KaiNinja extends the native 3D generator TRELLIS.2 to produce part-level meshes by introducing a dual‑volume representation that overcomes the single‑sheet limitation of the O‑Voxel grid. It maintains TRELLIS.2’s speed and quality while eliminating the need for external segmentation, and is trained on diverse data including CAD models and assets created by an LLM‑driven agent. The method improves whole‑object fidelity and outperforms other part‑generation pipelines, reducing Chamfer distance by 40% and increasing strict part F‑score by 16%.
By Ruihan Yu, Lian Fu, Muyao Niu, Zheng-hui Huang, Yu-Ju Tsai, Sho Kuno, Fengbo Lan, Yonghao Yu, Erwin Wu, Ming-Hsuan Yang, Kaipeng Zhang, Zhixiang Wang
Part-aware 3D generation aims to create digital assets that are coherent as complete objects while exposing structural parts for editing, material assignment, animation, and reuse. Existing methods impose this structure outside the native generation loop: segmentation-based methods partition an already generated shape, while additive methods synthesize parts from predefined layouts, boxes, or tokens and then reconcile them into a whole.
arXiv:2609.15639v1 Announce Type: new
Abstract: Part-level control is essential for modern 3D asset creation, where objects are frequently edited, reused, animated, or fabricated through their indivi...
By Jiahao Chang, Dong Du, Wanhu Sun, Yujian Zheng, Chuanyu Pan, Bowen Zhao, Chongjie Ye, Yuanming Hu, Xiaoguang Han
STRUCTPARTS is a new framework that reuses holistic 3D priors to generate part‑decomposed 3D assets. It introduces a dual‑frame coordinate interleaving mechanism, interpreting each shape latent under both part‑local and object‑global reference frames and processing them with a global‑local inter‑leaved transformer. Using a segmented mesh scaffold as structural input, the method achieves detailed intra‑part geometry and coherent inter‑part structure with lightweight finetuning, outperforming prior part‑decomposed models on PartObjaverse‑Tiny while enabling part‑mesh refinement and editing.
By Yiqun Zhao, Binbin Huang, Haobin Duan, Zibo Zhao, Shenghua Gao
arXiv:2609.38180v1 Announce Type: new
Abstract: Existing 3D part decomposition methods do not necessarily partition the original shape into non-overlapping parts that collectively cover the entire sh...
By Hao-Tang Tsui, Yu-Rou Tuan, Xiaoxuan Ma, Nicolas Ugrinovic, Takaaki Shiratori, Kris Kitani
Part-level 3D generation has recently attracted increasing attention for producing structured and editable 3D assets. However, existing methods typically decompose objects according to functional semantics rather than the editable material boundaries (e.
arXiv:2610.02201v1 Announce Type: cross
Abstract: High-resolution 3D generation increasingly relies on voxel latents and multi-stage pipelines that first predict active structure and then synthesize...
By Tianjiao Yu, Xinzhuo Li, Yifan Shen, Ying Shen, Kiet A. Nguyen, Adheesh Sunil Juvekar, Ismini Lourentzou
FlexPart is a new framework that enables text‑controlled, semantics‑aware 3D mesh segmentation at any desired granularity. It introduces a holistic control prompt that combines part names with location and shape clauses, and uses a Vision‑Language Model pipeline to generate a large multi‑granularity dataset. The model produces a view‑independent part‑identity field in 3D space, and an inference‑time VLM planner can auto‑complete descriptive clauses from 2D renderings, allowing users to specify parts with ordinary words. FlexPart outperforms strong baselines by up to 22% in mIoU on its benchmark and improves selective segmentation by 35% over click‑driven methods.
By Yiqun Zhao, Jiachen Xu, Yihao Zhi, Yunfei Zhao, Zibo Zhao, Xintong Han, Chunchao Guo, Shenghua Gao
NOFACE3D is a training‑free framework that refines fine‑scale, detail‑critical regions in 3D assets generated from a single image. It extracts a selected submesh, scales it to the generator’s input volume, and re‑encodes it into a full‑resolution latent grid, refining the region with flow‑based sampling guided by a spatially varying noise schedule. The refined region is then reintegrated, preserving surrounding geometry and appearance, and the method is evaluated on PAniC‑3D, TexVerse, and a new BigDetail benchmark, showing sharper geometry and finer texture detail.
whyItMatters: The approach improves the fidelity of small, high‑curvature structures in image‑to‑3D models without requiring additional training, addressing a key limitation in current generators.
By James Rose, Jingwen Liang, Wenchao Ma, Oren Jacob, Haomiao Jiang
arXiv:2607. 05568v1 Announce Type: cross Abstract: Representing 3D shapes as compact sets of geometric primitives is fundamental to robotics, simulation, and scene understanding.
By Gregor Kobsik, Tim Elsner, Leif Kobbelt
arXiv:2609.36918v1 Announce Type: new
Abstract: Part-level 3D assets are essential for editing, reassembly, and interaction, yet recovering such structure from a single image remains challenging due...
By Jiantao Lin, Meixi Chen, Yingjie Xu, Chenbo Fu, Leyi Wu, Hao Chen, Yinchuan Li, Ying-Cong Chen
arXiv:2604.19609v2 Announce Type: replace
Abstract: Transformers have become a common foundation across deep learning, yet 3D scene understanding still relies on specialized backbones with strong dom...
By Kadir Yilmaz, Adrian Kruse, Tristan H\"ofer, Daan de Geus, Bastian Leibe