2019 IEEE/CVF International Conference on Computer Vision (ICCV)最新文献_第8页

Skeleton-Aware 3D Human Shape Reconstruction From Point Clouds 基于点云的骨骼感知三维人体形状重建

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00553

Haiyong Jiang, Jianfei Cai, Jianmin Zheng

{"title":"Skeleton-Aware 3D Human Shape Reconstruction From Point Clouds","authors":"Haiyong Jiang, Jianfei Cai, Jianmin Zheng","doi":"10.1109/ICCV.2019.00553","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00553","url":null,"abstract":"This work addresses the problem of 3D human shape reconstruction from point clouds. Considering that human shapes are of high dimensions and with large articulations, we adopt the state-of-the-art parametric human body model, SMPL, to reduce the dimension of learning space and generate smooth and valid reconstruction. However, SMPL parameters, especially pose parameters, are not easy to learn because of ambiguity and locality of the pose representation. Thus, we propose to incorporate skeleton awareness into the deep learning based regression of SMPL parameters for 3D human shape reconstruction. Our basic idea is to use the state-of-the-art technique PointNet++ to extract point features, and then map point features to skeleton joint features and finally to SMPL parameters for the reconstruction from point clouds. Particularly, we develop an end-to-end framework, where we propose a graph aggregation module to augment PointNet++ by extracting better point features, an attention module to better map unordered point features into ordered skeleton joint features, and a skeleton graph module to extract better joint features for SMPL parameter regression. The entire framework network is first trained in an end-to-end manner on synthesized dataset, and then online fine-tuned on unseen dataset with unsupervised loss to bridges gaps between training and testing. The experiments on multiple datasets show that our method is on par with the state-of-the-art solution.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"74 1","pages":"5430-5440"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86156986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 54

A Dataset of Multi-Illumination Images in the Wild 野外多光照图像数据集

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00418

Lukas Murmann, Michaël Gharbi, M. Aittala, F. Durand

引用次数: 44

FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On FW-GAN:流导航扭曲GAN视频虚拟试戴

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00125

Haoye Dong, Xiaodan Liang, Xiaohui Shen, Bowen Wu, Bing-cheng Chen, Jian Yin

{"title":"FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On","authors":"Haoye Dong, Xiaodan Liang, Xiaohui Shen, Bowen Wu, Bing-cheng Chen, Jian Yin","doi":"10.1109/ICCV.2019.00125","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00125","url":null,"abstract":"Beyond current image-based virtual try-on systems that have attracted increasing attention, we move a step forward to developing a video virtual try-on system that precisely transfers clothes onto the person and generates visually realistic videos conditioned on arbitrary poses. Besides the challenges in image-based virtual try-on (e.g., clothes fidelity, image synthesis), video virtual try-on further requires spatiotemporal consistency. Directly adopting existing image-based approaches often fails to generate coherent video with natural and realistic textures. In this work, we propose Flow-navigated Warping Generative Adversarial Network (FW-GAN), a novel framework that learns to synthesize the video of virtual try-on based on a person image, the desired clothes image, and a series of target poses. FW-GAN aims to synthesize the coherent and natural video while manipulating the pose and clothes. It consists of: (i) a flow-guided fusion module that warps the past frames to assist synthesis, which is also adopted in the discriminator to help enhance the coherence and quality of the synthesized video; (ii) a warping net that is designed to warp clothes image for the refinement of clothes textures; (iii) a parsing constraint loss that alleviates the problem caused by the misalignment of segmentation maps from images with different poses and various clothes. Experiments on our newly collected dataset show that FW-GAN can synthesize high-quality video of virtual try-on and significantly outperforms other methods both qualitatively and quantitatively.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"1 1","pages":"1161-1170"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88902256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 72

Deep Single-Image Portrait Relighting 深单图像肖像重照明

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00729

Hao Zhou, Sunil Hadap, Kalyan Sunkavalli, D. Jacobs

{"title":"Deep Single-Image Portrait Relighting","authors":"Hao Zhou, Sunil Hadap, Kalyan Sunkavalli, D. Jacobs","doi":"10.1109/ICCV.2019.00729","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00729","url":null,"abstract":"Conventional physically-based methods for relighting portrait images need to solve an inverse rendering problem, estimating face geometry, reflectance and lighting. However, the inaccurate estimation of face components can cause strong artifacts in relighting, leading to unsatisfactory results. In this work, we apply a physically-based portrait relighting method to generate a large scale, high quality, “in the wild” portrait relighting dataset (DPR). A deep Convolutional Neural Network (CNN) is then trained using this dataset to generate a relit portrait image by using a source image and a target lighting as input. The training procedure regularizes the generated results, removing the artifacts caused by physically-based relighting methods. A GAN loss is further applied to improve the quality of the relit portrait image. Our trained network can relight portrait images with resolutions as high as 1024 × 1024. We evaluate the proposed method on the proposed DPR datset, Flickr portrait dataset and Multi-PIE dataset both qualitatively and quantitatively. Our experiments demonstrate that the proposed method achieves state-of-the-art results. Please refer to https://zhhoper.github.io/dpr.html for dataset and code.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"4 1","pages":"7193-7201"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90566788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 154

Meta-Learning to Detect Rare Objects 检测稀有对象的元学习

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.01002

Yu-Xiong Wang, Deva Ramanan, M. Hebert

{"title":"Meta-Learning to Detect Rare Objects","authors":"Yu-Xiong Wang, Deva Ramanan, M. Hebert","doi":"10.1109/ICCV.2019.01002","DOIUrl":"https://doi.org/10.1109/ICCV.2019.01002","url":null,"abstract":"Few-shot learning, i.e., learning novel concepts from few examples, is fundamental to practical visual recognition systems. While most of existing work has focused on few-shot classification, we make a step towards few-shot object detection, a more challenging yet under-explored task. We develop a conceptually simple but powerful meta-learning based framework that simultaneously tackles few-shot classification and few-shot localization in a unified, coherent way. This framework leverages meta-level knowledge about \"model parameter generation\" from base classes with abundant data to facilitate the generation of a detector for novel classes. Our key insight is to disentangle the learning of category-agnostic and category-specific components in a CNN based detection model. In particular, we introduce a weight prediction meta-model that enables predicting the parameters of category-specific components from few examples. We systematically benchmark the performance of modern detectors in the small-sample size regime. Experiments in a variety of realistic scenarios, including within-domain, cross-domain, and long-tailed settings, demonstrate the effectiveness and generality of our approach under different notions of novel classes.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"92 1","pages":"9924-9933"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85682020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 223

G3raphGround: Graph-Based Language Grounding G3raphGround:基于图形的语言基础

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00438

Mohit Bajaj, Lanjun Wang, L. Sigal

引用次数: 49

Non-Local ConvLSTM for Video Compression Artifact Reduction 用于视频压缩伪影减少的非局部ConvLSTM

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00714

Yi Xu, Longwen Gao, Kai Tian, Shuigeng Zhou, Huyang Sun

{"title":"Non-Local ConvLSTM for Video Compression Artifact Reduction","authors":"Yi Xu, Longwen Gao, Kai Tian, Shuigeng Zhou, Huyang Sun","doi":"10.1109/ICCV.2019.00714","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00714","url":null,"abstract":"Video compression artifact reduction aims to recover high-quality videos from low-quality compressed videos. Most existing approaches use a single neighboring frame or a pair of neighboring frames (preceding and/or following the target frame) for this task. Furthermore, as frames of high quality overall may contain low-quality patches, and high-quality patches may exist in frames of low quality overall, current methods focusing on nearby peak-quality frames (PQFs) may miss high-quality details in low-quality frames. To remedy these shortcomings, in this paper we propose a novel end-to-end deep neural network called non-local ConvLSTM (NL-ConvLSTM in short) that exploits multiple consecutive frames. An approximate non-local strategy is introduced in NL-ConvLSTM to capture global motion patterns and trace the spatiotemporal dependency in a video sequence. This approximate strategy makes the non-local module work in a fast and low space-cost way. Our method uses the preceding and following frames of the target frame to generate a residual, from which a higher quality frame is reconstructed. Experiments on two datasets show that NL-ConvLSTM outperforms the existing methods.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"8 1","pages":"7042-7051"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74632417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 53

MMAct: A Large-Scale Dataset for Cross Modal Human Action Understanding mmmact:跨模态人类行为理解的大规模数据集

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00875

Quan Kong, Ziming Wu, Ziwei Deng, Martin Klinkigt, Bin Tong, Tomokazu Murakami

{"title":"MMAct: A Large-Scale Dataset for Cross Modal Human Action Understanding","authors":"Quan Kong, Ziming Wu, Ziwei Deng, Martin Klinkigt, Bin Tong, Tomokazu Murakami","doi":"10.1109/ICCV.2019.00875","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00875","url":null,"abstract":"Unlike vision modalities, body-worn sensors or passive sensing can avoid the failure of action understanding in vision related challenges, e.g. occlusion and appearance variation. However, a standard large-scale dataset does not exist, in which different types of modalities across vision and sensors are integrated. To address the disadvantage of vision-based modalities and push towards multi/cross modal action understanding, this paper introduces a new large-scale dataset recorded from 20 distinct subjects with seven different types of modalities: RGB videos, keypoints, acceleration, gyroscope, orientation, Wi-Fi and pressure signal. The dataset consists of more than 36k video clips for 37 action classes covering a wide range of daily life activities such as desktop-related and check-in-based ones in four different distinct scenarios. On the basis of our dataset, we propose a novel multi modality distillation model with attention mechanism to realize an adaptive knowledge transfer from sensor-based modalities to vision-based modalities. The proposed model significantly improves performance of action recognition compared to models trained with only RGB information. The experimental results confirm the effectiveness of our model on cross-subject, -view, -scene and -session evaluation criteria. We believe that this new large-scale multimodal dataset will contribute the community of multimodal based action understanding.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"209 1","pages":"8657-8666"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74899815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 55

Order-Preserving Wasserstein Discriminant Analysis 保序Wasserstein判别分析

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00998

Bing Su, Jiahuan Zhou, Ying Wu

引用次数: 8

Better and Faster: Exponential Loss for Image Patch Matching 更好更快:图像补丁匹配的指数损失

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00491

Shuang Wang, Yanfeng Li, Xuefeng Liang, Dou Quan, Bowu Yang, Shaowei Wei, L. Jiao

引用次数: 23