GarmentGAN: Photo-realistic Adversarial Fashion Transfer

• URL: http://arxiv.org/abs/2003.01894v1
• Date: Wed, 4 Mar 2020 05:01:15 GMT
• Title: GarmentGAN: Photo-realistic Adversarial Fashion Transfer
• Authors: Amir Hossein Raffiee, Michael Sollami
• Abstract summary: GarmentGAN performs image-based garment transfer through generative adversarial methods. The framework allows users to virtually try-on items before purchase and generalizes to various apparel types.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The garment transfer problem comprises two tasks: learning to separate a person's body (pose, shape, color) from their clothing (garment type, shape, style) and then generating new images of the wearer dressed in arbitrary garments. We present GarmentGAN, a new algorithm that performs image-based garment transfer through generative adversarial methods. The GarmentGAN framework allows users to virtually try-on items before purchase and generalizes to various apparel types. GarmentGAN requires as input only two images, namely, a picture of the target fashion item and an image containing the customer. The output is a synthetic image wherein the customer is wearing the target apparel. In order to make the generated image look photo-realistic, we employ the use of novel generative adversarial techniques. GarmentGAN improves on existing methods in the realism of generated imagery and solves various problems related to self-occlusions. Our proposed model incorporates additional information during training, utilizing both segmentation maps and body key-point information. We show qualitative and quantitative comparisons to several other networks to demonstrate the effectiveness of this technique.

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