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Dual-Branch Collaborative Transformer for Virtual Try-On

Abstract: Image-based virtual try-on has recently gained a lot of attention in both the scientific and fashion industry communities due to its challenging setting and practical real-world applications. While pure convolutional approaches have been explored to solve the task, Transformer-based architectures have not received significant attention yet. Following the intuition that self- and cross-attention operators can deal with long-range dependencies and hence improve the generation, in this paper we extend a Transformer-based virtual try-on model by adding a dual-branch collaborative module that can exploit cross-modal information at generation time. We perform experiments on the VITON dataset, which is the standard benchmark for the task, and on a recently collected virtual try-on dataset with multi-category clothing, Dress Code. Experimental results demonstrate the effectiveness of our solution over previous methods and show that Transformer-based architectures can be a viable alternative for virtual try-on.


Citation:

Fenocchi, Emanuele; Morelli, Davide; Cornia, Marcella; Baraldi, Lorenzo; Cesari, Fabio; Cucchiara, Rita "Dual-Branch Collaborative Transformer for Virtual Try-On" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, New Orleans, Louisiana, June 19-24, 2022, 2022

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