ViSculpt: Visual-Centric Agentic Geometry Editing
On the map: LLM agents and tool use → Multimodal and embodied agents
Abstract (arXiv)
3D geometry editing is a critical yet labor-intensive part of the graphics pipeline, requiring artists to translate creative intent into precise operations in complex professional software. Large language models (LLMs) have shown promise for script-based 3D creation, but script generation is less suited to perception-driven editing of arbitrary existing meshes, where execution must remain visually grounded and untouched regions should be preserved. We present a visual-centric, training-free multi-agent system that edits existing 3D meshes directly in Blender by emulating the iterative workflow of human artists. Rather than generating scripts or regenerating geometry, our system operates through the Blender GUI: multimodal LLM agents observe the viewport, reason about the current mesh state, and execute localized edits through simulated user interactions. Experiments on a curated benchmark provide initial evidence that this agentic approach can follow natural language instructions, perform representative localized mesh edits, and preserve the overall identity of the input asset. Our results highlight a complementary regime for language-driven 3D editing: direct in-place modification of existing meshes within the native 3D editing workflow. We view this work as an exploratory step toward visual-centric agentic geometry editing in professional graphics software.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 28 of its 57 references that are in the map. First posted on arXiv 25 Aug 2026.
Builds on
28 of 57 references.
Closest ancestors · 3
Same direction, newest first
- 2025Agentic 3D Scene Generation with Spatially Contextualized VLMs26 May
- 2025Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation5 May
- 2024L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects14 Feb
Other references · 25
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2023Segment Anythingcited by 8,234
- 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,431
- 2024SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilitiescited by 706
- 2024CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assetscited by 455
- 2022CLIP-Mesh: Generating textured meshes from text using pretrained image-text modelscited by 281
- 2025Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformercited by 277
- 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 271
- 2025Native and Compact Structured Latents for 3D Generationcited by 175
- 2025Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Materialcited by 165
- 2023SAM3D: Segment Anything in 3D Scenescited by 157
13 more
- 2023Infinite Photorealistic Worlds using Procedural Generationcited by 147
- 2024Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generationcited by 137
- 2017CSGNet: Neural Shape Parser for Constructive Solid Geometrycited by 106
- 2023Anything-3D: Towards Single-view Anything Reconstruction in the Wildcited by 92
- 2020UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Treecited by 78
- 2023GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planningcited by 61
- 2025OmniPart: Part-Aware 3D Generation with Semantic Decoupling and Structural Cohesioncited by 61
- 2024Instant3dit: Multiview Inpainting for Fast Editing of 3D Objectscited by 44
- 2024Img2CAD: Conditioned 3D CAD Model Generation from Single Image with Structured Visual Geometrycited by 37
- 2024BlenderLLM: Training Large Language Models for Computer-Aided Design with Self-improvementcited by 29
- 20243D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp Features and Parametric Control?cited by 21
- 2025Agents of Change: Self-Evolving LLM Agents for Strategic Planningcited by 21
- 20243D Mesh Editing using Masked LRMscited by 18
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2025LL3M: Large Language 3D Modelers
- 2026DesignAgent3D: Interactive 3D Scene Editing via Designer-like Multimodal Reasoning
- 2024BlenderAlchemy: Editing 3D Graphics with Vision-Language Models
- 2025ShapeCraft: LLM Agents for Structured, Textured and Interactive 3D Modeling
- 20263DHarnessBench: Probing Agentic 3D-to-Code Capabilities of Frontier Vision-Language Models
- 2024SceneCraft: An LLM Agent for Synthesizing 3D Scene as Blender Code
- 20263DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code
- 2026SimWorlds: A Multi-Agent System for Dynamic 3D Scene Creation