Ego2Act：评估第一视角视频生成中的目标导向操作

Authors:Patrick Amadeus Irawan, Iskandar Muda Rizky Parlambang, Rava Maulana, Qinrong Cui, Erland Hilman Fuadi, Zayd M. K. Zuhri, Nanda Ryaas Absar, Ahmed Elshabrawy, Wilfried Ariel Mulyawan, Shoubin Yu, Yue Zhang, Mohit Bansal, Alham Fikri Aji

View PDF HTML (experimental)

Abstract:Video generation models are increasingly being explored as world simulators for embodied planning and learning. To do so effectively, these models must not only generate visually appealing frames, but also predict how environments dynamically evolve when executing goal-directed actions. While evaluating these capabilities is crucial, existing benchmarks focus mainly on single short actions or step-by-step instructions. This leaves multi-step physical reasoning underexplored, especially in egocentric video generation that requires planning to simulate proper execution to accomplish high-level goals by carrying out multiple real-world manipulations. We introduce Ego2Act, a goal-directed benchmark featuring 2,640 videos from 110 real-world tasks across day-to-day settings, varying object clutter and multi-step complexity. Given an initial scene image and a high-level goal, Ego2Act evaluates whether video generation models can produce realistic egocentric videos of a hand manipulating objects to carry out the task. To support scalable evaluation, we also introduce Ego2ActJudge, a reference-free evaluation pipeline that achieves better task completion and physics plausibility evaluation alignment with human consensus compared to relevant baselines. Our findings reveal that models' generated simulations often skip or partially execute steps, leaving later steps missing dependent states, which leads to unfulfilled goal. Furthermore, models consistently fail at fine-grained physical dynamics, particularly during complex object manipulation and persistent world modeling. We hope Ego2Act provides a rigorous testbed for advancing video models toward physically plausible, goal-directed simulation.

Comments:	Preprint. 51 pages, 19 figures, 23 tables. Code, dataset and project website linked in the paper
Subjects:	Computer Vision and Pattern Recognition (cs.CV)
ACM classes:	I.2.10; I.4.8
Cite as:	arXiv:2610.01092 [cs.CV]
	(or arXiv:2610.01092v1 [cs.CV] for this version)
	https://doi.org/10.48550/arXiv.2610.01092 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Patrick Irawan [view email]
[v1] Thu, 1 Oct 2026 05:35:29 UTC (19,583 KB)