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

Stochastic Exposure Coding for Handling Multi-ToF-Camera Interference 随机曝光编码处理多tof相机干扰

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00797

Jongho Lee, Mohit Gupta

{"title":"Stochastic Exposure Coding for Handling Multi-ToF-Camera Interference","authors":"Jongho Lee, Mohit Gupta","doi":"10.1109/ICCV.2019.00797","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00797","url":null,"abstract":"As continuous-wave time-of-flight (C-ToF) cameras become popular in 3D imaging applications, they need to contend with the problem of multi-camera interference (MCI). In a multi-camera environment, a ToF camera may receive light from the sources of other cameras, resulting in large depth errors. In this paper, we propose stochastic exposure coding (SEC), a novel approach for mitigating. SEC involves dividing a camera's integration time into multiple slots, and switching the camera off and on stochastically during each slot. This approach has two benefits. First, by appropriately choosing the on probability for each slot, the camera can effectively filter out both the AC and DC components of interfering signals, thereby mitigating depth errors while also maintaining high signal-to-noise ratio. This enables high accuracy depth recovery with low power consumption. Second, this approach can be implemented without modifying the C-ToF camera's coding functions, and thus, can be used with a wide range of cameras with minimal changes. We demonstrate the performance benefits of SEC with theoretical analysis, simulations and real experiments, across a wide range of imaging scenarios.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"201 1","pages":"7879-7887"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88864205","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: 8

On the Over-Smoothing Problem of CNN Based Disparity Estimation 基于CNN视差估计的过平滑问题研究

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00909

Chuangrong Chen, Xiaozhi Chen, Hui Cheng

{"title":"On the Over-Smoothing Problem of CNN Based Disparity Estimation","authors":"Chuangrong Chen, Xiaozhi Chen, Hui Cheng","doi":"10.1109/ICCV.2019.00909","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00909","url":null,"abstract":"Currently, most deep learning based disparity estimation methods have the problem of over-smoothing at boundaries, which is unfavorable for some applications such as point cloud segmentation, mapping, etc. To address this problem, we first analyze the potential causes and observe that the estimated disparity at edge boundary pixels usually follows multimodal distributions, causing over-smoothing estimation. Based on this observation, we propose a single-modal weighted average operation on the probability distribution during inference, which can alleviate the problem effectively. To integrate the constraint of this inference method into training stage, we further analyze the characteristics of different loss functions and found that using cross entropy with gaussian distribution consistently further improves the performance. For quantitative evaluation, we propose a novel metric that measures the disparity error in the local structure of edge boundaries. Experiments on various datasets using various networks show our method's effectiveness and general applicability. Code will be available at https://github.com/chenchr/otosp.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"1 1","pages":"8996-9004"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89098054","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: 29

Spatial Correspondence With Generative Adversarial Network: Learning Depth From Monocular Videos 空间对应与生成对抗网络:从单目视频学习深度

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00759

Zhenyao Wu, Xinyi Wu, Xiaoping Zhang, Song Wang, L. Ju

{"title":"Spatial Correspondence With Generative Adversarial Network: Learning Depth From Monocular Videos","authors":"Zhenyao Wu, Xinyi Wu, Xiaoping Zhang, Song Wang, L. Ju","doi":"10.1109/ICCV.2019.00759","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00759","url":null,"abstract":"Depth estimation from monocular videos has important applications in many areas such as autonomous driving and robot navigation. It is a very challenging problem without knowing the camera pose since errors in camera-pose estimation can significantly affect the video-based depth estimation accuracy. In this paper, we present a novel SC-GAN network with end-to-end adversarial training for depth estimation from monocular videos without estimating the camera pose and pose change over time. To exploit cross-frame relations, SC-GAN includes a spatial correspondence module which uses Smolyak sparse grids to efficiently match the features across adjacent frames, and an attention mechanism to learn the importance of features in different directions. Furthermore, the generator in SC-GAN learns to estimate depth from the input frames, while the discriminator learns to distinguish between the ground-truth and estimated depth map for the reference frame. Experiments on the KITTI and Cityscapes datasets show that the proposed SC-GAN can achieve much more accurate depth maps than many existing state-of-the-art methods on monocular videos.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"55 1 1","pages":"7493-7503"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86070249","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: 20

Neural Turtle Graphics for Modeling City Road Layouts 用于建模城市道路布局的神经龟图形

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00462

Hang Chu, Daiqing Li, David Acuna, Amlan Kar, Maria Shugrina, Xinkai Wei, Ming-Yu Liu, A. Torralba, S. Fidler

引用次数: 53

Adaptive Activation Thresholding: Dynamic Routing Type Behavior for Interpretability in Convolutional Neural Networks 自适应激活阈值:卷积神经网络中可解释性的动态路由类型行为

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00504

Yiyou Sun, Sathya Ravi, Vikas Singh

{"title":"Adaptive Activation Thresholding: Dynamic Routing Type Behavior for Interpretability in Convolutional Neural Networks","authors":"Yiyou Sun, Sathya Ravi, Vikas Singh","doi":"10.1109/ICCV.2019.00504","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00504","url":null,"abstract":"There is a growing interest in strategies that can help us understand or interpret neural networks -- that is, not merely provide a prediction, but also offer additional context explaining why and how. While many current methods offer tools to perform this analysis for a given (trained) network post-hoc, recent results (especially on capsule networks) suggest that when classes map to a few high level ``concepts'' in the preceding layers of the network, the behavior of the network is easier to interpret or explain. Such training may be accomplished via dynamic/EM routing where the network ``routes'' for individual classes (or subsets of images) are dynamic and involve few nodes even if the full network may not be sparse. In this paper, we show how a simple modification of the SGD scheme can help provide dynamic/EM routing type behavior in convolutional neural networks. Through extensive experiments, we evaluate the effect of this idea for interpretability where we obtain promising results, while also showing that no compromise in attainable accuracy is involved. Further, we show that the minor modification is seemingly ad-hoc, the new algorithm can be analyzed by an approximate method which provably matches known rates for SGD.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"18 1","pages":"4937-4946"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81281123","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: 14