Researchers improve virtual try-on methods by using a new dataset to choose target models and train specialized warpers, enhancing realism and accuracy.
Authors: Kedan Li, University of Illinois at Urbana-Champaign; Min Jin Chong, University of Illinois at Urbana-Champaign; Jingen Liu, JD AI Research; David Forsyth, University of Illinois at Urbana-Champaign. Table of Links Abstract and Intro Related Work Proposed Method Experiments Conclusions and References 2. Related Work Image synthesis: Spatial transformer networks estimate geometric transformations using neural networks . Subsequent work shows how to warp one object to another.
uses a U-Net to generate a coarse synthesis and a mask on the model where the product is presented. A mapping from the product mask to the on-model mask is learned through Thin plate spline transformation . The learned mapping is applied on the product image to create a warp. Following their work, Wang et al. improved the architecture using a Geometric Matching Module to estimate the TPS transformations parameters directly from pairs of product image and target person.
extends the work by incorporatiing body segments prediction and later works follow similar procedure . However, TPS transformation fails to produce reasonable warps, due to the noisiness of generated masks in our dataset, as shown in Figure 6 right. Instead, we adopt affine transformations which we have found to be more robust to imperfections instead of TPS transformation. A group of following work extended the task to multi-pose.
uses a U-Net to generate a coarse synthesis and a mask on the model where the product is presented. A mapping from the product mask to the on-model mask is learned through Thin plate spline transformation . The learned mapping is applied on the product image to create a warp. Following their work, Wang et al. improved the architecture using a Geometric Matching Module to estimate the TPS transformations parameters directly from pairs of product image and target person.
extends the work by incorporatiing body segments prediction and later works follow similar procedure . However, TPS transformation fails to produce reasonable warps, due to the noisiness of generated masks in our dataset, as shown in Figure 6 right. Instead, we adopt affine transformations which we have found to be more robust to imperfections instead of TPS transformation. A group of following work extended the task to multi-pose.
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