Unsupervised Learning of Object Deformation Models

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DensePose: Dense Human Pose Estimation in the Wild (2018, methodology): “More recently, [42] used the equivariance principle in order to align sets of images to a common coordinate system, while following the general idea of groupwise image alignment, e.g. [23, 21].”
DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild (2016, background): “Moving beyond discriminatively training, recent work (Thewlis et al, 2017) has explored how CNNs can be used for unsupervised non-rigid alignment of images, along the lines of earlier works on congealing (LearnedMiller, 2006; Kokkinos and Yuille, 2007).”
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance (2018, background): “Despite this challenge several works in the previous decade aimed at learning shape/appearance factorizations in an unsupervised manner, exploring groupwise image alignment, [11,12,13,14].”
Semantic part segmentation using compositional model combining shape and appearance (2014, background): “on for animals of various poses and viewpoints, which appears more challenging than landmark localization for faces in [35]. There are lots of works in the literature on modeling object shape such as [25, 14, 28, 19]. But they were only aimed at object-level detection or segmentation. Furthermore, n”