arXiv Computer Vision
MeshQuery: Agentic Seam Planning for UV Parametrization
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3DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code
arXiv:2606. 01057v1 Announce Type: cross Abstract: Procedural 3D modeling through code is emerging as a versatile paradigm, offering deterministic, engine-ready, and precisely editable assets that neural 3D generators inherently lack.
arXiv Computer Vision
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ViSculpt: Visual-Centric Agentic Geometry Editing
arXiv:2608.24169v1 Announce Type: new Abstract: 3D geometry editing is a critical yet labor-intensive part of the graphics pipeline, requiring artists to translate creative intent into precise operat...
arXiv AI
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ATP-Bench: Towards Agentic Tool Planning for MLLM Interleaved Generation
ATP‑Bench proposes a new benchmark for evaluating agentic tool planning in multimodal large language models (MLLMs) that generate interleaved text-and-image responses. The benchmark contains 7,702 QA pairs, including 1,592 visual‑question‑answer pairs, across eight categories and 25 visual‑critical intents, all verified by humans. A Multi‑Agent MLLM‑as‑a‑Judge (MAM) system is introduced to assess tool‑call precision, missed opportunities, and overall response quality without relying on ground‑truth references.
arXiv AI
Jul 15
CrochetBench: Can Vision-Language Models Move from Describing to Doing in Crochet Domain?
arXiv:2511. 09483v3 Announce Type: replace Abstract: While multimodal large language models can describe visual content, their ability to generate executable procedures remains underexplored.
arXiv AI
Jun 10
Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation
arXiv:2606. 10601v1 Announce Type: cross Abstract: Generating high-quality meshes for arbitrary geometries remains a fundamental bottleneck in computational engineering, often demanding heuristic tuning and semi-manual workflows.
Hugging Face Trending Papers
Jun 9
Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation
Generating high-quality meshes for arbitrary geometries remains a fundamental bottleneck in computational engineering, often demanding heuristic tuning and semi-manual workflows. In this paper, we introduce Dmsh, a first fully automated reinforcement learning pipeline that unifies geometric decomposition and quadrilateral mesh generation within a single learning-based framework.