5 papers
Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors
Erkan Turan, Gaspard Abel, Maysam Behmanesh +2
Graph Neural Networks (GNNs) learn node representations through iterative network-based message-passing. While powerful, deep GNNs suffer from oversmoothing, where node features co…
Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization
Erkan Turan, Aristotelis Siozopoulos, Louis Martinez +3
Continuous Normalizing Flows (CNFs) enable elegant generative modeling but remain bottlenecked by their iterative nature requiring costly sampling and lacking interpretability of t…
Beyond Prompts: Unconditional 3D Inversion for Out-of-Distribution Shapes
Victoria Yue Chen, Emery Pierson, Léopold Maillard +1
Text-driven inversion of generative models is a core paradigm for manipulating 2D or 3D content, unlocking numerous applications such as text-based editing, style transfer, or inve…
PatchAlign3D: Local Feature Alignment for Dense 3D Shape Understanding
Souhail Hadgi, Bingchen Gong, Ramana Sundararaman +4
Current foundation models for 3D shapes excel at global tasks (retrieval, classification) but transfer poorly to local part-level reasoning. Recent approaches leverage vision and l…
DiffuMatch: Category-Agnostic Spectral Diffusion Priors for Robust Non-rigid Shape Matching
Emery Pierson, Lei Li, Angela Dai +1
Deep functional maps have recently emerged as a powerful tool for solving non-rigid shape correspondence tasks. Methods that use this approach combine the power and flexibility of…