ZFlow: Gated Appearance Flow-Based Virtual Try-On With 3D Priors

Ayush Chopra, Rishabh Jain, Mayur Hemani, Balaji Krishnamurthy; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 5433-5442

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 benchmark image quality measures (PSNR, SSIM, and FID scores). The paper also presents extensive comparisons with existing state-of-the-art including a detailed user study and ablation studies to gauge the effectiveness of each of our contributions on multiple datasets