Understanding how humans interact with objects is key to building robust human-centric artificial intelligence. However, this area remains relatively unexplored due to the lack of large-scale datasets. Recent datasets focusing on this issue mainly consist of activities captured entirely in controlled lab environments, and contact annotations are mostly estimated using threshold clips. We introduce Contact4D, a multi-view video dataset for human-object interaction that provides detailed body poses and accurate contact annotations. We use a flexible multi-view capture system to record individuals performing furniture assembly tasks and provide annotations for human detection, tracking, 2D/3D pose estimation, and ground-truth contact. Additionally, we propose a novel processing pipeline to extract accurate hand poses even when they are severely occluded. Contact4D consists of 2M images captured from 19 synchronized cameras across 350 video sequences, spanning diverse environments, varioius furniture types, and unique subjects. We evaluate existing methods for human pose estimation and human-centric contact estimation, demonstrating their inability to generalize to our dataset. Lastly, we fine-tune a pretrained MultiHMR model on Contact4D and observe an improved performance of 56.6% body MPJPE and 26.4% hand MPJPE in scenarios under severe self-occlusion and object occlusion.
