arXiv Machine Learning By Ana Dodik, Vincent Sitzmann, Justin Solomon, Oded Stein

Robust Biharmonic Skinning Using Geometric Fields

Read the original on arXiv Machine Learning →

arXiv:2406. 00238v3 Announce Type: replace-cross Abstract: Bounded bihramonic weights are a popular tool used to rig and deform characters for animation, to compute reduced-order simulations, and to define feature descriptors for geometry processing.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI
Jul 28

DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces

arXiv:2603. 05607v2 Announce Type: replace-cross Abstract: Computer-Aided Design (CAD) relies on structured and editable geometric representations, yet existing generative methods are constrained by small annotated datasets with explicit design histories or boundary representation (BRep) labels.

By Mohammad Sadil Khan, Muhammad Usama, Rolandos Alexandros Potamias, Didier Stricker, Muhammad Zeshan Afzal, Jiankang Deng, Ismail Elezi
arXiv Machine Learning
Jul 28

Meshless Domain Randomization via Explicit Parameter Perturbation of 3D Gaussian Splatting

arXiv:2607. 22890v1 Announce Type: cross Abstract: Domain Randomization (DR) is a standard technique for closing the Sim-to-Real gap, yet traditional DR pipelines rely on classical computer graphics rendering driven by polygon meshes.

By Felipe Nunes Carbone de Carvalho, Joyce de Morais Souza, Alan de Aguiar, Charles Morphy D. Santos, Jo\~ao Paulo Gois
arXiv Machine Learning
Jun 3

CADFit: Precise Mesh-to-CAD Program Generation with Hybrid Optimization

arXiv:2605. 01171v2 Announce Type: replace-cross Abstract: Despite recent progress, recovering parametric CAD construction sequences from geometric input, such as meshes or point clouds, is a key challenge for design and manufacturing, as existing CAD reconstruction and generation methods are largely restricted to difficult-to-edit formats like meshes or Breps or editable simple sketch-and-extrude pipelines and low-complexity datasets.

By Ghadi Nehme, Eamon Whalen, Faez Ahmed
arXiv Machine Learning
Aug 12

HyperShape: Hyperelasticity Across Diverse Shapes

arXiv:2608. 09938v1 Announce Type: cross Abstract: Hyperelastic deformations are highly sensitive to domain geometry and boundary conditions, making generalization across both a critical capability for neural operators applied to these problems.

By Leo Widmer, Sidaty El Hadramy, St\'ephane Cotin, Philippe Claude Cattin