EWMBench: Evaluating Scene, Motion, and Semantic Quality in Embodied
  World Models
Authors: name: Hu Yue, name: Siyuan Huang, name: Yue Liao, name: Shengcong Chen, name: Pengfei Zhou, name: Liliang Chen, name: Maoqing Yao, name: Guanghui Ren

Recent advances in creative AI have enabled the synthesis of high-fidelity
images and videos conditioned on language instructions. Building on these
developments, text-to-video diffusion models have evolved into embodied world
models (EWMs) capable of generating physically plausible scenes from language
commands, effectively bridging vision and action in embodied AI applications.
This work addresses the critical challenge of evaluating EWMs beyond general
perceptual metrics to ensure the generation of physically grounded and
action-consistent behaviors. We propose the Embodied World Model Benchmark
(EWMBench), a dedicated framework designed to evaluate EWMs based on three key
aspects: visual scene consistency, motion correctness, and semantic alignment.
Our approach leverages a meticulously curated dataset encompassing diverse
scenes and motion patterns, alongside a comprehensive multi-dimensional
evaluation toolkit, to assess and compare candidate models. The proposed
benchmark not only identifies the limitations of existing video generation
models in meeting the unique requirements of embodied tasks but also provides
valuable insights to guide future advancements in the field. The dataset and
evaluation tools are publicly available at
https://github.com/AgibotTech/EWMBench.