GraphCAD introduces a geometry-aware generative framework for Computer-Aided Design (CAD) that models CAD construction using an Edge-Adjacency Graph (EAG), a novel graph-based representation. The method employs a hybrid graph diffusion process to jointly capture discrete topological connectivity and continuous spatial geometry, and incorporates a geometry-aware denoising mechanism that maps node attributes to SE(3)-equivariant encoded point sets. Experiments on the newly created EAGBench dataset demonstrate that GraphCAD can generate complex CAD models with more than 50 faces, achieving state‑of‑the‑art geometric fidelity and distributional alignment compared to sequence‑based baselines.
By Wenhao Zheng, Xianggen Liu, Li Yue, Jiancheng Shi, Jiancheng Lv
Generating high-quality triangle meshes is essential for film, gaming, and interactive 3D applications. Mainstream methods rely on mesh serialization and autoregressive processes, which stuggles in effective inference and is sensitive to error accumulation.
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
MeSS (Mesh-based Scene Synthesis) is a method that generates high‑quality, style‑consistent outdoor scenes using city mesh models as geometric priors. The pipeline first creates geometrically consistent sparse views with Cascaded Outpainting ControlNets, then densifies intermediate views via AGInpaint, and finally removes visual inconsistencies with GCAlign. Simultaneously, a 3D Gaussian Splatting scene is reconstructed from the mesh, enabling diverse rendering styles after synthesis.
By Xuyang Chen, Zhijun Zhai, Kaixuan Zhou, Zengmao Wang, Jianan He, Dong Wang, Yanfeng Zhang, Mingwei Sun, Xuqin Wang, R\"udiger Westermann, Tao Wu, Konrad Schindler, Liqiu Meng
arXiv:2609.08153v1 Announce Type: new
Abstract: Generative diffusion models have emerged as a class of powerful techniques for various imaging applications, including but not limited to synthesis, re...
By Nian Wu, Nivetha Jayakumar, Jiarui Xing, Miaomiao Zhang
arXiv:2603. 08825v2 Announce Type: replace-cross Abstract: Discrete graph generation has emerged as a powerful paradigm for modeling graph-structured data, yet state of the art models often rely on Graph Transformers or higher order architectures.
By Jay Revolinsky, Harry Shomer, Jiliang Tang
arXiv:2609.26039v1 Announce Type: cross
Abstract: Point cloud generation has emerged as a crucial task for accurately capturing and reproducing the complexity of the physical world. However, existing...
By Ilias Mitsouras, Nikolaos Chaidos, Giorgos Stamou, Athanasios Voulodimos
arXiv:2404.17569v4 Announce Type: replace
Abstract: This paper aims to generate materials for 3D meshes from text descriptions. Unlike existing methods that synthesize texture maps, we propose to gen...
By Shangzhan Zhang, Sida Peng, Tao Xu, Yuanbo Yang, Tianrun Chen, Nan Xue, Yujun Shen, Hujun Bao, Ruizhen Hu, Xiaowei Zhou
RecGen3D is a framework that merges feed‑forward reconstruction and diffusion‑based generation to address the trade‑off between reconstruction fidelity and generative plausibility in sparse‑view 3D modeling. By aligning both models in a shared canonical space and using decoupled cooperative learning, the system stabilizes training and allows the reconstruction module to supply canonical geometric anchors while the diffusion generator refines and completes the structure. Experiments show that RecGen3D outperforms existing methods in producing complete and consistent 3D models from sparse observations.
By Zhisheng Huang, Jiahao Chen, Cheng Lin, Chenyu Hu, Hanzhuo Huang, Zhengming Yu, Mengfei Li, Yuheng Liu, Zekai Gu, Zibo Zhao, Yuan Liu, Xin Li, Wenping Wang
arXiv:2512. 16275v2 Announce Type: replace-cross Abstract: Automated floor plan generation lies at the intersection of combinatorial search, geometric constraint satisfaction, and functional design requirements -- a confluence that has historically resisted a unified computational treatment.
By Mohamed Abouagour, Eleftherios Garyfallidis
Align3D is an image‑conditioned diffusion model that generates 3D indoor layouts faithful to a single input photograph while remaining physically plausible beyond the observed view. It infers a confidence‑calibrated scene graph from the photo and trains a set‑diffusion layout denoiser with image‑structure co‑alignment, balancing image and scene‑graph alignment through confidence‑weighted fusion. The model employs observability‑aware fidelity‑preserving guidance to reconcile conflicting scene‑quality gradients, using an object‑level confidence mask so under‑observed degrees of freedom can incorporate physical priors without disrupting the image‑graph match. On the 3D‑FRONT dataset, Align3D outperforms recent baselines in image‑alignment and scene‑quality metrics, and can map unseen photographs to layouts in a single feed‑forward pass.
By Chenliang Zhou, Yifan Wang, Andrzej Banburski-Fahey
arXiv:2609.37139v1 Announce Type: new
Abstract: 3D content generation technology has significantly advanced the work of designers, as well as the 3D printing and gaming industries. However, it remain...
By Xianze Fang, Qiyuan Feng, Dongfang Sun, Yan Zhang, Xiuchao Wu, Jingnan Gao, Jiangjing Lyu, Chengfei Lyu, Gang Yu