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.
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.
arXiv:2609.36024v1 Announce Type: new Abstract: Reconstructing simulation-ready 3D scenes from real-world observations enables robotics, gaming, and immersive applications, yet existing methods large...
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.
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:2608.31113v1 Announce Type: new Abstract: We introduce BLARM, a feed-forward method for video-driven 3D mesh animation. Given a monocular video and a static object mesh, BLARM predicts a tempor...
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.
The paper presents a method for learning compact, expressive parameterisations for Bayesian shape optimisation by applying principal component analysis to deformations between existing designs. This approach yields a linear, interpretable search space where the number of components balances expressivity and dimensionality. Experiments on aerofoils, wings, and radio‑frequency cavities demonstrate improved sample efficiency and exploration beyond hand‑crafted baselines.
CANTO is a transformer neural operator that directly maps continuous CAD geometry—represented as sequences of NURBS patches—to physical fields without requiring meshing. It tokenizes control points, knot vectors, and weights to predict continuous surface and volume fields at arbitrary query locations. Evaluated on automotive and aircraft aerodynamics benchmarks, CANTO achieves state‑of‑the‑art accuracy, notably a 19.8% reduction in surface‑pressure error on HiLiftAeroML, and enables gradient‑based inverse design, finding lower‑drag designs on AhmedML.
arXiv:2609.36806v1 Announce Type: new Abstract: Modern engineering systems, from automobiles to aircraft, are designed by using precise, continuous parametric computer-aided design (CAD) models. Eval...
KATOsuper is an objective‑agnostic framework that accelerates neural topology optimization by coupling neural‑reparameterized TO with a Sensitivity‑Consistent Fourier Neural Operator (SC‑FNO). It uses a forward_split architecture to ensure that sensitivities derived via automatic differentiation remain consistent with predicted objectives, enabling stable optimization. The method demonstrates significant deployment‑time speedups (15–110×) over MATLAB baselines while preserving optimality across 2D and 3D benchmark problems, including compliance and stress minimization, and supports zero‑shot extrapolation to higher resolutions.
arXiv:2611.02902v2 Announce Type: replace Abstract: Humanoid rigging for modern characters with complex garments remains challenging due to the need for dense auxiliary skeletons and precise skinning...
arXiv:2610.08672v1 Announce Type: new Abstract: Mesh-agnostic facial animation retargeting transfers expressions across meshes with different structures, but preserving facial motion without surface...