检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

92 篇 会议

馆藏范围

92 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

92 篇 工学
- 92 篇 计算机科学与技术...
1 篇 医学
- 1 篇 临床医学

主题

92 篇 3d from single i...
43 篇 deep learning
29 篇 and body pose
29 篇 gesture
29 篇 face
18 篇 3d from multivie...
13 篇 scene analysis a...
11 篇 robotics + drivi...
11 篇 vision + graphic...
8 篇 image and video ...
6 篇 categorization
6 篇 motion and track...
6 篇 recognition: det...
6 篇 retrieval
5 篇 segmentation
5 篇 rgbd sensors and...
5 篇 physics-based vi...
4 篇 vision applicati...
4 篇 grouping and sha...
3 篇 representation l...

机构

4 篇 zhejiang univ pe...
4 篇 carnegie mellon ...
4 篇 imperial coll lo...
4 篇 australian natl ...
4 篇 stanford univ st...
3 篇 univ southern ca...
3 篇 princeton univ p...
2 篇 google res mount...
2 篇 peking univ peop...
2 篇 northwestern pol...
2 篇 univ tubingen tu...
2 篇 univ oxford oxfo...
2 篇 univ chinese aca...
2 篇 natl tsing hua u...
2 篇 mpi intelligent ...
2 篇 univ middlesex e...
2 篇 ecole polytech f...
2 篇 univ adelaide ad...
2 篇 simon fraser uni...
2 篇 tsinghua univ pe...

作者

3 篇 li hongdong
3 篇 zafeiriou stefan...
3 篇 black michael j.
3 篇 brox thomas
2 篇 savarese silvio
2 篇 song shuran
2 篇 fua pascal
2 篇 pavlakos georgio...
2 篇 tatarchenko maxi...
2 篇 smith william a....
2 篇 zhou xiaowei
2 篇 chandraker manmo...
2 篇 reid ian
2 篇 theobalt christi...
2 篇 ploumpis stylian...
2 篇 yang ruigang
2 篇 li hao
2 篇 kotsia irene
2 篇 bolkart timo
2 篇 geiger andreas

语言

92 篇 英文

检索条件"主题词=3D from single image"

共 92 条记录，以下是1-10 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Expressive Body Capture: 3d Hands, Face, and Body from a single image 32

Expressive Body Capture: 3D Hands, Face, and Body from a Sin...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Pavlakos, Georgios Choutas, Vasileios Ghorbani, Nima Bolkart, Timo Osman, Ahmed A. A. Tzionas, dimitrios Black, Michael J. MPI Intelligent Syst Tubingen Germany Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9781728132938

To facilitate the analysis of human actions, interactions and emotions, we compute a 3d model of human body pose, hand pose, and facial expression from a single monocular image. To achieve this, we use thousands of 3d scans to train a new, unified, 3d model of the human body, SMPL-X, that extends SMPL with fully articulated hands and an expressive face. Learning to regress the parameters of SMPL-X directly from images is challenging without paired images and 3d ground truth. Consequently, we follow the approach of SMPLify, which estimates 2d features and then optimizes model parameters to fit the features. We improve on SMPLify in several significant ways: (1) we detect 2d features corresponding to the face, hands, and feet and fit the full SMPL-X model to these;(2) we train a new neural network pose prior using a large MoCap dataset;(3) we define a new interpenetration penalty that is both fast and accurate;(4) we automatically detect gender and the appropriate body models (male, female, or neutral);(5) our PyTorch implementation achieves a speedup of more than 8x over Chumpy. We use the new method, SMPLify-X, to fit SMPL-X to both controlled images and images in the wild. We evaluate 3d accuracy on a new curated dataset comprising 100 images with pseudo ground-truth. This is a step towards automatic expressive human capture from monocular RGB data.

关键词： 3d from single image And Body Pose Face Gesture

来源：评论

学校读者我要写书评

暂无评论

PlaneRCNN: 3d Plane detection and Reconstruction from a single image 32

PlaneRCNN: 3D Plane Detection and Reconstruction from a Sing...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Liu, Chen Kim, Kihwan Gu, Jinwei Furukawa, Yasutaka Kautz, Jan NVIDIA Santa Clara CA 95050 USA Washington Univ St Louis MO 14263 USA SenseTime Hong Kong Peoples R China Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781728132938

This paper proposes a deep neural architecture, PlaneRCNN, that detects and reconstructs piecewise planar surfaces from a single RGB image. PlaneRCNN employs a variant of Mask R-CNN to detect planes with their plane parameters and segmentation masks. PlaneRCNN then jointly refines all the segmentation masks with a novel loss enforcing the consistency with a nearby view during training. The paper also presents a new benchmark with more fine-grained plane segmentations in the ground-truth, in which, PlaneRCNN outperforms existing state-of-the-art methods with significant margins in the plane detection, segmentation, and reconstruction metrics. PlaneRCNN makes an important step towards robust plane extraction, which would have an immediate impact on a wide range of applications including Robotics, Augmented Reality, and Virtual Reality. Code and data are available at https://***/publication/2019-06_PlaneRCNN.

关键词： 3d from Multiview and Sensors 3d from single image Scene Analysis and Understanding

来源：评论

学校读者我要写书评

暂无评论

3d Hand Shape and Pose Estimation from a single RGB image 32

3D Hand Shape and Pose Estimation from a Single RGB Image

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ge, Liuhao Ren, Zhou Li, Yuncheng Xue, Zehao Wang, Yingying Cai, Jianfei Yuan, Junsong Nanyang Technol Univ Singapore Singapore Wormpex AI Res Bellevue WA USA Snap Inc Santa Monica CA USA SUNY Buffalo Buffalo NY USA

ISBN: (纸本)9781728132938

This work addresses a novel and challenging problem of estimating the full 3d hand shape and pose from a single RGB image. Most current methods in 3d hand analysis from monocular RGB images only focus on estimating the 3d locations of hand keypoints, which cannot fully express the 3d shape of hand. In contrast, we propose a Graph Convolutional Neural Network (Graph CNN) based method to reconstruct a full 3d mesh of hand surface that contains richer information of both 3d hand shape and pose. To train networks with full supervision, we create a large-scale synthetic dataset containing both ground truth 3d meshes and 3d poses. When fine-tuning the networks on real-world datasets without 3d ground truth, we propose a weakly-supervised approach by leveraging the depth map as a weak supervision in training. Through extensive evaluations on our proposed new datasets and two public datasets, we show that our proposed method can produce accurate and reasonable 3d hand mesh, and can achieve superior 3d hand pose estimation accuracy when compared with state-of-the-art methods.

关键词： 3d from single image And Body Pose Face Gesture

来源：评论

学校读者我要写书评

暂无评论

MRC-Net: 6-doF Pose Estimation with MultiScale Residual Correlation

MRC-Net: 6-DoF Pose Estimation with MultiScale Residual Corr...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Yuelong Mao, Yafei Bala, Raja Hadap, Sunil Amazon Inc Seattle WA 98109 USA

ISBN: (纸本)9798350353006

We propose a single-shot approach to determining 6-doF pose of an object with available 3d computer-aided design (CAd) model from a single RGB image. Our method, dubbed MRC-Net, comprises two stages. The first performs pose classification and renders the 3d object in the classified pose. The second stage performs regression to predict fine-grained residual pose within class. Connecting the two stages is a novel multi-scale residual correlation (MRC) layer that captures high-and-low level correspondences between the input image and rendering from first stage. MRC-Net employs a Siamese network with shared weights between both stages to learn embeddings for input and rendered images. To mitigate ambiguity when predicting discrete pose class labels on symmetric objects, we use soft probabilistic labels to define pose class in the first stage. We demonstrate state-of-the-art accuracy, outperforming all competing RGB-based methods on four challenging BOP benchmark datasets: T-LESS, LM-O, YCB-V, and ITOdd. Our method is non-iterative and requires no complex post-processing. Our code and pretrained models are available at https://***/amzn/mrc-net-6d-pose.

关键词： 3d from single image 6d Object Pose Estimation direct pose estimation Geometric deep learning

来源：评论

学校读者我要写书评

暂无评论

SCINeRF: Neural Radiance Fields from a Snapshot Compressive image

SCINeRF: Neural Radiance Fields from a Snapshot Compressive ...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Yunhao Wang, Xiaodong Wang, Ping Yuan, Xin Liu, Peidong Zhejiang Univ Hangzhou Peoples R China Westlake Univ Hangzhou Peoples R China

ISBN: (纸本)9798350353006

In this paper, we explore the potential of Snapshot Compressive Imaging (SCI) technique for recovering the underlying 3d scene representation from a single temporal compressed image. SCI is a cost-effective method that enables the recording of high- dimensional data, such as hyperspectral or temporal information, into a single image using low-cost 2d imaging sensors. To achieve this, a series of specially designed 2d masks are usually employed, which not only reduces storage requirements but also offers potential privacy protection. Inspired by this, to take one step further, our approach builds upon the powerful 3d scene representation capabilities of neural radiance fields (NeRF). Specifically, we formulate the physical imaging process of SCI as part of the training of NeRF, allowing us to exploit its impressive performance in capturing complex scene structures. To assess the effectiveness of our method, we conduct extensive evaluations using both synthetic data and real data captured by our SCI system. Extensive experimental results demonstrate that our proposed approach surpasses the state-of-the-art methods in terms of image reconstruction and novel view image synthesis. Moreover, our method also exhibits the ability to restore high frame-rate multi-view consistent images by leveraging SCI and the rendering capabilities of NeRF. The code is available at https://***/WU-CVGL/SCINeRF.

关键词： 3d from single image Computational Imagimg

来源：评论

学校读者我要写书评

暂无评论

single-image Piece-wise Planar 3d Reconstruction via Associative Embedding 32

Single-Image Piece-wise Planar 3D Reconstruction via Associa...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yu, Zehao Zheng, Jia Lian, dongze Zhou, Zihan Gao, Shenghua ShanghaiTech Univ Shanghai Peoples R China Penn State Univ University Pk PA 16802 USA

ISBN: (纸本)9781728132938

single-image piece-wise planar 3d reconstruction aims to simultaneously segment plane instances and recover 3d plane parameters from an image. Most recent approaches leverage convolutional neural networks (CNNs) and achieve promising results. However, these methods are limited to detecting a fixed number of planes with certain learned order. To tackle this problem, we propose a novel two-stage method based on associative embedding, inspired by its recent success in instance segmentation. In the first stage, we train a CNN to map each pixel to an embedding space where pixels from the same plane instance have similar embeddings. Then, the plane instances are obtained by grouping the embedding vectors in planar regions via an efficient mean shift clustering algorithm. In the second stage, we estimate the parameter for each plane instance by considering both pixel-level and instance-level consistencies. With the proposed method, we are able to detect an arbitrary number of planes. Extensive experiments on public datasets validate the effectiveness and efficiency of our method. Furthermore, our method runs at 30 fps at the testing time, thus could facilitate many real-time applications such as visual SLAM and human-robot interaction. Code is available at https://***/svip-lab/PlanarReconstruction.

关键词： 3d from single image Grouping and Shape Segmentation

来源：评论

学校读者我要写书评

暂无评论

Photo Wake-Up: 3d Character Animation from a single Photo 32

Photo Wake-Up: 3D Character Animation from a Single Photo

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Weng, Chung-Yi Curless, Brian Kemelmacher-Shlizerman, Ira Univ Washington Paul G Allen Sch Comp Sci & Engn UW Real Lab Seattle WA 98195 USA

ISBN: (纸本)9781728132938

We present a method and application for animating a human subject from a single photo. E.g., the character can walk out, run, sit, or jump in 3d. The key contributions of this paper are: 1) an application of viewing and animating humans in single photos in 3d, 2) a novel 2d warping method to deform a posable template body model to fit the person's complex silhouette to create an animatable mesh, and 3) a method for handling partial self occlusions. We compare to state-of-the-art related methods and evaluate results with human studies. Further, we present an interactive interface that allows re-posing the person in 3d, and an augmented reality setup where the animated 3d person can emerge from the photo into the real world. We demonstrate the method on photos, posters, and art. The project page is at https://***/projects/wakeup/.

关键词： 3d from single image Computational Photography image and Video Synthesis Vision + Graphics

来源：评论

学校读者我要写书评

暂无评论

Learning to Regress 3d Face Shape and Expression from an image without 3d Supervision 32

Learning to Regress 3D Face Shape and Expression from an Ima...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Sanyal, Soubhik Bolkart, Timo Feng, Haiwen Black, Michael J. Max Planck Inst Intelligent Syst Perceiving Syst Dept Stuttgart Germany

ISBN: (纸本)9781728132938

The estimation of 3d face shape from a single image must be robust to variations in lighting, head pose, expression,facial hair makeup, and occlusions. Robustness requires a large training set of in-the-wild images, which by construction, lack ground truth 3d shape. To train a network without any 2d-to-3d supervision, we present RingNet, which learns to compute 3d face shape from a single image. Our key observation is that an individual's face shape is constant across images, regardless of expression,pose, lighting, etc. RingNet leverages multiple images of a person and automatically detected 2d face features. It uses a novel loss that encourages the face shape to be similar when the identity is the same and different for different people. We achieve invariance to expression by representing the face using the FLAME model. Once trained, our method takes a single image and outputs the parameters of FLAME, which can be readily animated. Additionally we create a new database of faces "not quite in-the-wild" (NoW) with 3d head scans and high-resolution images of the subjects in a wide variety of conditions. We evaluate publicly available methods and find that RingNet is more accurate than methods that use 3d supervision. The dataset, model, and results are available for research purposes at http://***.

关键词： 3d from single image And Body Pose deep Learning Face Gesture

来源：评论

学校读者我要写书评

暂无评论

Semantic Graph Convolutional Networks for 3d Human Pose Regression 32

Semantic Graph Convolutional Networks for 3D Human Pose Regr...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhao, Long Peng, Xi Tian, Yu Kapadia, Mubbasir Metaxas, dimitris N. Rutgers State Univ New Brunswick NJ 22894 USA SUNY Binghamton Binghamton NY 13902 USA

ISBN: (纸本)9781728132938

In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node. To address these limitations, we propose Semantic Graph Convolutional Networks (SemGCN), a novel neural network architecture that operates on regression tasks with graph-structured data. SemGCN learns to capture semantic information such as local and global node relationships, which is not explicitly represented in the graph. These semantic relationships can be learned through end-to-end training from the ground truth without additional supervision or hand-crafted rules. We further investigate applying SemGCN to 3d human pose regression. Our formulation is intuitive and sufficient since both 2d and 3d human poses can be represented as a structured graph encoding the relationships between joints in the skeleton of a human body. We carry out comprehensive studies to validate our method. The results prove that SemGCN outperforms state of the art while using 90% fewer parameters.

关键词： 3d from single image And Body Pose Face Gesture Vision Applications and Systems

来源：评论

学校读者我要写书评

暂无评论

Joint Face detection and Facial Motion Retargeting for Multiple Faces 32

Joint Face Detection and Facial Motion Retargeting for Multi...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chaudhuri, Bindita Vesdapunt, Noranart Wang, Baoyuan Univ Washington Seattle WA 98195 USA Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9781728132938

Facial motion retargeting is an important problem in both computer graphics and vision, which involves capturing the performance of a human face and transferring it to another 3d character. Learning 3d morphable model (3dMM) parameters from 2d face images using convolutional neural networks is common in 2d face alignment, 3d face reconstruction etc. However, existing methods either require an additional face detection step before retargeting or use a cascade of separate networks to perform detection followed by retargeting in a sequence. In this paper, we present a single end-to-end network to jointly predict the bounding box locations and 3dMM parameters for multiple faces. First, we design a novel multitask learning framework that learns a disentangled representation of 3dMM parameters for a single face. Then, we leverage the trained single face model to generate ground truth 3dMM parameters for multiple faces to train another network that performs joint face detection and motion retargeting for images with multiple faces. Experimental results show that our joint detection and retargeting network has high face detection accuracy and is robust to extreme expressions and poses while being faster than state-of-the-art methods.

关键词： 3d from single image And Body Pose deep Learning Face Gesture Motion and Tracking

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共10页 << < 1 2 3 4 5 6 7 8 9 10 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：