Brand Label Albedo Extraction of eCommerce Products using Generative Adversarial Network
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Abstract (arXiv)
In this paper we present our solution to extract albedo of branded labels for e-commerce products. To this end, we generate a large-scale photo-realistic synthetic data set for albedo extraction followed by training a generative model to translate images with diverse lighting conditions to albedo. We performed an extensive evaluation to test the generalisation of our method to in-the-wild images. From the experimental results, we observe that our solution generalises well compared to the existing method both in the unseen rendered images as well as in the wild image.
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References · 7
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,656
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,000
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,516
- 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
- 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 650
- 2018Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Modelcited by 28
- 2016DARN: a Deep Adversial Residual Network for Intrinsic Image Decompositioncited by 4
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