  Current talking avatars mostly generate co-speech gestures based on audio and
text of the utterance, without considering the non-speaking motion of the
speaker. Furthermore, previous works on co-speech gesture generation have
designed network structures based on individual gesture datasets, which results
in limited data volume, compromised generalizability, and restricted speaker
movements. To tackle these issues, we introduce FreeTalker, which, to the best
of our knowledge, is the first framework for the generation of both spontaneous
(e.g., co-speech gesture) and non-spontaneous (e.g., moving around the podium)
speaker motions. Specifically, we train a diffusion-based model for speaker
motion generation that employs unified representations of both speech-driven
gestures and text-driven motions, utilizing heterogeneous data sourced from
various motion datasets. During inference, we utilize classifier-free guidance
to highly control the style in the clips. Additionally, to create smooth
transitions between clips, we utilize DoubleTake, a method that leverages a
generative prior and ensures seamless motion blending. Extensive experiments
show that our method generates natural and controllable speaker movements. Our
code, model, and demo are are available at
\url{https://youngseng.github.io/FreeTalker/}.
