MakeItTalk: Speaker-Aware Talking-Head Animation
ACM Transactions on Graphics (Proc. ACM SIGGRAPH ASIA 2020)
We present a method that generates expressive talking-head videos from a single facial image with audio as the only input. In contrast to previous attempts to learn direct mappings from audio to raw pixels for creating talking faces, our method first disentangles the content and speaker information in the input audio signal. The audio content robustly controls the motion of lips and nearby facial regions, while the speaker information determines the specifics of facial expressions and the rest of the talking-head dynamics. Another key component of our method is the prediction of facial landmarks reflecting the speaker-aware dynamics. Based on this intermediate representation, our method works with many portrait images in a single unified framework, including artistic paintings, sketches, 2D cartoon characters, Japanese mangas, and stylized caricatures. In addition, our method generalizes well for faces and characters that were not observed during training. We present extensive quantitative and qualitative evaluation of our method, in addition to user studies, demonstrating generated talking-heads of significantly higher quality compared to prior state-of-the-art methods.
Pipeline of MakeItTalk. Given an input audio signal along with a single portrait image (cartoon or real photo), our method animates the portrait in a speaker-aware fashion driven by disentangled content and speaker embeddings. The animation is driven by intermediate predictions of 3D landmark displacements.
Yang Zhou, Xintong Han, Eli Shechtman, Jose Echevarria, Evangelos Kalogerakis, Dingzeyu Li, "MakeItTalk: Speaker-Aware Talking-Head Animation", ACM Transactions on Graphics (Proc. ACM SIGGRAPH ASIA 2020) Bibtex
Source Code & Data
We would like to thank Timothy Langlois for the narration, and Kaizhi Qian for the help with the voice conversion module. We thank Daichi Ito for sharing the caricature image and Dave Werner for Wilk, the gruff but ultimately lovable puppet. We also thank the anonymous reviewers for their constructive comments and suggestions. This research is partially funded by NSF (EAGER-1942069) and a gift from Adobe. Our experiments were performed in the UMass GPU cluster obtained under the Collaborative Fund managed by the MassTech Collaborative.