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arXiv · 2512.11393

The N-Body Problem: Parallel Execution from Single-Person Egocentric Video

Zhifan Zhu·Yifei Huang·Yoichi Sato·Dima Damen
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Abstract

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.

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Zhifan Zhu, Yifei Huang, Yoichi Sato, Dima Damen. 2025-12-12. The N-Body Problem: Parallel Execution from Single-Person Egocentric Video. https://arxiv.org/abs/2512.11393

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