3DrawAgent: Teaching LLM to Draw in 3D with Early Contrastive Experience
Hongcan Xiao, Xinyue Xiao, Yilin Wang, Yue Zhang, Yonggang Qi

TL;DR
3DrawAgent introduces a training-free, language-driven framework that enables large language models to generate complex 3D sketches by leveraging pairwise comparisons and geometric feedback, advancing 3D sketching AI.
Contribution
It proposes a novel relative experience optimization strategy for 3D sketch generation that does not require explicit supervision or parameter updates.
Findings
Generates complex 3D Bezier sketches from diverse prompts.
Exhibits emergent geometric reasoning and generalization to new shapes.
Self-improves spatial understanding without parameter updates.
Abstract
Sketching in 3D space enables expressive reasoning about shape, structure, and spatial relationships, yet generating 3D sketches through natural language remains a major challenge. In this work, we introduce 3DrawAgent, a training-free, language-driven framework for 3D sketch generation that leverages large language models (LLMs) to sequentially draw 3D Bezier curves under geometric feedback. Unlike prior 2D sketch agents, our method introduces a relative experience optimization strategy that adapts the recently proposed Group Reward Policy Optimization (GRPO) paradigm. Instead of relying on explicit ground-truth supervision, we construct pairwise comparisons among generated sketches, with each pair consisting of a relatively better and a worse result based on CLIP-based perceptual rewards and LLM-based fine-grained qualitative assessment. These experiences are then used to iteratively…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
