A conversation avatar that maps a speaker's facial actions and head pose to a listener's reactions, maps text to speaking expressions, and renders the results as realistic face images using Pix2PixHD.
Video synthesis of human upper body with realistic face
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abstract
This paper presents a generative adversarial learning-based human upper body video synthesis approach to generate an upper body video of target person that is consistent with the body motion, face expression, and pose of the person in source video. We use upper body keypoints, facial action units and poses as intermediate representations between source video and target video. Instead of directly transferring the source video to the target video, we firstly map the source person's facial action units and poses into the target person's facial landmarks, then combine the normalized upper body keypoints and generated facial landmarks with spatio-temporal smoothing to generate the corresponding target video's image. Experimental results demonstrated the effectiveness of our method.
fields
cs.CV 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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A Realistic Face-to-Face Conversation System based on Deep Neural Networks
A conversation avatar that maps a speaker's facial actions and head pose to a listener's reactions, maps text to speaking expressions, and renders the results as realistic face images using Pix2PixHD.