AI & ML
Seeing Both the Forest and the Trees: Reusing Holistic 3D Priors for Part-Decomposed Generation
arXiv cs.GR0 views
arXiv:2610.21850v1 Announce Type: cross Abstract: Generating 3D assets with explicit parts requires two properties at once: geometric detail inside each part and structural coherence across the whole object. Recent image-conditioned 3D generation models adopt a structured 3D representation that anchors tokens to explicit spatial positions, capturing both properties, but their output remains a single fused mesh. We ask how a part-decomposed generation model can inherit both properties of these holistic priors, and answer with STRUCTPARTS, a framework whose core technical contribution is a dual-frame coordinate interleaving mechanism: each shape latent is interpreted under both a part-local and an object-global reference frame, and processed through a global-local inter-leaved transformer that simultaneously reuses the prior's geometric detail at the part scale and its structural coherence at the whole-object scale. As its structural input, STRUCTPARTS uses a segmented mesh scaffold, a coarse mesh paired with a face-level segmentation mask kept entirely within the prior's native 3D representation. With only lightweight finetuning, both intra-part detail and inter-part coherence emerge from the same pretrained weights. On PartObjaverse-Tiny, STRUCTPARTS matches or surpasses prior part-decomposed models at a fraction of their training cost, while naturally supporting part-mesh refinement and part-level editing.
Source:https://arxiv.org/abs/2610.21850PreviousLearning to Search for Executable Patternmaking from Certified ProgramsNextACEdit: Stable Multi-Turn Image Editing via Asymmetric Conditioning
Related posts

Copilot Fusion's Local Model Preview: Only One of Three Stages Runs on Your Machine
0dev.to AI

Meituan shipped LongCat-2.5
0dev.to AI
Standard bf16 Rounding Discards 99% of Your RL Policy Updates
0dev.to AI
Why Are Startups Choosing Coworking Space in Pune in 2026?
0dev.to AI
What a 32GB GPU actually lets you run locally
0dev.to AI

LLM weak random number generator detection test
0dev.to Security