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Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example

Deep Graphics Encoder for Real-Time Video Makeup Synthesis f...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kips, Robin Jiang, Ruowei Ba, Sileye Phung, Edmund Aarabi, Parham Gori, Pietro Perrot, Matthieu Bloch, Isabelle LOreal Res & Innovat Clichy France Inst Polytech Paris Telecom Paris LTCI Paris France Modiface Toronto ON Canada Sorbonne Univ CNRS LIP6 Paris France

ISBN: (纸本)9781665448994

While makeup virtual-try-on is now widespread, parametrizing a computer graphics rendering engine for synthesizing images of a given cosmetics product remains a challenging task. In this paper, we introduce an inverse computer graphics method for automatic makeup synthesis from a reference image, by learning a model that maps an example portrait image with makeup to the space of rendering parameters. This method can be used by artists to automatically create realistic virtual cosmetics image samples, or by consumers, to virtually try-on a makeup extracted from their favorite reference image.

关键词： computer vision conferences Estimation Streaming media Rendering (computer graphics) Real-time systems pattern recognition

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NTIRE 2022 Spectral Recovery Challenge and Data Set

NTIRE 2022 Spectral Recovery Challenge and Data Set

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Arad, Boaz Timofte, Radu Yahel, Rony Morag, Nimrod Bernat, Amir Cai, Yuanhao Lin, Jing Lin, Zudi Wang, Haoqian Zhang, Yulun Pfister, Hanspeter Van Gool, Luc Liu, Shuai Li, Yongqiang Feng, Chaoyu Lei, Lei Li, Jiaojiao Du, Songcheng Wu, Chaoxiong Leng, Yihong Song, Rui Zhang, Mingwei Song, Chongxing Zhao, Shuyi Lang, Zhiqiang Wei, Wei Zhang, Lei Dian, Renwei Shan, Tianci Guo, Anjing Feng, Chengguo Liu, Jinyang Agarla, Mirko Bianco, Simone Buzzelli, Marco Celona, Luigi Schettini, Raimondo He, Jiang Xiao, Yi Xiao, Jiajun Yuan, Qiangqiang Li, Jie Zhang, Liangpei Kwon, Taesung Ryu, Dohoon Bae, Hyokyoung Yang, Hao-Hsiang Chang, Hua-En Huang, Zhi-Kai Chen, Wei-Ting Kuo, Sy-Yen Chen, Junyu Li, Haiwei Liu, Song Uma, Sabarinathan K. Bama, B. Sathya Roomi, S. Mohamed Mansoor Oddity Tech Ltd New York NY USA Voyage81 Ltd New York NY USA Swiss Fed Inst Technol Computat Vis Lab Zurich Switzerland Univ Wurzburg Ctr Artificial Intelligence & Data Sci Wurzburg Germany Acad Coll Tel Aviv Yaffo Tel Aviv Israel Tel Aviv Univ Tel Aviv Israel Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Harvard Univ Visual Comp Grp Cambridge MA USA JMU Wurzburg Ctr Artificial Intelligence & Data Sci Wurzburg Germany Xiaomi Inc Beijing Peoples R China Xidian Univ Xian Peoples R China Northwestern Polytech Univ Xian Peoples R China Northwestern Polytech Univ Changan Campus Xian Peoples R China Hunan Univ Changsha Hunan Peoples R China Univ Milano Bicocca Dept Informat Syst & Commun Milan Italy Wuhan Univ Sch Geodesy & Geomat Wuhan Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Peoples R China Korea Adv Inst Sci & Technol Dept Bio & Brain Engn Daejeon South Korea Natl Taiwan Univ Dept Elect Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan Chinese Acad Sci Xian Inst Opt & Precis Mech Xian Peoples R China Couger Inc Tokyo Japan Thiagarajar Coll Engn Kamarajar Salai India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper reviews the third biennial challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. This challenge presents the "ARAD 1K" data set: a new, larger-than-ever natural hyperspectral image data set containing 1,000 images. Challenge participants were required to recover hyperspectral information from synthetically generated JPEG-compressed RGB images simulating capture by a known calibrated camera, operating under partially known parameters, in a setting which includes acquisition noise. The challenge was attended by 241 teams, with 60 teams competing in the final testing phase, 12 of which provided detailed descriptions of their methodology which are included in this report. The performance of these submissions is reviewed and provided here as a gauge for the current state-of-the-art in spectral reconstruction from natural RGB images.

关键词： Training computer vision Costs conferences Neural networks Cameras pattern recognition

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Shadow-Mapping for Unsupervised Neural Causal Discovery

Shadow-Mapping for Unsupervised Neural Causal Discovery

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Vowels, Matthew J. Camgoz, Necati Cihan Bowden, Richard Univ Surrey Ctr Vis Speech & Signal Proc Guildford Surrey England

ISBN: (纸本)9781665448994

An important goal across most scientific fields is the discovery of causal structures underling a set of observations. Unfortunately, causal discovery methods which are based on correlation or mutual information can often fail to identify causal links in systems which exhibit dynamic relationships. Such dynamic systems (including the famous coupled logistic map) exhibit 'mirage' correlations which appear and disappear depending on the observation window. This means not only that correlation is not causation but, perhaps counter-intuitively, that causation may occur without correlation. In this paper we describe Neural Shadow-Mapping, a neural network based method which embeds high-dimensional video data into a low-dimensional shadow representation, for subsequent estimation of causal links. We demonstrate its performance at discovering causal links from video-representations of dynamic systems.

关键词： computer vision Correlation conferences Neural networks Estimation pattern recognition Dynamical systems

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SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data

SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object De...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Hu, Yuan-Ting Wang, Jiahong Yeh, Raymond A. Schwing, Alexander G. Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781665448994

Extracting detailed 3D information of objects from video data is an important goal for holistic scene understanding. While recent methods have shown impressive results when reconstructing meshes of objects from a single image, results often remain ambiguous as part of the object is unobserved. Moreover, existing image-based datasets for mesh reconstruction don't permit to study models which integrate temporal information. To alleviate both concerns we present SAIL-VOS 3D: a synthetic video dataset with frame-by-frame mesh annotations which extends SAIL-VOS. We also develop first baselines for reconstruction of 3D meshes from video data via temporal models. We demonstrate efficacy of the proposed baseline on SAIL-VOS 3D and Pix3D, showing that temporal information improves reconstruction quality. Resources and additional information are available at http://***.

关键词： Solid modeling computer vision Three-dimensional displays Annotations Object detection Data models Data mining

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Modeling Fashion Compatibility with Explanation by using Bidirectional LSTM

Modeling Fashion Compatibility with Explanation by using Bid...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Pang Kaicheng Zou Xingxing Wong, Wai Keung Hong Kong Polytech Univ Inst Text & Clothing Hong Kong Peoples R China Lab Artificial Intelligence Design Hong Kong Peoples R China

ISBN: (纸本)9781665448994

The goal of this paper is to model the fashion compatibility of an outfit and provide the explanations. We first extract features of all attributes of all items via convolutional neural networks, and then train the bidirectional Long Shortterm Memory (Bi-LSTM) model to learn the compatibility of an outfit by treating these attribute features as a sequence. Gradient penalty regularization is exploited for training inter-factor compatibility net which is used to compute the loss for judgment and provide its explanation which is generated from the recognized reasons related to the judgment. To train and evaluate the proposed approach, we expanded the EVALUATION3 dataset in terms of the number of items and attributes. Experiment results show that our approach can successfully evaluate compatibility with reason.

关键词： Training Industries computer vision Computational modeling conferences Feature extraction pattern recognition

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Sketch-QNet: A Quadruplet ConvNet for Color Sketch-based Image Retrieval

Sketch-QNet: A Quadruplet ConvNet for Color Sketch-based Ima...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Fuentes, Anibal Saavedra, Jose M. Impresee Inc 600 Calif St San Francisco CA 94108 USA

ISBN: (纸本)9781665448994

Architectures based on siamese networks with triplet loss have shown outstanding performance on the image-based similarity search problem. This approach attempts to discriminate between positive (relevant) and negative (irrelevant) items. However, it undergoes a critical weakness. Given a query, it cannot discriminate weakly relevant items, for instance, items of the same type but different color or texture as the given query, which could be a serious limitation for many real-world search applications. Therefore, in this work, we present a quadruplet-based architecture that overcomes the aforementioned weakness. Moreover, we present an instance of this quadruplet network, which we call Sketch-QNet, to deal with the color sketch-based image retrieval (CSBIR) problem, achieving new state-of-the-art results.

关键词： Training Visualization computer vision Image color analysis conferences Image retrieval computer architecture

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A Low-cost & Real-time Motion Capture System

A Low-cost & Real-time Motion Capture System

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chatzitofis, Anargyros Albanis, Georgios Zioulis, Nikolaos Thermos, Spyridon Codewheel Larnaka Larnaca Cyprus Univ Thessaly Dept Informat & Telecommun Volos Greece

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Traditional marker-based motion capture requires excessive and specialized equipment, hindering accessibility and wider adoption. In this work, we demonstrate such a system but rely on a very sparse set of low-cost consumer-grade sensors. Our system exploits a data-driven backend to infer the captured subject's joint positions from noisy marker estimates in real-time. In addition to reduced costs and portability, its inherent denoising nature allows for quicker captures by alleviating the need for precise marker placement and post-processing, making it suitable for interactive virtual reality applications.

关键词： computer vision Costs Noise reduction Virtual reality Motion capture Sensor systems Real-time systems

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Filter Distribution Templates in Convolutional Networks for Image Classification Tasks

Filter Distribution Templates in Convolutional Networks for ...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Izquierdo-Cordova, Ramon Mayol-Cuevas, Walterio Univ Bristol Dept Comp Sci Bristol Avon England

ISBN: (纸本)9781665448994

Neural network designers have reached progressive accuracy by increasing models depth, introducing new layer types and discovering new combinations of layers. A common element in many architectures is the distribution of the number of filters in each layer. Neural network models keep a pattern design of increasing filters in deeper layers such as those in LeNet, VGG, ResNet, MobileNet and even in automatic discovered architectures such as NASNet. It remains unknown if this pyramidal distribution of filters is the best for different tasks and constrains. In this work we present a series of modifications in the distribution of filters in three popular neural network models and their effects in accuracy and resource consumption. Results show that by applying this approach, some models improve up to 8.9% in accuracy showing reductions in parameters up to 54%.

关键词： Computational modeling Neural networks Memory management Manuals Data models pattern recognition Convolutional neural networks

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Feedback control of event cameras

Feedback control of event cameras

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Delbruck, Tobi Graca, Rui Paluch, Marcin UZH ETH Zurich Inst Neuroinformat Zurich Switzerland

ISBN: (纸本)9781665448994

Dynamic vision sensor event cameras produce a variable data rate stream of brightness change events. Event production at the pixel level is controlled by threshold, bandwidth, and refractory period bias current parameter settings. Biases must be adjusted to match application requirements and the optimal settings depend on many factors. As a first step towards automatic control of biases, this paper proposes fixed-step feedback controllers that use measurements of event rate and noise. The controllers regulate the event rate within an acceptable range using threshold and refractory period control, and regulate noise using bandwidth control. Experiments demonstrate model validity and feedback control.

关键词： computer vision Current measurement conferences Bandwidth Production vision sensors Cameras

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vision + Language Applications: A Survey

Vision + Language Applications: A Survey

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yutong Zhou Nobutaka Shimada Ritsumeikan University Shiga Japan

Text-to-image generation has attracted significant interest from researchers and practitioners in recent years due to its widespread and diverse applications across various industries. Despite the progress made in the domain of vision and language research, the existing literature remains relatively limited, particularly with regard to advancements and applications in this field. This paper explores a relevant research track within multimodal applications, including text, vision, audio, and others. In addition to the studies discussed in this paper, we are also committed to continually updating the latest relevant papers, datasets, application projects and corresponding information at https://***/Yutong-Zhou-cv/Awesome-Text-to-Image.

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