Preserving Communication Context. Virtual workspace and interpersonal space in Japanese CSCW.

The past decade has seen the development of a perspective\ud holding that technology is socially constructed (Mackenzie and Wacjman, 1985; Bijker, Hughes and Pinch, 1987; Bijker and Law, 1992). This paper examines the social construction of one group of technologies, systems for computer supported cooperative work (CSCW). It describes the design of CSCW in Japan, with particular attention to the influence of culture on the design process. Two case studies are presented to illustrate the argument that culture is an important factor in technology design, despite commonly held assumptions about the neutrality and objectivity of science and technology. The paper further argues that, by looking at\ud CSCW systems as texts which reflect the context of their production and the society from which they come, we may be better able to understand the transformations that operate when these texts are “read” in the contexts of their implementation

Learning Garment DensePose for Robust Warping in Virtual Try-On

Virtual try-on, i.e making people virtually try new garments, is an active research area in computer vision with great commercial applications. Current virtual try-on methods usually work in a two-stage pipeline. First, the garment image is warped on the person's pose using a flow estimation network. Then in the second stage, the warped garment is fused with the person image to render a new try-on image. Unfortunately, such methods are heavily dependent on the quality of the garment warping which often fails when dealing with hard poses (e.g., a person lifting or crossing arms). In this work, we propose a robust warping method for virtual try-on based on a learned garment DensePose which has a direct correspondence with the person's DensePose. Due to the lack of annotated data, we show how to leverage an off-the-shelf person DensePose model and a pretrained flow model to learn the garment DensePose in a weakly supervised manner. The garment DensePose allows a robust warping to any person's pose without any additional computation. Our method achieves the state-of-the-art equivalent on virtual try-on benchmarks and shows warping robustness on in-the-wild person images with hard poses, making it more suited for real-world virtual try-on applications.Comment: 6 page

Interactive Fashion Content Generation Using LLMs and Latent Diffusion Models

Fashionable image generation aims to synthesize images of diverse fashion prevalent around the globe, helping fashion designers in real-time visualization by giving them a basic customized structure of how a specific design preference would look in real life and what further improvements can be made for enhanced customer satisfaction. Moreover, users can alone interact and generate fashionable images by just giving a few simple prompts. Recently, diffusion models have gained popularity as generative models owing to their flexibility and generation of realistic images from Gaussian noise. Latent diffusion models are a type of generative model that use diffusion processes to model the generation of complex data, such as images, audio, or text. They are called "latent" because they learn a hidden representation, or latent variable, of the data that captures its underlying structure. We propose a method exploiting the equivalence between diffusion models and energy-based models (EBMs) and suggesting ways to compose multiple probability distributions. We describe a pipeline on how our method can be used specifically for new fashionable outfit generation and virtual try-on using LLM-guided text-to-image generation. Our results indicate that using an LLM to refine the prompts to the latent diffusion model assists in generating globally creative and culturally diversified fashion styles and reducing bias.Comment: Third Workshop on Ethical Considerations in Creative applications of Computer Vision (EC3V) at CVPR 2023. arXiv admin note: substantial text overlap with arXiv:2301.02110 by other author

Fill in Fabrics: Body-Aware Self-Supervised Inpainting for Image-Based Virtual Try-On

Previous virtual try-on methods usually focus on aligning a clothing item with a person, limiting their ability to exploit the complex pose, shape and skin color of the person, as well as the overall structure of the clothing, which is vital to photo-realistic virtual try-on. To address this potential weakness, we propose a fill in fabrics (FIFA) model, a self-supervised conditional generative adversarial network based framework comprised of a Fabricator and a unified virtual try-on pipeline with a Segmenter, Warper and Fuser. The Fabricator aims to reconstruct the clothing image when provided with a masked clothing as input, and learns the overall structure of the clothing by filling in fabrics. A virtual try-on pipeline is then trained by transferring the learned representations from the Fabricator to Warper in an effort to warp and refine the target clothing. We also propose to use a multi-scale structural constraint to enforce global context at multiple scales while warping the target clothing to better fit the pose and shape of the person. Extensive experiments demonstrate that our FIFA model achieves state-of-the-art results on the standard VITON dataset for virtual try-on of clothing items, and is shown to be effective at handling complex poses and retaining the texture and embroidery of the clothing

StyleHumanCLIP: Text-guided Garment Manipulation for StyleGAN-Human

This paper tackles text-guided control of StyleGAN for editing garments in full-body human images. Existing StyleGAN-based methods suffer from handling the rich diversity of garments and body shapes and poses. We propose a framework for text-guided full-body human image synthesis via an attention-based latent code mapper, which enables more disentangled control of StyleGAN than existing mappers. Our latent code mapper adopts an attention mechanism that adaptively manipulates individual latent codes on different StyleGAN layers under text guidance. In addition, we introduce feature-space masking at inference time to avoid unwanted changes caused by text inputs. Our quantitative and qualitative evaluations reveal that our method can control generated images more faithfully to given texts than existing methods

Single Stage Virtual Try-on via Deformable Attention Flows

Virtual try-on aims to generate a photo-realistic fitting result given an in-shop garment and a reference person image. Existing methods usually build up multi-stage frameworks to deal with clothes warping and body blending respectively, or rely heavily on intermediate parser-based labels which may be noisy or even inaccurate. To solve the above challenges, we propose a single-stage try-on framework by developing a novel Deformable Attention Flow (DAFlow), which applies the deformable attention scheme to multi-flow estimation. With pose keypoints as the guidance only, the self- and cross-deformable attention flows are estimated for the reference person and the garment images, respectively. By sampling multiple flow fields, the feature-level and pixel-level information from different semantic areas are simultaneously extracted and merged through the attention mechanism. It enables clothes warping and body synthesizing at the same time which leads to photo-realistic results in an end-to-end manner. Extensive experiments on two try-on datasets demonstrate that our proposed method achieves state-of-the-art performance both qualitatively and quantitatively. Furthermore, additional experiments on the other two image editing tasks illustrate the versatility of our method for multi-view synthesis and image animation.Comment: ECCV 202