  This paper describes the ReprGesture entry to the Generation and Evaluation
of Non-verbal Behaviour for Embodied Agents (GENEA) challenge 2022. The GENEA
challenge provides the processed datasets and performs crowdsourced evaluations
to compare the performance of different gesture generation systems. In this
paper, we explore an automatic gesture generation system based on multimodal
representation learning. We use WavLM features for audio, FastText features for
text and position and rotation matrix features for gesture. Each modality is
projected to two distinct subspaces: modality-invariant and modality-specific.
To learn inter-modality-invariant commonalities and capture the characters of
modality-specific representations, gradient reversal layer based adversarial
classifier and modality reconstruction decoders are used during training. The
gesture decoder generates proper gestures using all representations and
features related to the rhythm in the audio. Our code, pre-trained models and
demo are available at https://github.com/YoungSeng/ReprGesture.

Human-Computer Interaction;Artificial Intelligence;Multimedia;Sound;Audio and Speech Processing