Thinking in Blender: Staged Executable Inverse Graphics with Vision-Language Models
Summary
This paper introduces SEIG, a framework that uses pretrained vision-language models to reconstruct 3D scenes from single images as editable Blender programs through progressive refinement of geometry, materials, composition, and lighting.
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Paper page - Thinking in Blender: Staged Executable Inverse Graphics with Vision-Language Models
Source: https://huggingface.co/papers/2606.02580
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.
Inverse graphicsis 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 pretrainedvision-language models(VLMs) can performexecutable inverse graphicsdirectly from a single image by reconstructing a scene as an editableBlender program, without relying on specialized 2D or 3D foundation models, differentiable rendering, or multi-view supervision. We introduce StagedExecutable Inverse Graphics(SEIG), an agentic framework that reconstructs a 3D scene from a single image by progressively refiningscene factorsincludinggeometry,materials,composition, andlightingdirectly 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 thatstaged reconstructionsubstantially improves reconstruction fidelity, highlightingthe importance oftask decompositionforexecutable inverse graphicswith general-purpose VLMs. Finally, we showcase various downstream applications enabled by the reconstructed editable Blender scenes.
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