Given a clothing image and a person image, an image-based virtual try-on aims to generate a customized image that appears natural and accurately reflects the character-istics of the clothing image. In this work, we aim to expand the applicability of the pre-trained diffusion model so that it can be utilized independently for the virtual try-on task. The main challenge is to preserve the clothing details while effectively utilizing the robust generative capability of the pre-trained model. In order to tackle these issues, we propose StableVITON, learning the semantic correspon-dence between the clothing and the human body within the latent space of the pre-trained diffusion model in an end-to-end manner. Our proposed zero cross-attention blocks not only preserve the clothing details by learning the semantic correspondence but also generate high-fidelity images by utilizing the inherent knowledge of the pre-trained model in the warping process. Through our proposed novel attention total variation loss and applying augmentation, we achieve the sharp attention map, resulting in a more precise representation of clothing details. Stable VITON out-performs the baselines in qualitative and quantitative evaluation, showing promising quality in arbitrary person images. Our code is available at https://github.com/rlawjdghek/StableVITON.

Recent advances in Neural Radiance Field (NeRF) show promising results in rendering realistic novel view images. However, NeRF and its variants assume that input images are captured using a pinhole camera and that subjects in images are always all-in-focus by tacit agreement. In this paper, we propose aperture-aware NeRF optimization and rendering methods using a thin-lens model (dubbed LensNeRF), which allows defocus images of any aperture size as input and output. To generalize a pinhole camera model to a thin-lens camera model in NeRF framework, we define multiple rays originating from the aperture area, solving world-to-pixel scale ambiguity. Also, we propose in-focus loss that assigns the given pixel color to points on the focus plane to alleviate the color ambiguity caused by the use of multiple rays. For the rigorous evaluation of the proposed method, we collect a real forward-facing dataset with different F-numbers for each viewpoint. Experimental results demonstrate that our method successfully fuses an aperture-size adjustable thin-lens camera model into the NeRF architecture, showing favorable qualitative and quantitative results compared to baseline models. The dataset will be made available.

The hair and beauty industry is a fast-growing industry. This led to the development of various applications, such as virtual hair dyeing or hairstyle translations, to satisfy the customer needs. Although several hairstyle datasets are available for these applications, they often consist of a relatively small number of images with low resolution, thus limiting their performance on high-quality hair editing. In response, we introduce a novel large-scale Korean hairstyle dataset, K-hairstyle, containing 500,000 high-resolution images. In addition, K-hairstyle includes various hair attributes annotated by Korean expert hairstylists as well as hair segmentation masks. We validate the effectiveness of our dataset via several applications, such as hair dyeing, hairstyle translation, and hairstyle classification. K-hairstyle is publicly available at https://psh01087.github.io/K-Hairstyle/.