← 研踪 PaperTrailarXiv:2611.16221 · v1
BWM-Forge: Agentic World Programming for Embodied Intelligence
首次提交 2026-11-12 11:14
版本更新 2026-11-12 11:14
雷达观测 2026-11-13 14:16
摘要
Generating diverse robot videos requires control over scene geometry, robot motion, and viewpoints. End-to-end video generation offers limited direct access to these variables, making targeted edits and reuse across tasks difficult. We present BWM-Forge, a code-driven framework for controllable video generation in robotics. Given robot assets and a task description, the framework accepts an initial image, an image with motion data, or a human demonstration video. A coding agent constructs editable Blender scene programs that specify objects, robot motion, and cameras. The resulting proxy videos provide structural and motion references for a video generation model, which adds visual detail. A multi-agent harness coordinates input processing, scene construction, review, and rendering. Automated evaluation and user feedback support iterative refinement, while an experience repository stores and retrieves reusable modeling and rendering rules. This design supports multiple robot embodiments, synchronized viewpoints, and appearance variation from a shared scene program. We evaluate the framework on 15 tasks covering five robot types. Compared with LingBot-World Base Cam on these tasks, BWM-Forge achieves higher aesthetic quality, imaging quality, subject and background consistency, motion smoothness, and RoboJudge-9B physical adherence and instruction alignment scores. The baseline scores higher in dynamic degree. Qualitative cases illustrate viewpoint control, scene variation, feedback-driven edits, and experience reuse across scenes. These results demonstrate a practical approach to generating controllable robot videos through editable, executable scene representations.
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v1 · 2026-11-12 11:14 · 最新观测版本
BWM-Forge: Agentic World Programming for Embodied Intelligence
Generating diverse robot videos requires control over scene geometry, robot motion, and viewpoints. End-to-end video generation offers limited direct access to these variables, making targeted edits and reuse across tasks difficult. We present BWM-Forge, a code-driven framework for controllable video generation in robotics. Given robot assets and a task description, the framework accepts an initial image, an image with motion data, or a human demonstration video. A coding agent constructs editable Blender scene programs that specify objects, robot motion, and cameras. The resulting proxy videos provide structural and motion references for a video generation model, which adds visual detail. A multi-agent harness coordinates input processing, scene construction, review, and rendering. Automated evaluation and user feedback support iterative refinement, while an experience repository stores and retrieves reusable modeling and rendering rules. This design supports multiple robot embodiments, synchronized viewpoints, and appearance variation from a shared scene program. We evaluate the framework on 15 tasks covering five robot types. Compared with LingBot-World Base Cam on these tasks, BWM-Forge achieves higher aesthetic quality, imaging quality, subject and background consistency, motion smoothness, and RoboJudge-9B physical adherence and instruction alignment scores. The baseline scores higher in dynamic degree. Qualitative cases illustrate viewpoint control, scene variation, feedback-driven edits, and experience reuse across scenes. These results demonstrate a practical approach to generating controllable robot videos through editable, executable scene representations.
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