## **Track 1 (micro-gesture classification) description**  
In this track, we conduct skeleton-based micro-gesture classification from short video clips. The MG datasets were collected from in-the-wild settings (tennis players having postmatch interviews). Compared to ordinary action/gesture data, MGs concern more fine-grained and subtle body movements that occur spontaneously in practical interactions. Thus, learning those fine-grained body movement patterns, handling imbalanced sample distribution of MGs, and distinguishing the high heterogeneous MG samples of interclass are the big challenges to be addressed. In this year's challenge we also provide complementary RGB data and encourage competitors to find multi-modal solutions that leverage both skeleton and RGB.