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All fields3324Multimodal499Agents499Frontier LLMs431Inference & serving248Learning verifiable rewards165Image segmentation164Graph learning1593D scene142Evals108Voice AI88Classifier & causal discovery77Flow matching76Forecasting time75Training & fine-tuning64Teacher policy distillation57CAD models15All 32 fields
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  • Sensing communication16+23.1%
  • Federated learning40+8.1%
  • Agents4990.0%
  • Frontier LLMs431−7.5%
  • Learning verifiable rewards165−17.5%
  • Forecasting time75−17.6%
  • Inference & serving248−18.2%
  • Character recognition OCR15−21.1%

Papers

  • arXiv cs.CVOct 19

    Can It Actually Work? Synthesizing Functional CAD Mechanisms

    Wenqian Zhang, Yangyi Huang, Zigeng Xu, Weiyang Liu, Zhen Liu

    While LLM agents can generate visually plausible CAD assemblies, these designs often fail to fulfill their intended physical functions. We study functional CAD design, a setting where agents interpret underspecified requests to construct and iteratively revise mechanisms that satisfy functional requirements in physical simulation. Verifying these requirements is nontrivial: different candidate mechanisms may realize the same function through entirely different components, controls, and operating sequences. To bridge this gap, we propose FunCAD-Harness, a framework that translates abstract requirements into candidate-specific physical tests and uses simulation feedback to guide design revisions. To systematically evaluate this capability, we introduce FunCADBench, a benchmark spanning 46 mechanism families with interdependent and multi-step operations. Experiments across frontier LLM agents show that FunCAD-Harness exposes subtle mechanical failures invisible to visual inspection, substantially improving functional success rates over standard simulation-augmented baselines. Ultimately, our work marks a step toward CAD agents capable of translating human intent into working…

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  • arXiv cs.CVOct 19▲ 25

    InfiniDream: Open-Ended Photorealistic 3D Worlds Through Procedural Generation

    Yi-Chuan Huang, Hao-Jen Chien, Zheng-Hui Huang, Yu-Lun Liu, Kaipeng Zhang +1

    Procedural generation plays a key role in synthetic data creation and game development, offering greater controllability, editability, and diversity than static mesh representations. However, VLM-based code generation often produces relatively simple 3D assets, while sophisticated photorealistic procedural systems require extensive manual development. Existing VLM-guided procedural methods remain largely constrained by predefined factories and APIs, limiting their ability to create novel asset types. We introduce InfiniDream, a VLM-driven framework for open-ended procedural 3D world generation. First, we propose Symbolically Constrained Asset Generation, which leverages existing procedural algorithms to create novel, photorealistic assets while preserving editability and seed-based diversity. Second, we develop an end-to-end text-to-world pipeline that combines semantic scene planning with Deterministic Frustum-Guided Grid Placement to construct large-scale 3D worlds with precise object placement, procedural population, and animation. Extensive evaluations on 100 object and 20 scene generation tasks demonstrate improvements across four perceptual metrics. Diversity analysis,…

    CodePDF
  • arXiv cs.CVOct 19

    Open-Form Geometric Style Transfer

    Adar Elad, Gal Metzer, Omer Dahari, Min Lu, Daniel Cohen-Or

    A geometric style learned from one family of shapes is typically applied to new shapes from the same family. We consider a more challenging setting: learning the geometric design language of a stylized Latin alphabet and applying it to new forms, from Greek glyphs to entirely different structures such as human poses. We introduce Open-Form Geometric Style Transfer, where the forms to which a learned geometric style can be applied are not restricted to the shape family in which that style was observed. This setting is deceptively challenging: the difficulty is not merely learning a style, but learning the style without learning the shape family in which it was observed. We represent target forms by structural sketches and disentangle structural adherence from style realization using a Geometry LoRA and a style-specific LoRA. Critically, open-form transfer requires the Geometry LoRA to be universal. To achieve this, we devise a geometry-enrichment strategy that constructs a large and purposefully diverse collection of synthetic structural sketches spanning a broad range of unfamiliar geometries. Our experiments and ablations demonstrate the importance of both disentanglement and…

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  • arXiv cs.LGOct 19

    CAST: Cast-Admissible Generative Design and Shape Optimization with Paired Depth Images

    Jihoon Kim, Yongmin Kwon, Namwoo Kang

    Deep generative design can produce complex geometries, but unconstrained representations may create undercuts that prevent release from a two-part mold. We present CAST, which reformulates three-dimensional shape generation under a selected mold-opening constraint as a two-dimensional image generation problem using paired top and bottom depth images. Each valid depth pair defines one material interval along a selected mold-opening direction, so reconstructions exclude undercuts in that direction by construction. Diffusion models generate candidate shapes in this representation, and a finite element analysis (FEA) guided optimizer refines the same two boundaries. We evaluate diffusion-based generation with limited training data comprising 211 SimJEB bracket designs. On SimJEB brackets, FEA-guided refinement with a volume penalty reduced compliance in 193 of 200 generated candidates. The median reduction across all candidates was 36.8 percent in the optimization FEA model. Reconstruction of existing brackets revealed a mass--stiffness trade-off: filling occluded regions increased material volume and reduced displacement under the evaluated loads. Paired depth images thus connect…

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  • arXiv physics.comp-phOct 18

    Concurrent two-scale topology optimization of printable bone scaffolds via rectified-flow generation and exact latent-space refinement

    Reza T Batley, Sourav Saha

    Bone scaffolds must reconcile a macroscale mechanical response with microscale geometric feasibility, yet conventional approaches to their design restrict attainable constitutive properties. This work presents a concurrent two-scale topology optimization framework; a conditional rectified flow generates periodic lattice microstructures, which are then refined in latent space by exact finite-element homogenization sensitivities. The resulting unit-cell geometries and their spatial assignments are jointly optimized under mechanical and feature-size constraints. In a distal rabbit femur benchmark, all eight flow-started refinements produce printable cells with maximum relative errors under 1\% across six prescribed engineering moduli. Constructing the implant from a single tiled cell reproduces intact whole-femur compliance within 0.1\% and regional strain energy within 0.35\% under four loading conditions. In human femoral reconstruction, multiscale concurrent optimization produces a cortical mid-shaft scaffold with porosity 63\% while satisfying the compliance constraint within 2\% under walking and stair-climbing loads. It achieves an 84\% porosity on a proximal cancellous…

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  • arXiv cs.AIOct 17

    IndustrialCAD-1400: Benchmarking Detailed Industrial Assemblies from Text

    Hebao Zhu, Kang An, Zhuoke Liao, Faqiang Qian, Ziliang Wang +2

    Industrial assemblies require more than a recognizable shape: functional parts, interfaces, dimensions and service features must satisfy a detailed request together. We introduce IndustrialCAD-1400, a text-to-CAD benchmark for this industrial setting. Its 1,400 requests combine 400 equipment categories across 40 domains with 250 four-case design families. Family requests specify a median of 17 named component or configuration roles and six explicit numeric targets; controlled variants test whether dimensions change while shared structure is retained. Task construction draws on documented manufacturer information, owner specifications and engineering guidance. Models generate geometry programs from requests, and a common Blender environment executes them to produce scenes, optional measurements, and standardized views for evaluation. A request assessment of front, isometric, and top views measures visible component, arrangement, and interface fulfillment without requiring a single reference geometry. Five stratified panels preserve complete families while covering the benchmark exactly once, enabling balanced comparisons and separating industrial breadth from controlled numerical…

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  • arXiv cs.CVOct 16

    FlexPart: Text-Controlled Semantics-Aware 3D Part Segmentation at Flexible Granularity

    Yiqun Zhao, Jiachen Xu, Yihao Zhi, Yunfei Zhao, Zibo Zhao +3

    A 3D asset admits multiple valid part decompositions, while semantics-aware segmentation should reflect both the desired granularity and the parts a user wants to extract. Ideally, a practical segmentation model should strive for a simple principle: what you ask for is what you get. However, realizing this goal is hindered by two core obstacles: the lack of multi-granularity training data and the absence of a holistic control mechanism. We propose FLEXPART, a unified framework for text-controlled 3D mesh segmentation. To establish flexible control, we introduce a holistic control prompt comprising semantic part names with location and shape clauses. To support this mechanism, we utilize an automated Vision-Language Model (VLM) pipeline to construct a large-scale dataset of multi-granularity partitions and their corresponding prompts. Driven by this data, FLEXPART generates a view-independent part-identity field in native 3D space, directly assigning discrete part IDs to mesh faces. To compel the model to respect semantic conditions rather than rely on geometric priors, we train the model using randomized background dropping. Furthermore, an inference-time VLM planner can…

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  • arXiv cs.LGOct 16

    Structural Affinity: An Algorithm for Coherent Cage-Based Shape Design

    Aviraj Newatia, Shengan Yang, Hantao Zhong, Rika Antonova

    Cage-based deformation (CBD) parametrises 3D geometry through a small set of control handles, effectively enabling a variety of tasks from animation and interactive modelling to tool-based co-design, neural deformation transfer, and aerodynamic shape optimization. Many optimization approaches relying on CBD employ regularisation strategies to reduce the distortion of the deformed mesh by encouraging smooth, regular cage deformations. These techniques offer soft guidance to an optimization algorithm towards desirable deformations, but compete with the task objective rather than changing the deformation parameterization itself. In this work we introduce Structural Affinity for CBD, an explicit regularisation method which enables smooth deformations through control cages by exploiting a two-cage structure to couple cage-handle motion directly. Structural Affinity precomputes an affinity matrix using mean-value coordinates, binding the inner control-cage to a coarser outer-cage. This matrix defines how much a cage handle displacement propagates to neighbouring handles which substantially decreases and often eliminates cage self-intersections, reduces mesh distortion, and adds linear…

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  • arXiv cs.CVOct 16▲ 56

    Scene-Designer: Learning Design Intelligence for Agentic 3D Scene Generation

    Xuanyu Liu, Hongyan Feng, Miao Pan, Sunlai Chen, Lizhou Liang +3

    Generating complete, coherent, and usable 3D indoor scenes from natural language requires strong \textbf{design intelligence}: the ability to design high-quality scenes and critique their quality. However, existing approaches either rely on general-purpose models lacking domain-specific design experience or learn from static scenes. In this work, we introduce \textbf{Scene-Designer}, an agentic framework that learns design intelligence through complementary design and critique experiences. We construct two datasets: \textbf{Scene-OTA-35K}, which provides Observation--Thought--Action supervision for learning scene design and efficient error correction, and \textbf{Scene-OTC-15K}, which provides Observation--Thought--Critic supervision for learning scene quality critique across physical validity, functional correctness, and layout coherence. Based on these experiences, we propose \textbf{Scene-GRPO}, a Critic-guided GRPO framework that improves design decisions and reduces unnecessary trial-and-error corrections. Extensive experiments demonstrate that Scene-Designer achieves superior performance in physical validity, functional correctness, and layout coherence, while the learned…

    Code
  • arXiv cs.CVCode modelsOct 15▲ 107

    3DCodeVerse: Exploring 3D Worlds as Code

    Yipeng Gao, Ziyao Zeng, Jinfa Huang, Yijiang Li, Haolin Xiong +14

    We introduce 3DCodeVerse, a research ecosystem that enables coding agents to create editable and verifiable 3D content at scale. 3DCodeVerse comprises large-scale, high-quality datasets of executable 3D code, a coding harness for spatial reasoning and automated verification, and practical guidance for 3D coding. First, we construct 3DCodeVerse-2M, a large-scale curated dataset of standalone 3D executable code. We build this corpus by collecting open-source data, converting specialized representations, and generating new programs with coding agents. Each program is packaged with the source files and associated assets required for execution in its declared environment, along with available descriptions and verification records. Second, we develop the 3DCodeVerse Harness, a multi-agent directed acyclic graph framework for generating and refining 3D content. It uses execution checks, spatial measurements, and visual feedback to automatically evaluate and revise generated programs without human feedback during generation. Third, we conduct systematic empirical studies of 3D coding, establishing baselines with fine-tuned open models and deriving practical guidance through…

    AIAgents80Code
  • arXiv cs.AIOct 15

    GNN-Guided Local Refinement for Efficient Layout Decomposition with DSA-MP

    Yu Feng, Liyi Zhou, Haoyu Wang, Yiming Xu, Hao Geng +3

    Multiple patterning lithography (MPL) has been widely used to improve lithography resolution in advanced nodes, but it faces cost limits as feature sizes shrink. Directed self-assembly (DSA) offers a cost-effective complement, extending the resolution of optical lithography to sub-10 nm and beyond. Integrating DSA into MPL (DSA-MP) further enhances the resolution of contact/via patterning. Nevertheless, growing design complexity exposes the limits of existing decomposition methods, which struggle to balance solution quality and runtime, and neglect edge-to-edge spacing constraints of DSA templates, compromising manufacturability. To address these issues, we introduce a new conflict graph model capturing the previously overlooked constraints and develop a local refinement strategy powered by the graph neural network, enabling efficient and scalable layout decomposition. Experiments demonstrate that our method achieves higher solution quality with faster convergence than state-of-the-art methods, while maintaining robustness across layout scales, design styles, technology nodes, and varying mask counts.

    AI10PDF
  • arXiv cs.CLOct 15

    RenoLayout: Unified Floorplan Renovation and Furniture Layout for Constraint-Aware Residential Design

    Jingguo Liu, Yuanqing Liu, Chong Zhang, Yue Yang

    Residential design aims to transform existing homes into functional living spaces while satisfying diverse design preferences and physical constraints. However, existing floorplan generation methods typically assume empty building boundaries as input, whereas renovation requires reasoning over existing floorplans. Furthermore, floorplan renovation and furniture layout are commonly addressed as separate tasks, overlooking their inherent spatial dependency. Consequently, practical residential design must preserve immutable structural components while transforming editable spatial elements. It must also optimize furniture placement under design objectives and physical constraints. To address these challenges, we formulate residential design as a design-point-guided conditional generation task and propose RenoLayout, a framework that translates a pre-design residential state together with a design-point set into an executable Geometry-as-Code program. To enable controllable supervision, we construct a Design Map comprising 123 fine-grained design points covering structural renovation and furniture layout. We further introduce Difference-Grounded Design-Point Annotation to derive…

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  • arXiv cs.CVComputer visionOct 14

    MARS - A foundational map auto-regressor

    Qi Zhang, Suvam Bag, Rupanjali Kukal, Lucas Thai, Jacob Kovarskiy +7

    Map generation tasks feature extensive non-structural vectorized data (e.g., points, polylines, and polygons) and thus pose significant challenges to common pixel-wise generative models. Conventional approaches use multiple stages, first segmenting these features at the pixel level and then performing vectorized post-processing, with errors and complexity compounding at each stage. Motivated by the recent success of auto-regressive language modeling, we propose the first map foundation model, named Map Auto-Regressor (MARS), that is capable of generating both multi-polyline road networks and polygon buildings in a unified manner. For training MARS, we collected to our knowledge the largest multi-class map extraction dataset totaling 3.4M examples, which we call MAP-3M. Across four road and building datasets, MARS outperforms or matches the performance of multistage baselines. Additionally, we develop a "Chat with MARS" capability that enables interactive human-in-the-loop map generation and correction, supported by the auto-regressive nature of our end-to-end approach. We release our MAP-3M dataset and project demo page at (1) https://huggingface.co/datasets/bag-lab/MAP-3M and…

    AIMultimodal70PDF
  • arXiv cs.AIAgentsOct 14

    PhySGA: A Physics-Space Grounded Agentic Framework for Generalizable and Interactive 3D Composition

    Weichen Lyu, Chong Xia, Mingyu Dong, Long Xu, Shengjun Zhang +1

    3D object composition is pivotal to digital content creation and robot simulation, while also serving as a critical testbed for physical intelligence in foundation models. Previous methods either leverage large language models to directly predict object coordinates, or utilize generative diffusion priors to synthesize composite images for visual alignment, both of which rely on static priors in digital spaces. However, these methods consistently struggle with complex compositions such as precise hanging, gravity-driven settling and hidden containment, as these tasks require interactive construction processes involving global planning, fine-grained alignment, and physical simulation. To address these challenges, we propose PhySGA, a Physics-Space Grounded Agentic Framework for 3D composition via agent exploration and physical simulation, enabling the universal interactive assembly of rigid, articulated, and deformable objects. Directly applying vanilla LLMs or VLMs to this task proves insufficient, as current models inherently lack grounded physical understanding and interactive construction experience. To overcome this limitation, we design a collaborative feedforward-feedback…

    AIMultimodal80PDF
  • arXiv cs.CVOct 14

    Q-MAGIC: Quad-only Mesh Autoregressive Generation via Singularity-Aware Circular Relative Offset Tokenization

    Yuming Zhao, Zezeng Li, Weimin Wang, Xiang Wu, Na Lei

    Autoregressive models have proven effective for triangular mesh generation by transforming it into predicting the next face, converting the non-differentiable connectivity prediction into a sequence of token predictions. However, generating quad-only meshes remains challenging because quad faces impose stricter topological constraints than triangles. The model must not only serialize quad faces into a valid token sequence, but also support unambiguous connectivity reconstruction during de-serialization. To address this, we propose Q-MAGIC, a novel autoregressive model for quad mesh generation. We first introduce a tailored serialization scheme for quad meshes, which requires at least two new vertices for the next face and is less flexible than triangles due to strict wiring constraints. To meet these requirements and reduce serialization length, we propose a singularity-aware serialization that prioritizes wiring from singular points, leading to more coherent traversals. Additionally, we propose a circular relative offset tokenization that encodes geometry mainly via local relative offsets. Built upon this representation, an autoregressive Transformer learns the underlying…

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Research · AI field

CAD models

Found by the topic model: cad models.

Field page: people, tools, topics →
15papers this week−31.8% vs last week77 this month

Open problems in CAD models

  • Long-horizon iterative 3D editing1 paper raises it · 2 address it

    Real-world CAD assets are built through many sequential edits, but current methods fail to preserve unedited parts or follow instructions over long sequences.

    • EditHero: A Benchmark for Long-Horizon Part-Level 3D Editing and Vibe Modelingraises · addresses
    • 3DCodeVerse: Exploring 3D Worlds as Codeaddresses
  • Physically grounded and functional 3D generation2 papers raise it · 3 address it

    Generating 3D models that are buildable, stable, and functional remains difficult, as most methods focus only on geometry or appearance.

    • LMBuild: Evaluating LLM Agents for Generating Buildable and Functional Structuresraises · addresses
    • Towards Physically Grounded Articulated 3D Assets: A Multi-Agent Pipeline and Benchmarkraises · addresses
    • Scene-Designer: Learning Design Intelligence for Agentic 3D Scene Generationaddresses
  • Autonomous CAD reconstruction from 3D shapes1 paper raises it · 1 addresses it

    Recovering editable, parametric CAD programs from raw meshes is error-prone and requires coordinating multiple tools and proposals.

    • CADFather: Autonomous CAD Reconstruction through Coordinated Tool Useraises · addresses
  • Local control in 3D generation1 paper raises it · 1 addresses it

    Existing 3D generation methods lack fine-grained control over geometry and appearance, limiting precise editing and customization.

    • SpaceFlow: Locally Controllable 3D Generationraises · addresses
  • Open-ended procedural 3D world generation1 paper raises it · 1 addresses it

    Procedural generation of diverse, photorealistic 3D worlds is constrained by predefined APIs and lacks flexibility for novel asset types.

    • InfiniDream: Open-Ended Photorealistic 3D Worlds Through Procedural Generationraises · addresses

Named by an AI from these papers' abstracts.