  In this technical report, we present our findings from the research conducted
on the Human-Object Interaction 4D (HOI4D) dataset for egocentric action
segmentation task. As a relatively novel research area, point cloud video
methods might not be good at temporal modeling, especially for long point cloud
videos (\eg, 150 frames). In contrast, traditional video understanding methods
have been well developed. Their effectiveness on temporal modeling has been
widely verified on many large scale video datasets. Therefore, we convert point
cloud videos into depth videos and employ traditional video modeling methods to
improve 4D action segmentation. By ensembling depth and point cloud video
methods, the accuracy is significantly improved. The proposed method, named
Mixture of Depth and Point cloud video experts (DPMix), achieved the first
place in the 4D Action Segmentation Track of the HOI4D Challenge 2023.
