RESEARCH PAPER

Wow, wo, val! A Comprehensive Embodied World Model Evaluation Turing Test

Chun-Kai Fan; Xiaowei Chi; Xiaozhu Ju; Hao Li; Yong Bao; Yu-Kai Wang; Lizhang Chen; Zhiyuan Jiang; Kuangzhi Ge; Ying Li; Weishi Mi; Qingpo Wuwu; Peidong Jia; Yulin Luo; Kevin Zhang; Zhiyuan Qin; Yong Dai; Sirui Han; Yike Guo; Shanghang Zhang; Jian Tang

Classification

View four quadrants
Architecture
Not applicable
Prediction paradigm
Not applicable
Source review status
Not assigned

Category review. The work contributes curated embodied evaluation cases, automated/human video judgments and a separate IDM-based execution test for existing generators. The cataloged contribution is a dataset/data-generation method or evaluation/simulation resource, not the architecture of an evaluated or external policy. Reading evidence

AT A GLANCE

Contribution

WoW-World-Eval tests whether instruction-conditioned robot videos are visually convincing, task-correct, physically plausible and usable for action extraction. Its 609-sample benchmark combines automated metrics, human judgments and a separate GC-IDM robot-execution test. Hailuo leads the reported aggregate video score, while WoW-wan leads physical execution. The central lesson is that a plausible imagined manipulation and an executable one are distinct outcomes; calibration and evaluator dependence constrain how broadly the scores can be interpreted.

Abstract

An abstract has not been added yet.

Affiliations

State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University; Beijing Innovation Center of Humanoid Robotics; The Hong Kong University of Science and Technology