Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion

Tero Karras, Timo Aila, Samuli Laine, Antti Herva, Jaakko Lehtinen

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

54 Sitaatiot (Scopus)

Abstrakti

We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone. During inference, the latent code can be used as an intuitive control for the emotional state of the face puppet.

We train our network with 3--5 minutes of high-quality animation data obtained using traditional, vision-based performance capture methods. Even though our primary goal is to model the speaking style of a single actor, our model yields reasonable results even when driven with audio from other speakers with different gender, accent, or language, as we demonstrate with a user study. The results are applicable to in-game dialogue, low-cost localization, virtual reality avatars, and telepresence.
AlkuperäiskieliEnglanti
Artikkeli94
Sivut1-12
JulkaisuACM Transactions on Graphics
Vuosikerta36
Numero4
DOI - pysyväislinkit
TilaJulkaistu - heinäkuuta 2017
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

Sormenjälki Sukella tutkimusaiheisiin 'Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

  • Siteeraa tätä