SingingHead: A Large-Scale 4D Dataset for Singing Head Animation

Singing, as a common facial movement second only to talking, can be regarded as a universal language across ethnicities and cultures, plays an important role in emotional communication, art, and entertainment. However, it is often overlooked in the field of audio-driven 3D facial animation due to the lack of singing head datasets and the domain gap between singing and talking in rhythm and amplitude. To this end, we collect a large-scale high-quality multi-modal singing head dataset,SingingHead, which consists of more than 27 hours of synchronized singing video, 3D facial motion, singing audio, and background music from 76 individuals and 8 types of music. Along with the SingingHead dataset, we benchmark existing audio-driven 3D facial animation methods and 2D talking head methods on the singing task. Existing 3D facial animation methods and 2D talking head methods fail to produce satisfactory singing results. Focusing on the 3D singing head animation, we first utilize the proposed singing-specific dataset to retrain the 3D facial animation methods, resulting in substantial performance improvements. Besides, considering the absence of background music and the slow generation speed of existing methods, we propose a simple but efficient non-autoregressive VAE-based framework with background music as an input signal to generate diverse and accurate 3D singing facial motions in real time. Extensive experiments demonstrate the significance of the SingingHead dataset in promoting the development of singing head animation. The dataset is released for research purposes at:https://wsj-sjtu.github.io/SingingHead/.