Audio is all in one: speech-driven gesture synthetics using WavLM pre-trained model
arxiv(2023)
Abstract
The generation of co-speech gestures for digital humans is an emerging area
in the field of virtual human creation. Prior research has made progress by
using acoustic and semantic information as input and adopting classify method
to identify the person's ID and emotion for driving co-speech gesture
generation. However, this endeavour still faces significant challenges. These
challenges go beyond the intricate interplay between co-speech gestures, speech
acoustic, and semantics; they also encompass the complexities associated with
personality, emotion, and other obscure but important factors. This paper
introduces "diffmotion-v2," a speech-conditional diffusion-based and
non-autoregressive transformer-based generative model with WavLM pre-trained
model. It can produce individual and stylized full-body co-speech gestures only
using raw speech audio, eliminating the need for complex multimodal processing
and manually annotated. Firstly, considering that speech audio not only
contains acoustic and semantic features but also conveys personality traits,
emotions, and more subtle information related to accompanying gestures, we
pioneer the adaptation of WavLM, a large-scale pre-trained model, to extract
low-level and high-level audio information. Secondly, we introduce an adaptive
layer norm architecture in the transformer-based layer to learn the
relationship between speech information and accompanying gestures. Extensive
subjective evaluation experiments are conducted on the Trinity, ZEGGS, and BEAT
datasets to confirm the WavLM and the model's ability to synthesize natural
co-speech gestures with various styles.
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