Abstract: We propose a novel approach to convert given speech audio to a photo-realistic speaking video of a specific person, where the output video has synchronized, realistic, and expressive rich body dynamics. We achieve this by first generating 3D skeleton movements from the audio sequence using a recurrent neural network (RNN), and then synthesizing the output video via a conditional generative adversarial network (GAN). To make the skeleton movement realistic and expressive, we embed the knowledge of an articulated 3D human skeleton and a learned dictionary of personal speech iconic gestures into the generation process in both learning and testing pipelines. The former prevents the generation of unreasonable body distortion, while the later helps our model quickly learn meaningful body movement through a few recorded videos. To produce photo-realistic and high-resolution video with motion details, we propose to insert part attention mechanisms in the conditional GAN, where each detailed part, e.g. head and hand, is automatically zoomed in to have their own discriminators.

Publication

Speech2Video Synthesis with 3D Skeleton Regularization and Expressive Body Poses. 

Miao Liao*, Sibo Zhang*, Peng Wang, Hao Zhu, Xinxin Zuo, Ruigang Yang.

Asian Conference on Computer Vision (ACCV 2020). 

[PDF] [Result Video] [1 min Spotlight] [10 min Presentation] [Github]

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语音驱动3D虚拟人，百度ACCV 2020最新文章解读