In this paper, the authors present a cosmetic-specific skin image dataset, which is a valuable contribution to the field of cosmetic rendering and image-to-image translation. The dataset consists of skin images from 45 patches, with 5 skin patches each from 9 participants. The size of each patch is 8mm x 8mm. These patches were captured using a novel capturing device inspired by Light Stage.
The use of a specialized capturing device is a significant improvement over existing methods for capturing skin images. By capturing over 600 images of each skin patch under diverse lighting conditions in just 30 seconds, the authors have been able to create a comprehensive dataset that captures the nuances of cosmetic products on different skin types and under various lighting conditions.
One of the strengths of this dataset is its focus on specific cosmetic products, namely foundation, blusher, and highlighter. This allows researchers and practitioners in the cosmetics industry to have a more targeted approach when it comes to analyzing and developing new rendering techniques for these particular products.
The authors then demonstrate the viability of the dataset by using it in an image-to-image translation-based pipeline for cosmetic rendering. This approach shows promise in accurately rendering how different cosmetic products would appear on different skin types. By comparing their data-driven approach to an existing cosmetic rendering method, the authors clearly demonstrate the advantages and improved results that can be achieved by using their dataset.
Overall, this paper provides a valuable resource for researchers and practitioners in the field of cosmetics. The dataset and the image-to-image translation pipeline introduce new possibilities for cosmetic rendering and provide a solid foundation for future research in this area. Furthermore, with the rapid growth of the cosmetics industry, datasets like these will be crucial in ensuring that digital representations of cosmetic products accurately reflect their real-world appearance on various skin types.