WorldArena: A Unified Benchmark for Evaluating Perception and Functional Utility of Embodied World Models
Yu Shang, Zhuohang Li, Yiding Ma, Weikang Su, Xin Jin, Ziyou Wang, Lei Jin, Xin Zhang, Yinzhou Tang, Haisheng Su, Chen Gao, Wei Wu, Xihui Liu, Dhruv Shah, Zhaoxiang Zhang, Zhibo Chen, Jun Zhu, Yonghong Tian, Tat-Seng Chua, Wenwu Zhu, Yong Li

TL;DR
WorldArena introduces a comprehensive benchmark for evaluating embodied world models across perceptual and functional aspects, revealing gaps between visual quality and task performance, and providing a unified metric and public leaderboard.
Contribution
The paper presents WorldArena, a unified benchmark with multi-dimensional evaluation metrics and a holistic score, addressing the fragmented evaluation of embodied world models.
Findings
High perception quality does not guarantee strong task performance.
The benchmark reveals a significant perception-functionality gap.
Extensive experiments on 14 models demonstrate the framework's effectiveness.
Abstract
While world models have emerged as a cornerstone of embodied intelligence by enabling agents to reason about environmental dynamics through action-conditioned prediction, their evaluation remains fragmented. Current evaluation of embodied world models has largely focused on perceptual fidelity (e.g., video generation quality), overlooking the functional utility of these models in downstream decision-making tasks. In this work, we introduce WorldArena, a unified benchmark designed to systematically evaluate embodied world models across both perceptual and functional dimensions. WorldArena assesses models through three dimensions: video perception quality, measured with 16 metrics across six sub-dimensions; embodied task functionality, which evaluates world models as data engines, policy evaluators, and action planners integrating with subjective human evaluation. Furthermore, we propose…
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Taxonomy
TopicsHuman Pose and Action Recognition · Action Observation and Synchronization · Social Robot Interaction and HRI
