FaceDiffuser: Speech-Driven 3D Facial Animation Synthesis Using
  Diffusion
Stefan Stan Kazi Injamamul Haque Zerrin Yumak
Speech-driven 3D facial animation synthesis has been a challenging task both
in industry and research. Recent methods mostly focus on deterministic deep
learning methods meaning that given a speech input, the output is always the
same. However, in reality, the non-verbal facial cues that reside throughout
the face are non-deterministic in nature. In addition, majority of the
approaches focus on 3D vertex based datasets and methods that are compatible
with existing facial animation pipelines with rigged characters is scarce. To
eliminate these issues, we present FaceDiffuser, a non-deterministic deep
learning model to generate speech-driven facial animations that is trained with
both 3D vertex and blendshape based datasets. Our method is based on the
diffusion technique and uses the pre-trained large speech representation model
HuBERT to encode the audio input. To the best of our knowledge, we are the
first to employ the diffusion method for the task of speech-driven 3D facial
animation synthesis. We have run extensive objective and subjective analyses
and show that our approach achieves better or comparable results in comparison
to the state-of-the-art methods. We also introduce a new in-house dataset that
is based on a blendshape based rigged character. We recommend watching the
accompanying supplementary video. The code and the dataset will be publicly
available.