BlenderWorld: 実世界参照からの3D制作における視覚エージェントのベンチマーク
BlenderWorld: Benchmarking Visual Agents for 3D Creation from Real-World References
テキスト・画像・動画・仕様入力からBlenderで3Dコンテンツを制作するエージェントを評価する223タスクのベンチマークを構築し、13の最先端モデルを多視点レンダリングと2,651の二値ルーブリックで評価した。
著者: Xinyu Fang, Yuchi Wang, Weihao Yu, Rongyao Fang, Zhaohai Li, Zhixun Li, Tiancheng Gu, Xilin Wei, Yuhang Zang, Hongsheng Li, Dahua Lin, Guofeng Zhang, Shuai Bai, Zhibo Yang, Dayiheng Liu
分類: cs.CV
原文アブストラクト
Recent advances in multimodal large language models (MLLMs) have enabled them to act as autonomous agents, creating and editing 3D models via code or direct software manipulation. In practical workflows, these models must infer object shapes, materials, spatial layouts, and lighting from diverse references while strictly following instructions to reconstruct, modify, or design 3D content. However, existing benchmarks provide limited coverage of such open-ended requests across real-world scenarios. To bridge this gap, we introduce BlenderWorld, the first comprehensive benchmark dedicated to evaluating frontier models on realistic 3D creation tasks. BlenderWorld comprises 223 tasks spanning architecture, products, natural environments, and scientific visualization, driven by text, image, video, and structured specification inputs. To rigorously assess these complex deliverables, we establish a unified evaluation protocol: we render the submitted Blender projects from multiple viewpoints and evaluate them against 2,651 task-specific binary rubrics alongside holistic visual quality scores. Benchmarking thirteen frontier models reveals that GPT-6-Astra achieves the highest overall fused score of 79.9, followed by Opus-5 (73.0) and GPT-5.6-Sol (67.7). Ultimately, our findings reveal a critical bottleneck: while current models can parse broad instructions, they still profoundly struggle with fine-grained 3D spatial coherence and accurate structural reproduction in Blender, leaving substantial headroom for future 3D-native agentics.