High‐Resolution Neural Face Swapping for Visual Effects
Jacek Naruniec , L. Helminger , C. Schroers , Robert Weber
AbstractIn this paper, we propose an algorithm for fully automatic neural face swapping in images and videos. To the best of our knowledge, this is the ﬁrst method capable of rendering photo-realistic and temporally coherent results at megapixel resolution. To this end, we introduce a progressively trained multi-way comb network and a light- and contrast-preserving blending method. We also show that while progressive training enables generation of high-resolution images, extending the architecture and training data beyond two people allows us to achieve higher ﬁdelity in generated expressions. When compositing the generated expression onto the target face, we show how to adapt the blending strategy to preserve contrast and low-frequency lighting. Finally, we incorporate a reﬁnement strategy into the face landmark stabilization algorithm to achieve temporal stability, which is crucial for working with high-resolution videos. We conduct an extensive ablation study to show the inﬂuence of our design choices on the quality of the swap and compare our work with popular state-of-the-art methods.
|Journal series||Computer Graphics Forum, ISSN 0167-7055, e-ISSN 1467-8659|
|Publication size in sheets||0.55|
|Score||= 100.0, 19-08-2020, ArticleFromJournal|
|Publication indicators||: 2018 = 1.567; : 2018 = 2.373 (2) - 2018=2.46 (5)|
|Citation count*||1 (2020-09-12)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.