SeamFlow is a new generative framework for 3D surface cutting and UV unwrapping that reformulates the discrete mesh‑cutting problem as continuous flow matching in a high‑dimensional edge‑probability space. By learning a deterministic mapping from a Gaussian prior to a target seam‑probability distribution and using an evolution network to couple local topological tokens with global shape priors, SeamFlow guides smooth probability flow through ODE solving. Compared with existing autoregressive generative methods, SeamFlow improves topology awareness, eliminates 3D spatial projection errors and artificial sequential‑order bias, and achieves exceptional semantic coherence with remarkably low parameterization distortion.
By Yuming Zhao, Zangyueyang Xian, Qijian Zhang, Rendong Liang, Qin Jia, Ying He, Junhui Hou
The paper introduces ToW3D, a method for precise and consistent control over 3D generative adversarial networks (GANs) using a Tug-of-War approach between shape deformation and appearance consistency. It addresses the challenge that 3D generators often lack generalization, leading to drastic global appearance changes when editing local mesh areas. ToW3D employs a two-step optimization—drag locally and shove globally—along with a structure adaptation module and a semantic preservation module, achieving superior appearance consistency and fidelity compared to prior methods, especially under large deformations.
By Haixu Song, Fangfu Liu, Chenyu Zhang, Yueqi Duan
arXiv:2606. 13400v1 Announce Type: cross Abstract: While flow-based generative models have demonstrated strong performance across a wide range of domains, deploying them in safety-critical physical systems remains challenging due to strict constraint requirements.
By Jianming Ma, Qiyue Yang, Yang Zhang, Liyun Yan, Zhanxiang Cao, Yazhou Zhang, Yue Gao
arXiv:2609.40287v1 Announce Type: new
Abstract: Physics-constrained generative models aim to generate physical fields that match a target distribution and satisfy prescribed constraints. However, enf...
By Zhangyong Liang, Haibin Ling
Geometric foundation models, such as the Visual Geometry Grounded Transformer (VGGT), provide strong 3D priors from unposed images. However, such models operate purely in a feed-forward, deterministic regime, \ie~they cannot generate plausible geometry beyond what the input views directly support.
TokenMatch is a transformer-based model that estimates 3D shape correspondences by adaptively tokenising meshes into curvature-guided patches. Trained only on the BeCoS partial-to-partial dataset, it generalises to full-shape matching without retraining, using self‑ and cross‑attention to learn patch‑ and point‑level relations. Evaluated on CP2P, PSMAL, BeCoS, FAUST, SCAPE, and SHREC'19, TokenMatch consistently outperforms existing methods in mean geodesic error and intersection‑over‑union while achieving sub‑second inference speeds.
By Adeela Islam, Zorah L\"ahner, Vittorio Murino, Vladislav Golyanik
arXiv:2607. 14652v1 Announce Type: new Abstract: Topology optimisation (TO) often requires repeated finite element analysis and sensitivity-based material updates, which can be costly when multiple candidate designs are needed under varying physical and design conditions.
By Shusheng Xiao, Jinshuai Bai, Hyogu Jeong, Yunfei Xi, Yilin Gui, YuanTong Gu
arXiv:2512.09201v2 Announce Type: replace-cross
Abstract: We introduce a framework for converting 3D shapes into compact and editable assemblies of analytic primitives, directly addressing the persis...
By Aditya Ganeshan, Matheus Gadelha, Thibault Groueix, Zhiqin Chen, Siddhartha Chaudhuri, Vladimir G. Kim, Wang Yifan, Daniel Ritchie
The paper introduces a novel compression framework for image-to-shape Diffusion Transformers (DiTs) that significantly reduces model size while preserving geometric fidelity. By exploiting the non-uniform importance of 3D DiT layers, the authors combine structured pruning, adaptive quantization, and targeted fine‑tuning into a vitality‑guided approach. The method achieves up to a 66% reduction in model size across state‑of‑the‑art image‑to‑3D models without compromising synthesis quality, offering a plug‑and‑play solution for efficient 3D shape generation.
By Jaeah Lee, Hyunjin Kim, Jaewoong Cho, Gihyun Kwon
arXiv:2609.07137v1 Announce Type: cross
Abstract: Recent image-to-3D generation models built on flow-matching diffusion Transformers (DiT) can produce high-fidelity meshes, yet their post-training st...
By Zhiwei Ning, Zhen Zhou, Puhua Jiang, Xintong Han, Gengming Zhang, Jie Yang, Zhonglong Zheng, Yuanjie Zheng, Wei Liu, Chunchao Guo
Implicit Neural Representations (INRs) have become the standard for continuous 2D shape modeling, but they suffer from black-box uneditability, vulnerability to noise, and high parameter counts that severely hinder deployment on edge devices. We introduce Fluid-SDF, a highly compressed, differentiable Constructive Solid Geometry (CSG) framework that models shapes using explicit geometric primitives blended via a smooth minimum function.
arXiv:2610.01233v1 Announce Type: new
Abstract: Flow matching is central to 3D generation, yet in practice its reinforcement learning (RL) methods are largely adapted from 2D visual generation. Repre...
By Zhen Zhou, Zhiwei Ning, Puhua Jiang, Sheng Zhang, Yifei Tang, Jie Yang, Xintong Han, Wei Liu, Chunchao Guo