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...
arXiv:2609.40353v1 Announce Type: new Abstract: The task of 3D assembly requires translating an understanding of parts and their relationships into precise spatial arrangements. Can pretrained genera...
arXiv:2605. 26182v2 Announce Type: replace Abstract: Generating physically buildable brick structures from 3D shapes requires more than geometric reconstruction: the output must also satisfy discrete part constraints and structural stability.
arXiv:2608.22637v1 Announce Type: new Abstract: Recent vision-language models (VLMs) show strong capabilities in robotic perception and spatial reasoning, yet their ability to reason about complex me...
arXiv:2603. 16085v2 Announce Type: replace-cross Abstract: Recent breakthroughs in 3D generation have enabled the synthesis of high-fidelity individual assets.
Recent advances in large language models and programmatic CAD have significantly improved Text-to-CAD generation for individual parts. However, production-ready mechanical assembly generation remains largely unsolved.
arXiv:2605. 28579v2 Announce Type: replace Abstract: Large language models (LLMs) have recently advanced text-driven 3D generation, yet Text-to-CAD remains far from supporting industrial product design.
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...
arXiv:2607. 05123v1 Announce Type: new Abstract: Recent advances in large language models and programmatic CAD have significantly improved Text-to-CAD generation for individual parts.
Recent vision-language models (VLMs) show strong capabilities in robotic perception and spatial reasoning, yet their ability to reason about complex mechanical assemblies remains underexplored. We int...
CADKnitter is a compositional CAD generation framework that uses geometric-guiding cues to steer diffusion sampling, enabling the creation of complementary CAD parts that satisfy both geometric constraints of an existing model and semantic constraints from a text prompt. The authors introduce KnitCAD, a dataset of over 310,000 CAD models paired with textual prompts and assembly metadata to support training and evaluation. Experiments show that CADKnitter outperforms state‑of‑the‑art baselines by a clear margin.
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.