Thinking in Blender: Staged Executable Inverse Graphics with Vision-Language Models
Guangzhao He Rundong Luo Wei-Chiu Ma Hadar Averbuch-Elor
作者信息
Abstract
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.引用本文复制引用
Guangzhao He,Rundong Luo,Wei-Chiu Ma,Hadar Averbuch-Elor.Thinking in Blender: Staged Executable Inverse Graphics with Vision-Language Models[EB/OL].(2026-06-01)[2026-06-05].https://arxiv.org/abs/2606.02580.学科分类
计算技术、计算机技术
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