arXiv Machine Learning By Aviraj Newatia, Shengan Yang, Hantao Zhong, Rika Antonova

Structural Affinity: An Algorithm for Coherent Cage-Based Shape Design

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The paper introduces Structural Affinity, an explicit regularisation technique for cage-based deformation (CBD) that couples an inner control cage to a coarser outer cage via a precomputed affinity matrix derived from mean‑value coordinates. This method directly influences cage‑handle motion, reducing self‑intersections, mesh distortion, and the need for hand‑crafted loss terms. Experiments on robotic morphology co‑design, neural deformation transfer, and aerodynamic wing optimization demonstrate improved surface smoothness, coherent cage distortion, and reduced surface roughness.

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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
Hugging Face Trending Papers
Aug 7

Flow-Corrected Shape Optimization: Taming Manifold Drift in High-Dimensional 3D Models

Optimizing 3D shapes within the latent spaces of deep generative models is fundamental to computer assisted engineering, yet remains prone to a critical failure mode we term manifold drift: the tendency of gradient-based optimization to move latent vectors away from the manifold of valid shapes. This problem is exacerbated in state-of-the-art 3D shape generative models that operate in increasingly high-dimensional latent spaces where valid shapes occupy a vanishingly small fraction of the full space.

Hugging Face Trending Papers
Jul 30

ViP-Rig: Visual-Prompted Controllable Rigging

Rigging is inherently task-dependent because the same mesh may require different skeletons and deformation behaviors across animation tasks. In practice, artists often inspect an initial rig and repeatedly edit its skeletal structure and deformation behavior to meet specific animation requirements.

arXiv AI
Sep 24

Learning Stiffness Dependent Fluid Structure Dynamics from Coarse Flow Representations

The paper presents a data‑driven framework that predicts long‑term fluid–structure interaction dynamics for a flexible plate undergoing flow‑induced vibration. It uses a stiffness‑conditioned neural evolution operator that jointly models the Eulerian flow field and the Lagrangian structural state, employing a hybrid CNN‑Transformer architecture with bidirectional cross‑attention. The operator accurately captures three stiffness‑dependent response regimes, preserves key flow and structural features over 1000‑step rollouts, and can interpolate to unseen stiffness values, while a differentiable aerodynamic‑force module based on derivative‑moment transformation enables accurate lift and drag reconstruction.

By Chun-Jun Pu, Li-Wei Chen, Hai-Bo Huang