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Thinking in Blender: Staged Executable Inverse Graphics with Vision-Language Models
Abstract
Pretrained vision-language models can reconstruct 3D scenes from single images as editable Blender programs through progressive refinement, demonstrating improved fidelity through staged reconstruction approaches.
AI-generated summary
Inverse graphics is a longstanding and highly underconstrained problem that seeks to reconstruct images as editable 3D scenes which can be rendered, relit, and manipulated. In this work, we investigate whether pretrained vision-language models (VLMs) can perform executable inverse graphics directly from a single image by reconstructing a scene as an editable Blender program, without relying on specialized 2D or 3D foundation models, differentiable rendering, or multi-view supervision. We introduce Staged Executable Inverse Graphics (SEIG), an agentic framework that reconstructs a 3D scene from a single image by progressively refining scene factors including geometry, materials, composition, and lighting directly in executable Blender code space. We evaluate our framework across diverse scenes using a range of reconstruction metrics spanning pixel-level, perceptual, and semantic fidelity. Our experiments show that staged reconstruction substantially improves reconstruction fidelity, highlighting the importance of task decomposition for executable inverse graphics with general-purpose VLMs. Finally, we showcase various downstream applications enabled by the reconstructed editable Blender scenes.
Community
SEIG is an agentic framework that reconstructs 3D scenes from single images by progressively generating executable Blender code, enabling novel-view synthesis, scene editing, and relighting.
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Cite arxiv.org/abs/2606.02580 in a model README.md to link it from this page.
Cite arxiv.org/abs/2606.02580 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2606.02580 in a Space README.md to link it from this page.
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