Humans can intuitively parallelise complex activities, but can a model predict this from observing a single person? Given one egocentric video, we introduce the N-Body Problem: predicting how N individuals, can hypothetically perform the same set of tasks. The goal is to maximise speed-up, but naive assignment of video segments to individuals often violates real-world constraints, leading to physically impossible scenarios like two people using the same object or occupying the same space. To quantify this, we formalise the N-Body Problem and propose a suite of metrics to evaluate both performance (speed-up, task coverage) and feasibility (spatial collisions, object conflicts and causal constraints). As a proof of concept, we introduce a structured prompting strategy that guides a Vision-Language Model (VLM) to reason about the 3D environment, object usage, and temporal dependencies, producing a viable parallel execution. On 100 videos from EPIC-Kitchens and HD-EPIC, for $N = 2$, our structured prompt improves action coverage by 45% over a baseline prompt for Gemini 2.5 Pro, while simultaneously slashing collision rates, object and causal conflicts by 51%, 52% and 55% respectively.

翻译：人类能够直观地将复杂活动并行化，但模型能否通过观察单人行为预测这一过程？针对单段自我中心视频，我们提出N体问题：预测N个个体如何假设性地执行同一组任务。目标是最大化加速比，但将视频片段简单分配给个体往往会违反现实世界约束，导致两人使用同一物体或占据同一空间等物理上不可能的场景。为量化这一问题，我们形式化定义了N体问题，并提出一套评估指标，综合衡量性能（加速比、任务覆盖率）与可行性（空间碰撞、物体冲突及因果约束）。作为概念验证，我们引入一种结构化提示策略，引导视觉语言模型（VLM）推理三维环境、物体使用及时间依赖关系，生成可行的并行执行方案。在EPIC-Kitchens和HD-EPIC的100段视频中，针对N=2的情况，我们的结构化提示使Gemini 2.5 Pro的动作覆盖率较基线提示提升45%，同时将碰撞率、物体冲突和因果冲突分别降低51%、52%和55%。