Kaininja: Extending Native 3D Generators to the Part Level
Summary
KaiNinja extends a native 3D generator to produce part-level outputs using a dual-volume representation, improving both part and whole-object fidelity without requiring segmentation.
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Paper page - Kaininja: Extending Native 3D Generators to the Part Level
Source: https://huggingface.co/papers/2609.15659 Authors:
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Abstract
KaiNinja extends a native 3D generator to part-level outputs using a dual-volume representation that resolves interface conflicts, improving both part and whole-object fidelity without segmentation.
Native 3D generators turn one image into a single mesh.TRELLIS.2and its peers deliver high-fidelity non-watertight geometry with materials, but the output is one fused object, while downstream work such as editing, rigging and simulation operates on part-level assets. A naive idea is to run a 3D segmentation network on the fused mesh thatTRELLIS.2generates, but such pipelines are slow and bounded by the accuracy of the segmentation. We want a simple way to extend an existingnative 3D generatorto the part level. But we face a critical problem: theO-Voxelgrid stores one sheet of surface per voxel, so a single volume cannot represent the interface where two parts touch, at any resolution. We introduce adual-volume representationto solve this problem and put forward KaiNinja, a part-level extension ofTRELLIS.2built on a dual-volume form of itsO-Voxelrepresentation. KaiNinja keeps the generation speed and quality ofTRELLIS.2while extending it to the part level, with no mask or segmenter in the pipeline. Its training data come from sources of many kinds, including CAD models and assets authored by an LLM-driven agent; to our knowledge it is the first 3D generative model trained on agent-authored part data. Surprisingly, we also find that whole-object fidelity improves over the same backbone fine-tuned on the same dataset. Against part generation pipelines of different paradigms, it lowers whole-objectChamfer distanceby 40% and raises strictpart F-scoreby 16%.
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