ZFlow: Gated Appearance Flow-based Virtual Try-on with 3D Priors

• URL: http://arxiv.org/abs/2109.07001v1
• Date: Tue, 14 Sep 2021 22:41:14 GMT
• Title: ZFlow: Gated Appearance Flow-based Virtual Try-on with 3D Priors
• Authors: Ayush Chopra, Rishabh Jain, Mayur Hemani, Balaji Krishnamurthy
• Abstract summary: Image-based virtual try-on involves synthesizing convincing images of a model wearing a particular garment. Recent methods involve a two stage process: i.) warping of the garment to align with the model ii. The lack of geometric information about the model or the garment often results in improper rendering of granular details. We propose ZFlow, an end-to-end framework, which seeks to alleviate these concerns.
• Score: 13.977100716044104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image-based virtual try-on involves synthesizing perceptually convincing images of a model wearing a particular garment and has garnered significant research interest due to its immense practical applicability. Recent methods involve a two stage process: i) warping of the garment to align with the model ii) texture fusion of the warped garment and target model to generate the try-on output. Issues arise due to the non-rigid nature of garments and the lack of geometric information about the model or the garment. It often results in improper rendering of granular details. We propose ZFlow, an end-to-end framework, which seeks to alleviate these concerns regarding geometric and textural integrity (such as pose, depth-ordering, skin and neckline reproduction) through a combination of gated aggregation of hierarchical flow estimates termed Gated Appearance Flow, and dense structural priors at various stage of the network. ZFlow achieves state-of-the-art results as observed qualitatively, and on quantitative benchmarks of image quality (PSNR, SSIM, and FID). The paper presents extensive comparisons with other existing solutions including a detailed user study and ablation studies to gauge the effect of each of our contributions on multiple datasets.

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