Towards photographic image manipulation with balanced growing of generative autoencoders

Ari Heljakka, Arno Solin, Juho Kannala

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

10 Citations (Scopus)


We present a generative autoencoder that provides fast encoding, faithful reconstructions (e.g. retaining the identity of a face), sharp generated/reconstructed samples in high resolutions, and a well-structured latent space that supports semantic manipulation of the inputs. There are no current autoencoder or GAN models that satisfactorily achieve all of these. We build on the progressively growing autoencoder model PIONEER , for which we completely alter the training dynamics based on a careful analysis of recently introduced normalization schemes. We show significantly improved visual and quantitative results for face identity conservation in CELEBA-HQ. Our model achieves state-of-the-art disentanglement of latent space, both quantitatively and via realistic image attribute manipulations. On the LSUN Bedrooms dataset, we improve the disentanglement performance of the vanilla PIONEER, despite having a simpler model. Overall, our results indicate that the PIONEER networks provide a way towards photorealistic face manipulation.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020
Number of pages10
ISBN (Electronic)9781728165530
Publication statusPublished - Mar 2020
MoE publication typeA4 Conference publication
EventIEEE Winter Conference on Applications of Computer Vision - Snowmass Village, United States
Duration: 1 Mar 20205 Mar 2020


ConferenceIEEE Winter Conference on Applications of Computer Vision
Abbreviated titleWACV
Country/TerritoryUnited States
CitySnowmass Village


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