  The availability of a large labeled dataset is a key requirement for applying
deep learning methods to solve various computer vision tasks. In the context of
understanding human activities, existing public datasets, while large in size,
are often limited to a single RGB camera and provide only per-frame or per-clip
action annotations. To enable richer analysis and understanding of human
activities, we introduce IKEA ASM -- a three million frame, multi-view,
furniture assembly video dataset that includes depth, atomic actions, object
segmentation, and human pose. Additionally, we benchmark prominent methods for
video action recognition, object segmentation and human pose estimation tasks
on this challenging dataset. The dataset enables the development of holistic
methods, which integrate multi-modal and multi-view data to better perform on
these tasks.
