38,094 research outputs found
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
- …