv CS 5d ago