  Understanding comprehensive assembly knowledge from videos is critical for
futuristic ultra-intelligent industry. To enable technological breakthrough, we
present HA-ViD - the first human assembly video dataset that features
representative industrial assembly scenarios, natural procedural knowledge
acquisition process, and consistent human-robot shared annotations.
Specifically, HA-ViD captures diverse collaboration patterns of real-world
assembly, natural human behaviors and learning progression during assembly, and
granulate action annotations to subject, action verb, manipulated object,
target object, and tool. We provide 3222 multi-view, multi-modality videos
(each video contains one assembly task), 1.5M frames, 96K temporal labels and
2M spatial labels. We benchmark four foundational video understanding tasks:
action recognition, action segmentation, object detection and multi-object
tracking. Importantly, we analyze their performance for comprehending knowledge
in assembly progress, process efficiency, task collaboration, skill parameters
and human intention. Details of HA-ViD is available at:
https://iai-hrc.github.io/ha-vid.
