4DCodeBench: Benchmarking Agents on Inverse Graphics of Dynamic Scenes
arXiv 人工智能预印本(Atom) · 2026-10-03 01:58 发布 · 2026-10-06 06:58 采集 · Jiajun Wu
基本信息
原始数据层(AI 与用户都不会覆盖这些字段)
❔ 待判断
政府/政策
AI
状态:已分析
综合价值 3.7 / 5
- 来源
- arXiv 人工智能预印本(Atom)(arXiv · INTL)
- 发布时间
- 2026-10-03 01:58
- 采集时间
- 2026-10-06 06:58
- 语言
- en
- 地域置信度
- 20%
- 原始链接
- https://arxiv.org/abs/2610.03715v1
- 内容指纹
- fefc63893969e8fea388ea4381d5ea35be9a18c000b19e9c9fab6ff04eb4f6fe
AI 整理
分析于 2026-10-06 07:06(第 1 版,来源:offline)We introduce 4DCodeBench, a benchmark for 4D inverse graphics through code generation, in which agents reconstruct dynamic scenes from video as executable graphics programs.
为什么值得关注:属于政府/政策信息,对教育或研究方向可能产生影响
趋势标签: dcodebench agents graphics scenes
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仅在合法且采集到正文时显示;否则请查看原文链接We introduce 4DCodeBench, a benchmark for 4D inverse graphics through code generation, in which agents reconstruct dynamic scenes from video as executable graphics programs. To accomplish this, agents must translate visual observations into compact representations of scene structure and dynamics, by implementing abstractions such as physical simulations to reproduce complex behavior. To evaluate this capability, we curate a set of real-world videos and construct synthetic scenes spanning diverse physical phenomena, including deformation, fluid flow, and fracture. We perform extensive benchmarking of frontier models, finding that strong static reconstruction capabilities do not yet translate into reliable reconstruction of complex dynamics. 4DCodeBench provides a testbed for tracking progress toward agents that can interpret the dynamics of the world through code. Our benchmark is available at https://github.com/4DCodeBench/4DCodeBench
事件演化
同一事件的后续报道、状态变化与版本记录
2026-10-03
首次出现
4DCodeBench: Benchmarking Agents on Inverse Graphics of Dynamic Scenes
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