Encode-in-Style: Latent-based Video Encoding using StyleGAN2

• URL: http://arxiv.org/abs/2203.14512v1
• Date: Mon, 28 Mar 2022 05:44:19 GMT
• Title: Encode-in-Style: Latent-based Video Encoding using StyleGAN2
• Authors: Trevine Oorloff, Yaser Yacoob
• Abstract summary: We propose an end-to-end facial video encoding approach that facilitates data-efficient high-quality video re-synthesis. The approach builds on StyleGAN2 image inversion and multi-stage non-linear latent-space editing to generate videos that are nearly comparable to input videos.
• Score: 0.7614628596146599
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose an end-to-end facial video encoding approach that facilitates data-efficient high-quality video re-synthesis by optimizing low-dimensional edits of a single Identity-latent. The approach builds on StyleGAN2 image inversion and multi-stage non-linear latent-space editing to generate videos that are nearly comparable to input videos. It economically captures face identity, head-pose, and complex facial motions at fine levels, and thereby bypasses training and person modeling which tend to hamper many re-synthesis approaches. The approach is designed with maximum data efficiency, where a single W+ latent and 35 parameters per frame enable high-fidelity video rendering. This pipeline can also be used for puppeteering (i.e., motion transfer).

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