SemEllipsoid introduces a semantic‑aware shape abstraction method that uses ellipsoidal primitives to represent 3D shapes. Unlike prior geometry‑only approaches, it enforces that each primitive lies entirely within a single semantic part by incorporating semantic constraints derived from large‑scale foundation models. The fully differentiable projection‑blending‑evaluation framework allows iterative optimization of both primitive placement and semantic assignment without requiring training data, and the method supports adjustable abstraction levels across diverse shape categories.
arXiv:2502. 08884v3 Announce Type: replace-cross Abstract: We present ShapeLib, the first method that uses the priors of Large Language Models (LLMs) to design libraries of programmatic 3D shape abstractions.
arXiv:2606.11152v4 Announce Type: replace Abstract: Multimodal large language models can write code to produce complex programs as well as use programs to do 3D modeling, which opens up a new avenue...
arXiv:2605. 19350v2 Announce Type: replace-cross Abstract: Creating and editing high-quality 3D content remains a central challenge in computer graphics.
arXiv:2607. 05568v1 Announce Type: cross Abstract: Representing 3D shapes as compact sets of geometric primitives is fundamental to robotics, simulation, and scene understanding.
TokenMatch is a transformer-based model that estimates 3D shape correspondences by adaptively tokenising meshes into curvature-guided patches. Trained only on the BeCoS partial-to-partial dataset, it generalises to full-shape matching without retraining, using self‑ and cross‑attention to learn patch‑ and point‑level relations. Evaluated on CP2P, PSMAL, BeCoS, FAUST, SCAPE, and SHREC'19, TokenMatch consistently outperforms existing methods in mean geodesic error and intersection‑over‑union while achieving sub‑second inference speeds.
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.
Fysiverse-3D-Vision is a unified vision‑language‑geometry framework that reconstructs executable 3D scenes from a single image. It separates spatial layout reasoning from asset synthesis, using a shared representation where spatial reasoning and geometric reconstruction reinforce each other. The model employs a Transformer that integrates textual supervision, semantic visual cues, and geometric representations, and includes an object‑conditioned layout module to predict object translation, rotation, and scale while maintaining physical consistency through collision‑aware optimization.
HERMES++ is a unified driving world model that simultaneously performs 3D scene understanding and future geometry prediction. It uses a BEV representation to merge multi‑view spatial data for LLM compatibility, introduces LLM‑enhanced world queries for knowledge transfer, and employs a Current‑to‑Future Link to condition future geometry on semantic context. A Joint Geometric Optimization strategy enforces structural integrity by combining explicit geometric constraints with implicit latent regularization, and extensive benchmark tests show that HERMES++ outperforms specialist methods in both point cloud prediction and scene understanding.
arXiv:2609.23386v1 Announce Type: new Abstract: Text-guided 3D building generation holds tremendous application potential, yet existing generative models typically output inseparable single meshes or...
SemGeo-Gen is an unsupervised approach that generates approximate cross‑instance semantic‑geometric correspondences from weakly aligned 3D object collections. It uses coarse category‑level rotation alignment, lifts multi‑view DINOv2 features to 3D, applies continuous piecewise‑affine registration, and prunes matches with semantic consistency to produce 3D correspondences that can be projected into 2D views for scalable supervision. The method achieves high accuracy against human annotations and improves state‑of‑the‑art semantic correspondence models after synthetic pretraining.
Optimizing 3D shapes within the latent spaces of deep generative models is fundamental to computer assisted engineering, yet remains prone to a critical failure mode we term manifold drift: the tendency of gradient-based optimization to move latent vectors away from the manifold of valid shapes. This problem is exacerbated in state-of-the-art 3D shape generative models that operate in increasingly high-dimensional latent spaces where valid shapes occupy a vanishingly small fraction of the full space.
arXiv:2603. 05607v2 Announce Type: replace-cross Abstract: Computer-Aided Design (CAD) relies on structured and editable geometric representations, yet existing generative methods are constrained by small annotated datasets with explicit design histories or boundary representation (BRep) labels.