Speech2Video Synthesis with 3D Skeleton Regularization and Expressive Body Poses
This work addresses the challenge of creating realistic and synchronized body dynamics in speech-to-video synthesis, which is incremental by building on existing GAN and RNN methods with specific enhancements for personal gestures and part attention.
The paper tackles the problem of generating photo-realistic speaking videos from speech audio by using 3D skeleton regularization and expressive body poses, achieving better results than previous state-of-the-art methods as validated by a user study.
In this paper, 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. To validate our approach, we collect a dataset with 20 high-quality videos from 1 male and 1 female model reading various documents under different topics. Compared with previous SoTA pipelines handling similar tasks, our approach achieves better results by a user study.