Recursive Chaining of Reversible Image-to-Image Translators for Face Aging
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
|Title of host publication||Advanced Concepts for Intelligent Vision Systems - 19th International Conference, ACIVS 2018, Proceedings|
|Publication status||Published - 1 Jan 2018|
|MoE publication type||A4 Article in a conference publication|
|Event||International Conference on Advanced Concepts for Intelligent Vision Systems - Poitiers, France|
Duration: 24 Sep 2018 → 27 Sep 2018
Conference number: 19
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||International Conference on Advanced Concepts for Intelligent Vision Systems|
|Period||24/09/2018 → 27/09/2018|
- GenMind Ltd.
This paper addresses the modeling and simulation of progressive changes over time, such as human face aging. By treating the age phases as a sequence of image domains, we construct a chain of transformers that map images from one age domain to the next. Leveraging recent adversarial image translation methods, our approach requires no training samples of the same individual at different ages. Here, the model must be flexible enough to translate a child face to a young adult, and all the way through the adulthood to old age. We find that some transformers in the chain can be recursively applied on their own output to cover multiple phases, compressing the chain. The structure of the chain also unearths information about the underlying physical process. We demonstrate the performance of our method with precise and intuitive metrics, and visually match with the face aging state-of-the-art.
- Deep learning, Face aging, Face synthesis, GAN, Transfer learning