A Generative Model of Worldwide Facial Appearance
By Zachary Bessinger, Nathan Jacobs. 2019
13 citations recorded by the source corpus.
Human appearance depends on many proximate factors, including age, gender, ethnicity, and personal style choices. In this work, we model the relationship between human appearance and geographic location, which can impact these factors in complex ways. We propose GPS2Face, a dual-component generative network architecture that enables flexible facial generation with fine-grained control of latent factors. We use facial landmarks as a guide to synthesize likely faces for locations around in the world. We train our model on a large-scale dataset of geotagged faces and evaluate our proposed model, both qualitatively and quantitatively, against previous work.
Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology
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In Computer Vision and Pattern Recognition
- John R. Smith
- Rogério Schmidt Feris
- Michele Merler
- Nalini K. Ratha
- Kristen Grauman
- Jungseock Joo
- Kimmo Kärkkäinen
- Yun Raymond Fu
Citations in the corpus
- Diversity in Faces (2019, background): “We expect that DiF will be valuable to steer these generative methods towards gaps and blind spots in the training data, for example, to assist methods that automatically generate faces corresponding to a specific geographical area [79].”