ClothFlow: A Flow-Based Model for Clothed Person Generation

By Xintong Han, Weilin Huang, Xiaojun Hu, Matthew R. Scott. IEEE International Conference on Computer Vision, 2019

307 citations recorded by the source corpus.

We present ClothFlow, an appearance-flow-based generative model to synthesize clothed person for posed-guided person image generation and virtual try-on. By estimating a dense flow between source and target clothing regions, ClothFlow effectively models the geometric changes and naturally transfers the appearance to synthesize novel images as shown in Figure 1. We achieve this with a three-stage framework: 1) Conditioned on a target pose, we first estimate a person semantic layout to provide richer guidance to the generation process. 2) Built on two feature pyramid networks, a cascaded flow estimation network then accurately estimates the appearance matching between corresponding clothing regions. The resulting dense flow warps the source image to flexibly account for deformations. 3) Finally, a generative network takes the warped clothing regions as inputs and renders the target view. We conduct extensive experiments on the DeepFashion dataset for pose-guided person image generation and on the VITON dataset for the virtual try-on task. Strong qualitative and quantitative results validate the effectiveness of our method.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/52f3c46066eb88ff2f0a3c7d48fe36206870f985

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In Computational Mechanics

J. Choo

In Computer Vision and Pattern Recognition

Citations in the corpus

Progressive One-shot Human Parsing (2020, unknown): “It has a broad range of applications especially in the fashion industry including fashion image generating (Han et al. 2019), virtual try-on (Dong et al.”
VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization (2021, methodology): “Also, several recent methods [9, 36, 35] add a module that generates segmentation maps and determine the person’s layout from the ﬁnal image in advance.”