TriWorldBench: A Tri-View Consistency Perspective on Embodied World Models

Xuanyi Liu; Haofeng Wang; Ruiqi Li; Danni Yu; Rui Wan; Ruixu Zhang; Siyu Tao; Xue Yang; Shaofeng Zhang; Zicheng Zhang; Jiaqi Zhang; Siwei Ma

arXiv · 2609.26314

TriWorldBench: A Tri-View Consistency Perspective on Embodied World Models

Xuanyi Liu·Haofeng Wang·Ruiqi Li·Danni Yu·Rui Wan·Ruixu Zhang·Siyu Tao·Xue Yang·Shaofeng Zhang·Zicheng Zhang·Jiaqi Zhang·Siwei Ma

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Abstract

Embodied world models predict the outcomes of robot actions to support learning and planning. For robots equipped with head and wrist cameras, this requires complementary views: the head view captures the overall task, while wrist views reveal local gripper-object interactions. However, evaluating these views independently cannot determine whether they describe the same action and object state. We introduce TRIWORLDBENCH, a benchmark for evaluating embodied world models through synchronized head, left-wrist, and right-wrist videos. It contains 500 episodes across 50 bimanual manipulation tasks and uses 19 metrics to assess tri-view consistency, task alignment, physical and 3D coherence, motion quality, temporal consistency, and visual quality. By combining cross-view checks with measurements tailored to each camera, the benchmark evaluates whether plausible individual videos also form a consistent prediction of the intended task. We summarize overall performance with TWB-Score and retain per-view results to identify where predictions fail. This extends world-model evaluation beyond single-view visual quality. Code, data, and metric definitions are available at https://github.com/TriWorldBench/TriWorldBench.

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BibTeX RIS

Xuanyi Liu, Haofeng Wang, Ruiqi Li, Danni Yu, Rui Wan, Ruixu Zhang, Siyu Tao, Xue Yang, Shaofeng Zhang, Zicheng Zhang, Jiaqi Zhang, Siwei Ma. 2026-09-22. TriWorldBench: A Tri-View Consistency Perspective on Embodied World Models. https://arxiv.org/abs/2609.26314

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