Sustainable clothing: challenges, barriers and interventions for encouraging more sustainable consumer behaviour

Research with consumers has revealed limited awareness of the sustainability impact of clothing (Goworek et al., 2012). Semi-structured interviews conducted with a range of experts in sustainable clothing to increase understanding of the challenges for sustainable clothing revealed that a focus on sustainability alone will not drive the necessary changes in consumers’ clothing purchase, care and disposal behaviour for three reasons: (i) clothing sustainability is too complex; (ii) consumers are too diverse in their ethical concerns; and (iii) clothing is not an altruistic purchase. The findings identify the challenges that need to be addressed and the associated barriers for sustainable clothing. Interventions targeting consumers, suppliers, buyers and retailers are proposed that encourage more sustainable clothing production, purchase, care and disposal behaviour. These interventions range from normalising the design of sustainable clothing and increasing the ease of purchase, to shifting clothes washing norms and increasing upcycling, recycling and repair

Sweatshops and Consumer Choices

We consider a case where consumers are faced with a choice between sweatshop-produced clothing and identical clothing produced in high-income countries. We argue that it is morally better for consumers to purchase clothing produced in sweatshops and then to compensate sweatshop workers for the difference between their actual wage and a fair wage than it is for them either to purchase the sweatshop clothing without this compensatory transfer or to purchase clothing produced in high-income countries

DEFRA Clothing Action Plan

As part of Defra’s Sustainable Consumption and Production (SCP) programme, a voluntary clothing industry initiative was co-ordinated by Defra with the aim to improve the environmental and ethical performance of clothing. The Sustainable Clothing Roadmap aims to improve the environmental and social performance of clothing, building on existing initiatives and by co-ordinating action by key clothing supply chain stakeholders. Although organisations in the clothing supply chain have already taken significant steps to reduce adverse environmental and social impacts, further industry-wide co-operation and agreed commitments will enable that process to accelerate. That is the rationale behind the collaborative nature of the roadmap. The DEFRA initiative is now a WRAP (Waste Resources Action Plan) initiative. Centre for Sustainable Fashion participate on the WRAP steering group and the sub groups on design and recycling. Dilys Williams advised this report's lead author

Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting

This paper proposes a single-shot approach for recognising clothing categories from 2.5D features. We propose two visual features, BSP (B-Spline Patch) and TSD (Topology Spatial Distances) for this task. The local BSP features are encoded by LLC (Locality-constrained Linear Coding) and fused with three different global features. Our visual feature is robust to deformable shapes and our approach is able to recognise the category of unknown clothing in unconstrained and random configurations. We integrated the category recognition pipeline with a stereo vision system, clothing instance detection, and dual-arm manipulators to achieve an autonomous sorting system. To verify the performance of our proposed method, we build a high-resolution RGBD clothing dataset of 50 clothing items of 5 categories sampled in random configurations (a total of 2,100 clothing samples). Experimental results show that our approach is able to reach 83.2\% accuracy while classifying clothing items which were previously unseen during training. This advances beyond the previous state-of-the-art by 36.2\%. Finally, we evaluate the proposed approach in an autonomous robot sorting system, in which the robot recognises a clothing item from an unconstrained pile, grasps it, and sorts it into a box according to its category. Our proposed sorting system achieves reasonable sorting success rates with single-shot perception.Comment: 9 pages, accepted by IROS201

Functional textile preferences of elderly people

Aging is an inevitable stage of lifetime bringing along physical and emotional deterioration. Rate of aging population in the world has been constantly rising with the contribution of technological improvements on life quality, and medical services. Depending on the unavoidable physical and emotional changes of aging human body, clothing preferences and needs become different then needs of other textile and clothing consumer groups. Textile and clothing products are one of the basic needs of human kind. Sufficient and appropriate clothing is especially important for life quality improvement at elderly stage of human life. New generation functional and smart textile and clothing products bring new opportunities to improve life quality of elderly people with such wide range products of mobility support clothing, medical help, hygiene, and health monitoring textile and clothing products. This survey based research work is aimed to search the awareness level and priority of society about the functional and smart textile products for elderly people. It has been found that gender difference has significant influence on preference level of functional textile products, where women has higher interest then men about functional textiles. It can also be stated that comfort properties are primarily preferred preferences comparing to the fashion and functionality properties of textile products. © 2015, Mediterranean Center of Social and Educational Research. All rights reserved

VITON: An Image-based Virtual Try-on Network

We present an image-based VIirtual Try-On Network (VITON) without using 3D information in any form, which seamlessly transfers a desired clothing item onto the corresponding region of a person using a coarse-to-fine strategy. Conditioned upon a new clothing-agnostic yet descriptive person representation, our framework first generates a coarse synthesized image with the target clothing item overlaid on that same person in the same pose. We further enhance the initial blurry clothing area with a refinement network. The network is trained to learn how much detail to utilize from the target clothing item, and where to apply to the person in order to synthesize a photo-realistic image in which the target item deforms naturally with clear visual patterns. Experiments on our newly collected Zalando dataset demonstrate its promise in the image-based virtual try-on task over state-of-the-art generative models

Learning to Dress {3D} People in Generative Clothing

Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not generalize to the complexity of dressed people in common images and videos. Additionally, current models lack the expressive power needed to represent the complex non-linear geometry of pose-dependent clothing shapes. To address this, we learn a generative 3D mesh model of clothed people from 3D scans with varying pose and clothing. Specifically, we train a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making clothing an additional term in SMPL. Our model is conditioned on both pose and clothing type, giving the ability to draw samples of clothing to dress different body shapes in a variety of styles and poses. To preserve wrinkle detail, our Mesh-VAE-GAN extends patchwise discriminators to 3D meshes. Our model, named CAPE, represents global shape and fine local structure, effectively extending the SMPL body model to clothing. To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses. The model, code and data are available for research purposes at https://cape.is.tue.mpg.de.Comment: CVPR-2020 camera ready. Code and data are available at https://cape.is.tue.mpg.d

Self-contained clothing system provides protection against hazardous environments

Self-contained clothing system protects personnel against hazardous environments. The clothing has an environmental control system and a complete protection envelope consisting of an outer garment, inner garment, underwear, boots, gloves, and helmet