arXiv cs.AI· Yuzhe Chen, Muxi Diao, Lele Yang, Wenbin Xu, Xuyang Liu, Qile Su, Changyi Liu, Jiankang Chen, Kaiyu Jiang, Haoyu Zhang, Hongyi Fu, Jianxiong Wang, Tianke Zhang, Haonan Fan, Yingxin Li, Bingyi Li, Xu Wang, Xuanyu Zheng, Bin Wen, Tingting Gao, Wentao Zhang, Jiale Cao·· 3 小时前AI 评分37
VibePrevisBench：用 Blender 评估面向 4D 场景生成的智能体编程能力
VibePrevisBench: Evaluating Agentic Coding for 4D Scene Generation in Blender
AI 导读
研究者推出 VibePrevisBench，一个评估编程智能体在 Blender 中重建 4D 场景的基准，包含 242 个任务，覆盖脚本、照片、续写和参考视频四类输入。
正文 · 原文
Authors:Yuzhe Chen, Muxi Diao, Lele Yang, Wenbin Xu, Xuyang Liu, Qile Su, Changyi Liu, Jiankang Chen, Kaiyu Jiang, Haoyu Zhang, Hongyi Fu, Jianxiong Wang, Tianke Zhang, Haonan Fan, Yingxin Li, Bingyi Li, Xu Wang, Xuanyu Zheng, Bin Wen, Tingting Gao, Wentao Zhang, Jiale Cao
Abstract:Controllable real-world video generation models can condition on a rendered graybox proxy video, which fixes layout, motion and camera. Coding agents can build such videos by reconstructing a 4D scene in Blender, yet no benchmark checks both these videos and the long, multi-shot scenes behind them. We introduce VibePrevisBench, a benchmark for 4D scene reconstruction: an agent turns a brief into an executable 4D scene and renders its proxy video. Its 242 tasks span four input kinds, including scripts, photos, continuations, and reference videos. Each delivery is scored along two evaluation dimensions. Rule-based Scene Check re-runs the code and compares the exported 4D scene with hidden ground truth facts in nine dimensions. VLM-based Screen Check asks a VLM judge whether the facts that pass can be recognized in the proxy video. We analyze the evaluation results and trajectories of 11 frontier coding agents and identify three key observations. First, multimodal understanding separates the agents, especially in understanding space and camera motion. Second, all agents seem to already master basic camera work and staging, and fail at challenging requests. Third, agents inspect their own renders but fail to correct their errors. Agents thus need better visual understanding of space and camera, explicit checks for challenging requests, and item-by-item verification of renders against the brief.
| Comments: | Project page: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.15789 [cs.CV] |
| (or arXiv:2611.15789v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15789 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuzhe Chen [view email]
[v1]
Wed, 11 Nov 2026 18:02:07 UTC (13,283 KB)
来源：arXiv cs.AI · arxiv.org