MMHU: A Massive-Scale Multimodal Benchmark for Human Behavior
  Understanding
Authors: name: Renjie Li, name: Ruijie Ye, name: Mingyang Wu, name: Hao Frank Yang, name: Zhiwen Fan, name: Hezhen Hu, name: Zhengzhong Tu

Humans are integral components of the transportation ecosystem, and
understanding their behaviors is crucial to facilitating the development of
safe driving systems. Although recent progress has explored various aspects of
human behaviorx2014such as motion, trajectories, and
intentionx2014a comprehensive benchmark for evaluating human
behavior understanding in autonomous driving remains unavailable. In this work,
we propose MMHU, a large-scale benchmark for human behavior analysis
featuring rich annotations, such as human motion and trajectories, text
description for human motions, human intention, and critical behavior labels
relevant to driving safety. Our dataset encompasses 57k human motion clips and
1.73M frames gathered from diverse sources, including established driving
datasets such as Waymo, in-the-wild videos from YouTube, and self-collected
data. A human-in-the-loop annotation pipeline is developed to generate rich
behavior captions. We provide a thorough dataset analysis and benchmark
multiple tasksx2014ranging from motion prediction to motion
generation and human behavior question answeringx2014thereby
offering a broad evaluation suite. Project page :
https://MMHU-Benchmark.github.io.