自由自在に服を着こなせ！StyleGANを用いたファッション画像生成
3つの要点 ✔️StyleGANを用いて好きな服を着せた人物画像生成 ✔️元画像の服の色や雰囲気を自在に操作 ✔️圧倒的高解像度の人物画像生成 上図の人物画像は全て生成された画像であり、実在する画像ではありません。 信じられますか？ ついにGANによる人物画像生成もこれほどの領域まで到達しました。 今までのGANによる圧倒的高解像度の人物画像生成は顔部分のみに限られていましたが、本論文では全身のとても自然な人物画像生成を行える手法を提案しました。 GANによる画像生成においては動物であったり、食べ物、人の顔、ベッドルームなど様々な物が生成されてきました。そして現在、GANはシンプルな画像生成だけでなく、アートやファッションに転用しようとする動きが出てきています。 それでは見ていきましょう。 概要 本論文のテーマは「服を操作可能な高解像度ファッション画像生成」となっています。今までは服を細か
フェイスブック、コーディネートの悩みをAIで解明した論文「Fashion++」を発表
ZEPPELIN ドイツ腕時計ブランドの"ZEPPELIN"とのコラボレーションウォッチパートナ... 詳細を見る
Fashion++: Minimal Edits for Outfit Improvement - Meta Research | Meta Research
Fashion++: Minimal Edits for Outfit ImprovementInternational Conference on Computer Vision (ICCV) Given an outfit, what small changes would most improve its fashionability? This question presents an intriguing new vision challenge. We introduce Fashion++, an approach that proposes minimal adjustments to a full-body clothing outfit that will have maximal impact on its fashionability. Our model cons
Learning the Latent "Look": Unsupervised Discovery of a Style-Coherent Embedding from Fashion Images
Learning the Latent "Look": Unsupervised Discovery of a Style-Coherent Embedding from Fashion Images We propose an unsupervised approach to learn a style-coherent representation. Our method leverages probabilistic polylingual topic models based on visual attributes to discover a set of latent style factors. Given a collection of unlabeled fashion images, our approach mines for the latent styles, t
GitHub - lemondan/HumanParsing-Dataset
This human parsing dataset includes the detailed pixel-wise annotations for fashion images, which is proposed in our TPAMI paper "Deep Human Parsing with Active Template Regression", and ICCV 2015 paper "Human Parsing with Contextualized Convolutional Neural Network". You can download the dataset from this link. http://pan.baidu.com/s/1qY8bToS passwd：kjgk We will mainly maintain a new LIP benchmar
CRF as RNN Semantic Image Segmentation
CRF as RNN Semantic Image Segmentation Live Demo Our work allows computers to recognize objects in images, what is distinctive about our work is that we also recover the 2D outline of the object. Currently we have trained this model to recognize 20 classes. The demo below allows you to test our algorithm on your own images – have a try and see if you can fool it, if you get some good examples you
CRFasRNN
Conditional Random Fields as Recurrent Neural Networks Shuai Zheng*, Sadeep Jayasumana*, Bernardino Romera-Paredes, Vibhav Vineet^, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr. Torr Vision Group, University of Oxford, Stanford University, Baidu IDL * equal contribution. ^ Work conducted while authors at the University of Oxford. Online demo for semantic image segmentation. Pixel-level l
Conditional Random Fields as Recurrent Neural Networks
Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learning techniques for image recognition to tackle pixel-level labelling tasks. One central issue in this methodology is the limited capacity of deep learning techniques to delineate visual objects. To solve this problem, we i
Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences
With the rapid proliferation of smart mobile devices, users now take millions of photos every day. These include large numbers of clothing and accessory images. We would like to answer questions like `What outfit goes well with this pair of shoes?' To answer these types of questions, one has to go beyond learning visual similarity and learn a visual notion of compatibility across categories. In th
Kota Yamaguchi - PaperDoll Parsing
Clothing recognition is an extremely challenging problem due to wide variation in clothing item appearance, layering, and style. In this paper, we tackle the clothing parsing problem using a retrieval based approach. For a query image, we find similar styles from a large database of tagged fashion images and use these examples to effectively parse the query. Our approach combines: trained global m
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https://openaccess.thecvf.com/content/ICCV2021/papers/Patashnik_StyleCLIP_Text-Driven_Manipulation_of_StyleGAN_Imagery_ICCV_2021_paper.pdf
GeoStyle: Discovering Fashion Trends and Events
Fashion++: Minimal Edits for Outfit Improvement
GeoStyle
Learning Deconvolution Network for Semantic Segmentation
GitHub - torrvision/crfasrnn: This repository contains the source code for the semantic image segmentation method described in the ICCV 2015 paper: Conditional Random Fields as Recurrent Neural Networks. http://crfasrnn.torr.vision/
ICCV 2015 Open Access Repository
http://tamaraberg.com/papers/street2shop.pdf