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arXiv · 2004.12000

Neural Head Reenactment with Latent Pose Descriptors

Egor Burkov·Igor Pasechnik·Artur Grigorev·Victor Lempitsky
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Abstract

We propose a neural head reenactment system, which is driven by a latent pose representation and is capable of predicting the foreground segmentation alongside the RGB image. The latent pose representation is learned as a part of the entire reenactment system, and the learning process is based solely on image reconstruction losses. We show that despite its simplicity, with a large and diverse enough training dataset, such learning successfully decomposes pose from identity. The resulting system can then reproduce mimics of the driving person and, furthermore, can perform cross-person reenactment. Additionally, we show that the learned descriptors are useful for other pose-related tasks, such as keypoint prediction and pose-based retrieval.

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Egor Burkov, Igor Pasechnik, Artur Grigorev, Victor Lempitsky. 2020-04-24. Neural Head Reenactment with Latent Pose Descriptors. https://doi.org/10.1109/cvpr42600.2020.01380

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