  This work tackles the problem of temporally coherent face anonymization in
natural video streams.We propose JaGAN, a two-stage system starting with
detecting and masking out faces with black image patches in all individual
frames of the video. The second stage leverages a privacy-preserving Video
Generative Adversarial Network designed to inpaint the missing image patches
with artificially generated faces. Our initial experiments reveal that image
based generative models are not capable of inpainting patches showing temporal
coherent appearance across neighboring video frames. To address this issue we
introduce a newly curated video collection, which is made publicly available
for the research community along with this paper. We also introduce the
Identity Invariance Score IdI as a means to quantify temporal coherency between
neighboring frames.
