arXiv AI
Kaininja: Extending Native 3D Generators to the Part Level
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KaiNinja: Extending Native 3D Generators to the Part Level
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%.
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SCULPT: Subtractive Composition for 3D Part Generation
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SAM3D-Part: Interactive Part Selection and Generation from 3D Objects
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...
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PartMat: Material-Aware 3D Part Decomposition with a Single Global Latent
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 AI
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Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction
arXiv:2607. 05568v1 Announce Type: cross Abstract: Representing 3D shapes as compact sets of geometric primitives is fundamental to robotics, simulation, and scene understanding.
arXiv Computer Vision
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Volume Transformer: Revisiting Vanilla Transformers for 3D Scene Understanding
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...