Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance

ECCV 2018 Zhixin ShuMihir SahasrabudheAlp GulerDimitris SamarasNikos ParagiosIasonas Kokkinos

In this work we introduce Deforming Autoencoders, a generative model for images that disentangles shape from appearance in an unsupervised manner. As in the deformable template paradigm, shape is represented as a deformation between a canonical coordinate system (`template') and an observed image, while appearance is modeled in `canonical', template, coordinates, thus discarding variability due to deformations... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Unsupervised Facial Landmark Detection MAFL Deforming Autoencoders NME 5.45 # 6