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We present a high-precision real-time facial animation pipeline suitable for animators to use on their desktops. This pipeline is about to be launched in FACEGOOD's Avatary\footnote{https://www.avatary.com/} software, which will accelerate animators' productivity. The pipeline differs from professional head-mounted facial capture solutions in that it only requires the use of a consumer-grade 3D camera on the desk to achieve high-precision real-time facial capture. The system enables animators to create high-quality facial animations with ease and speed, while reducing the cost and complexity of traditional facial capture solutions. Our approach has the potential to revolutionize the way facial animation is done in the entertainment industry., Comment: 7pages, 5 figures

Audio-Driven Face Animation is an eagerly anticipated technique for applications such as VR/AR, games, and movie making. With the rapid development of 3D engines, there is an increasing demand for driving 3D faces with audio. However, currently available 3D face animation datasets are either scale-limited or quality-unsatisfied, which hampers further developments of audio-driven 3D face animation. To address this challenge, we propose MMFace4D, a large-scale multi-modal 4D (3D sequence) face dataset consisting of 431 identities, 35,904 sequences, and 3.9 million frames. MMFace4D exhibits two compelling characteristics: 1) a remarkably diverse set of subjects and corpus, encompassing actors spanning ages 15 to 68, and recorded sentences with durations ranging from 0.7 to 11.4 seconds. 2) It features synchronized audio and 3D mesh sequences with high-resolution face details. To capture the subtle nuances of 3D facial expressions, we leverage three synchronized RGBD cameras during the recording process. Upon MMFace4D, we construct a non-autoregressive framework for audio-driven 3D face animation. Our framework considers the regional and composite natures of facial animations, and surpasses contemporary state-of-the-art approaches both qualitatively and quantitatively. The code, model, and dataset will be publicly available., Comment: 10 pages, 8 figures. This paper has been submitted to IEEE Transaction on MultiMedia, which is the extension of our MM2023 paper arXiv:2308.05428. The dataset is now publicly available, see Project page at https://wuhaozhe.github.io/mmface4d/