Manifold preserving edit propagation

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Rewriting a Deep Generative Model (2020, background): “Edit transfer methods propagate pixel edits to corresponding locations in other images of the same object or adjacent frames in the same video [2,77,28,14,13,81,21].”
Real-time user-guided image colorization with learned deep priors (2017, background): “Learning machinery, such as boosting (Li et al. 2008), local linear embeddings (Chen et al. 2012), feature discrimination (Xu et al. 2013), and more recently, neural networks (Endo et al. 2016), have been proposed to automatically learn similarity between pixels given user strokes and input images.”
DeepProp: Extracting Deep Features from a Single Image for Edit Propagation (2016, methodology): “Second, interpolation can alleviate noise and halo artifacts along object boundaries [CZZT12, XYJ13].”
Let there be color! (2016, background): “…as global optimization with all-pair constraints [An and Pel-lacini 2008; Xu et al. 2009], Radial Basis Function interpolation [Li et al. 2010], manifold learning [Chen et al. 2012], and stochastic modeling of appearance similarities between user-speciﬁed pixels and other pixels [Xu et al. 2013].”
Sparse Dictionary Learning for Edit Propagation of High-Resolution Images (2014, methodology): “[6] proposed a manifold preserving edit propagation method which maintains the relative relationship between pixels by using locally linear embedding during edit propagation.”