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

4,653 篇 会议
2 篇 期刊文献

馆藏范围

4,655 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,715 篇 工学
- 1,623 篇 计算机科学与技术...
- 182 篇 软件工程
- 142 篇 机械工程
- 133 篇 光学工程
- 41 篇 生物工程
- 29 篇 信息与通信工程
- 18 篇 电气工程
- 9 篇 电子科学与技术（可...
- 9 篇 化学工程与技术
- 9 篇 交通运输工程
- 8 篇 控制科学与工程
- 8 篇 生物医学工程（可授...
- 7 篇 安全科学与工程
- 4 篇 材料科学与工程（可...
- 4 篇 建筑学
- 3 篇 土木工程
- 3 篇 农业工程
173 篇 理学
- 135 篇 物理学
- 42 篇 生物学
- 30 篇 数学
- 16 篇 统计学（可授理学、...
- 10 篇 化学
- 2 篇 大气科学
14 篇 管理学
- 7 篇 管理科学与工程(可...
- 7 篇 图书情报与档案管...
- 3 篇 工商管理
10 篇 医学
- 10 篇 临床医学
5 篇 法学
- 3 篇 社会学
- 2 篇 法学
3 篇 教育学
- 3 篇 教育学
2 篇 农学
1 篇 经济学

主题

2,867 篇 computer vision
1,227 篇 training
1,038 篇 pattern recognit...
870 篇 computational mo...
794 篇 conferences
693 篇 visualization
593 篇 three-dimensiona...
469 篇 codes
460 篇 benchmark testin...
422 篇 semantics
420 篇 computer archite...
349 篇 accuracy
301 篇 adaptation model...
282 篇 feature extracti...
267 篇 transformers
242 篇 cameras
225 篇 diffusion models
223 篇 solid modeling
214 篇 pipelines
210 篇 measurement

机构

72 篇 tsinghua univers...
69 篇 zhejiang univers...
58 篇 university of sc...
57 篇 shanghai jiao to...
52 篇 google research
47 篇 nanyang technolo...
44 篇 national univers...
40 篇 shanghai ai labo...
39 篇 university of ch...
37 篇 adobe research
37 篇 the university o...
37 篇 the chinese univ...
35 篇 stanford univers...
34 篇 harbin institute...
34 篇 shanghai artific...
34 篇 carnegie mellon ...
33 篇 university of el...
30 篇 peng cheng labor...
30 篇 sun yat-sen univ...
29 篇 s-lab nanyang te...

作者

75 篇 timofte radu
24 篇 yu qiao
22 篇 luc van gool
19 篇 ying shan
16 篇 van gool luc
15 篇 radu timofte
14 篇 xin li
13 篇 li xin
12 篇 chen chen
12 篇 zhang zhao
12 篇 boxin shi
11 篇 lizhuang ma
11 篇 fan haoqiang
11 篇 loy chen change
11 篇 zheng-jun zha
11 篇 liu shuaicheng
11 篇 kai zhang
11 篇 marcos v. conde
11 篇 chen wei-ting
11 篇 ziwei liu

语言

4,654 篇 英文
1 篇 中文

检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024"

共 4655 条记录，以下是4641-4650 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：

Rapid 3D Model Generation with Intuitive 3D Input

Rapid 3D Model Generation with Intuitive 3D Input

引用

conference on computer vision and pattern recognition (CVPR)

作者： Tianrun Chen Chaotao Ding Shangzhan Zhang Chunan Yu Ying Zang Zejian Li Sida Peng Lingyun Sun College of Computer Science and Technology Zhejiang University KOKONI3D Moxin (Huzhou) Technology Co. LTD. School of Information Engineering Huzhou University School of Software Technology Zhejiang University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

With the emergence of AR/VR, 3D models are in tremendous demand. However, conventional 3D modeling with computer-Aided Design software requires much expertise and is difficult for novice users. We find that AR/VR devices, in addition to serving as effective display mediums, can offer a promising potential as an intuitive 3D model creation tool, especially with the assistance of AI generative models. Here, we propose Deep3DVRSketch, the first 3D model generation network that inputs 3D VR sketches from novice users and generates highly consistent 3D models in multiple categories within seconds, irrespective of the users' drawing abilities. We also contribute KO3D+, the largest 3D sketch-shape dataset. Our method pre-trains a conditional diffusion model on quality 3D data, then fine-tunes an encoder to map 3D sketches onto the generator's manifold using an adaptive curriculum strategy for limited ground truths. In our experiment, our approach achieves state-of-the-art performance in both model quality and fidelity with real-world input from novice users, and users can even draw and obtain very detailed geometric structures. In our user study, users were able to complete the 3D modeling tasks over 10 times faster using our approach compared to conventional CAD software tools. We believe that our Deep3DVRSketch and KO3D+ dataset can offer a promising solution for future 3D modeling in metaverse era. Check the project page at http://***/Deep3DVRSketch.

关键词： Training Solid modeling Adaptation models Three-dimensional displays Metaverse Shape Computational modeling

来源：评论

学校读者我要写书评

暂无评论

Continual Self-Supervised Learning: Towards Universal Multi-Modal Medical Data Representation Learning

Continual Self-Supervised Learning: Towards Universal Multi-...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Qi Wu Yong Xia School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning (AIML) The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Research & Development Institute of Northwestern Polytechnical University in Shenzhen China Ningbo Institute of Northwestern Polytechnical University China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Self-supervised learning (SSL) is an efficient pre-training method for medical image analysis. However, current research is mostly confined to certain modalities, consuming considerable time and resources without achieving universality across different modalities. A straightforward solution is combining all modality data for joint SSL, which poses practical challenges. Firstly, our experiments reveal conflicts in representation learning as the number of modalities increases. Secondly, multi-modal data collected in advance cannot cover all real-world scenarios. In this paper, we reconsider versatile SSL from the perspective of continual learning and propose MedCoSS, a continuous SSL approach for multi-modal medical data. Different from joint representation learning, MedCoSS assigns varying data modalities to separate training stages, creating a multi-stage pre-training process. We propose a rehearsal- based continual learning approach to manage modal conflicts and prevent catastrophic forgetting. Specifically, we use the k-means sampling to retain and rehearse previous modality data during new modality learning. Moreover, we apply feature distillation and intra-modal mixup on buffer data for knowledge retention, bypassing pretext tasks. We conduct experiments on a large-scale multi-modal unlabeled dataset, including clinical reports, X-rays, CT, MRI, and pathological images. Experimental results demonstrate MedCoSS's exceptional generalization ability across 9 downstream datasets and its significant scalability in inte- grating new modality data. The code and pre-trained model are available at https://***/yeerwen/MedCoSS.

关键词： Representation learning Continuing education Training Pathology Scalability Magnetic resonance imaging X-rays

来源：评论

学校读者我要写书评

暂无评论

Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers

Triplane Meets Gaussian Splatting: Fast and Generalizable Si...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zi–Xin Zou Zhipeng Yu Yuan–Chen Guo Yangguang Li Ding Liang Yan–Pei Cao Song–Hai Zhang BNRist Tsinghua University VAST Qinghai University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recent advancements in 3D reconstruction from single images have been driven by the evolution of generative models. Prominent among these are methods based on Score Distillation Sampling (SDS) and the adaptation ofdiffusion models in the 3D domain. Despite their progress, these techniques often face limitations due to slow optimization or rendering processes, leading to extensive training and optimization times. In this paper, we introduce a novel approach for single-view reconstruction that efficiently generates a 3D model from a single image via feed-forward inference. Our method utilizes two transformer-based networks, namely a point decoder and a triplane decoder, to reconstruct 3D objects using a hybrid Triplane-Gaussian intermediate representation. This hybrid representation strikes a balance, achieving a faster rendering speed compared to implicit representations while simultaneously delivering superior rendering quality than explicit representations. The point decoder is designed for generating point clouds from single images, offering an explicit representation which is then utilized by the triplane decoder to query Gaussian features for each point. This design choice addresses the challenges associated with directly regressing explicit 3D Gaussian attributes characterized by their non-structural nature. Subsequently, the 3D Gaussians are decoded by an MLP to enable rapid rendering through splatting. Both decoders are built upon a scalable, transformer-based architecture and have been efficiently trained on large-scale 3D datasets. The evaluations conducted on both synthetic datasets and real-world images demonstrate that our method not only achieves higher quality but also ensures a faster runtime in comparison to previous state-of-the-art techniques. Please see our project page at https://***/TriplaneGaussian/

关键词： Training Solid modeling Adaptation models Three-dimensional displays Rendering (computer graphics) Transformers Decoding

来源：评论

学校读者我要写书评

暂无评论

Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-Ray Expert Models

Bootstrapping Chest CT Image Understanding by Distilling Kno...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Weiwei Cao Jianpeng Zhang Yingda Xia Tony C. W. Mok Zi Li Xianghua Ye Le Lu Jian Zheng Yuxing Tang Ling Zhang DAMO Academy Alibaba Group University of Science and Technology of China China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences China College of Computer Science and Technology Zhejiang University China Hupan Lab Hangzhou China The First Affiliated Hospital of College of Medicine Zhejiang University China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Radiologists highly desire fully automated versatile AI for medical imaging interpretation. However, the lack of extensively annotated large-scale multi-disease datasets has hindered the achievement of this goal. In this paper, we explore the feasibility of leveraging language as a natu-rally high-quality supervision for chest CT imaging. In light of the limited availability of image-report pairs, we boot-strap the understanding of 3D chest CT images by distilling chest-related diagnostic knowledge from an extensively pre-trained 2D X-ray expert model. Specifically, we propose a language-guided retrieval method to match each 3D CT image with its semantically closest 2D X-ray image, and perform pair-wise and semantic relation knowledge distillation. Subsequently, we use contrastive learning to align images and reports within the same patient while distin-guishing them from the other patients. However, the challenge arises when patients have similar semantic diagnoses, such as healthy patients, potentially confusing if treated as negatives. We introduce a robust contrastive learning that identifies and corrects these false negatives. We train our model with over 12K pairs of chest CT images and radiology reports. Extensive experiments across multiple scenarios, including zero-shot learning, report generation, and fine-tuning processes, demonstrate the model's feasibility in interpreting chest CT images.

关键词： Solid modeling Three-dimensional displays Zero-shot learning Computed tomography Computational modeling Semantics Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

VS: Reconstructing Clothed 3D Human from Single Image via Vertex Shift

VS: Reconstructing Clothed 3D Human from Single Image via Ve...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Leyuan Liu Yuhan Li Yunqi Gao Changxin Gao Yuanyuan Liu Jingying Chen National Engineering Research Center for E-Learning Central China Normal University School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Computer Science China University of Geosciences (Wuhan)

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Various applications require high-fidelity and artifact free 3D human reconstructions. However, current implicit function-based methods inevitably produce artifacts while existing deformation methods are difficult to reconstruct high-fidelity humans wearing loose clothing. In this paper, we propose a two-stage deformation method named Vertex Shift (VS) for reconstructing clothed 3D humans from single images. Specifically, VS first stretches the estimated SMPL-X mesh into a coarse 3D human model using shift fields inferred from normal maps, then refines the coarse 3D human model into a detailed 3D human model via a graph convolutional network embedded with implicit-function-learned features. This “stretch-refine” strategy addresses large deformations required for reconstructing loose clothing and delicate deformations for recovering intricate and detailed surfaces, achieving high-fidelity reconstructions that faithfully convey the pose, clothing, and surface details from the input images. The graph convolutional network's ability to exploit neighborhood vertices coupled with the advantages inherited from the deformation methods ensure VS rarely produces artifacts like distortions and non-human shapes and never produces artifacts like holes, broken parts, and dismembered limbs. As a result, VS can reconstruct highfidelity and artifact-less clothed 3D humans from single images, even under scenarios of challenging poses and loose clothing. Experimental results on three benchmarks and two in-the-wild datasets demonstrate that VS significantly outperforms current state-of-the-art methods. The code and models of VS are available for research purposes at https://***/starvisionTeam/VS.

关键词： Solid modeling Surface reconstruction Three-dimensional displays Deformation Shape Graph convolutional networks Clothing

来源：评论

学校读者我要写书评

暂无评论

BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning

BT-Adapter: Video Conversation is Feasible Without Video Ins...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Ruyang Liu Chen Li Yixiao Ge Thomas H. Li Ying Shan Ge Li School of Electronic and Computer Engineering Shenzhen Graduate School Peking University Peng Cheng Laboratory ARC Lab Tencent PCG Applied Research Center (ARC) Tencent PCG

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The recent progress in Large Language Models (LLM) has spurred various advancements in image-language con-versation agents, while how to build a proficient video-based dialogue system is still under exploration. Consid-ering the extensive scale of LLM and visual backbone, min-imal GPU memory is left for facilitating effective temporal modeling, which is crucial for comprehending and providing feedback on videos. To this end, we propose Branching Temporal Adapter (BT-Adapter), a novel method for ex-tending image-language pretrained models into the video domain. Specifically, BT-Adapter serves as a plug-and-use temporal modeling branch alongside the pretrained vi-sual encoder, which is tuned while keeping the backbone frozen. Just pretrained once, BT-Adapter can be seamlessly integrated into all image conversation models using this version of CLIP, enabling video conversations without the need for video instructions. Besides, we develop a unique asymmetric token masking strategy inside the branch with tailor-made training tasks for BT-Adapter, facilitating faster convergence and better results. Thanks to BT-Adapter, we are able to empower existing multimodal dialogue models with strong video understanding capabilities without incur-ring excessive GPU costs. Without bells and whistles, BT-Adapter achieves (1) state-of-the-art zero-shot results on various video tasks using thousands of fewer GPU hours. (2) better performance than current video chatbots without any video instruction tuning. (3) state-of-the-art results of video chatting using video instruction tuning, outperforming previous SOTAs by a large margin. The code has been available at https://***/farewellthreeIBT-Adapter.

关键词： Training Adaptation models Visualization Costs Computational modeling Graphics processing units Oral communication

来源：评论

学校读者我要写书评

暂无评论

Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields

Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shijie Zhou Haoran Chang Sicheng Jiang Zhiwen Fan Zehao Zhu Dejia Xu Pradyumna Chari Suya You Zhangyang Wang Achuta Kadambi 1University of California Los Angeles University of Texas at Austin DEVCOM Army Research Laboratory

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

3D scene representations have gained immense popularity in recent years. Methods that use Neural Radiance fields are versatile for traditional tasks such as novel view synthesis. In recent times, some work has emerged that aims to extend the functionality of NeRF beyond view synthesis, for semantically aware tasks such as editing and segmentation using 3D feature field distillation from 2D foundation models. However, these methods have two major limitations: (a) they are limited by the rendering speed of NeRF pipelines, and (b) implicitly represented feature fields suffer from continuity artifacts reducing feature quality. Recently, 3D Gaussian Splatting has shown state-of-the-art performance on real-time radiance field rendering. In this work, we go one step further: in addition to radiance field rendering, we enable 3D Gaussian splatting on arbitrary-dimension semantic features via 2D foundation model distillation. This translation is not straightforward: naively incorporating feature fields in the 3DGS framework encounters significant challenges, notably the disparities in spatial resolution and channel consistency between RGB images and feature maps. We propose architectural and training changes to efficiently avert this problem. Our proposed method is general, and our experiments showcase novel view semantic segmentation, language-guided editing and segment anything through learning feature fields from state-of-the-art 2D foundation models such as SAM and CLIP-LSeg. Across experiments, our distillation method is able to provide comparable or better results, while being significantly faster to both train and render. Additionally, to the best of our knowledge, we are the first method to enable point and bounding-box prompting for radiance field manipulation, by leveraging the SAM model. Project website at: https://***/.

关键词： Training Three-dimensional displays Semantic segmentation Semantics Pipelines Rendering (computer graphics) Neural radiance field

来源：评论

学校读者我要写书评

暂无评论

URHand: Universal Relightable Hands

URHand: Universal Relightable Hands

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zhaoxi Chen Gyeongsik Moon Kaiwen Guo Chen Cao Stanislav Pidhorskyi Tomas Simon Rohan Joshi Yuan Dong Yichen Xu Bernardo Pires He Wen Lucas Evans Bo Peng Julia Buffalini Autumn Trimble Kevyn McPhail Melissa Schoeller Shoou-I Yu Javier Romero Michael Zollhöfer Yaser Sheikh Ziwei Liu Shunsuke Saito Codec Avatars Lab Meta Nanyang Technological University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing photorealistic relightable hand models require extensive identity-specific observations in different views, poses, and illuminations, and face challenges in generalizing to natural illuminations and novel identities. To bridge this gap, we present URHand, the first universal relightable hand model that generalizes across viewpoints, poses, illuminations, and identities. Our model allows few-shot personalization using images captured with a mobile phone, and is ready to be photorealistically rendered under novel illuminations. To simplify the personalization process while retaining photorealism, we build a powerful universal relightable prior based on neural relighting from multi-view images of hands captured in a light stage with hundreds of identities. The key challenge is scaling the cross-identity training while maintaining personalized fidelity and sharp details without compromising generalization under natural illuminations. To this end, we propose a spatially varying linear lighting model as the neural renderer that takes physics-inspired shading as input feature. By removing non-linear activations and bias, our specifically designed lighting model explicitly keeps the linearity of light transport. This enables single-stage training from light-stage data while generalizing to real-time rendering under arbitrary continuous illuminations across diverse identities. In addition, we introduce the joint learning of a physically based model and our neural relighting model, which further improves fidelity and generalization. Extensive experiments show that our approach achieves superior performance over existing methods in terms of both quality and generalizability. We also demonstrate quick personalization of URHand from a short phone scan of an unseen identity.

关键词： Training Photorealism computer vision Lighting Linearity Rendering (computer graphics) Real-time systems

来源：评论

学校读者我要写书评

暂无评论

DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D vision

DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-bas...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Lu Ling Yichen Sheng Zhi Tu Wentian Zhao Cheng Xin Kun Wan Lantao Yu Qianyu Guo Zixun Yu Yawen Lu Xuanmao Li Xingpeng Sun Rohan Ashok Aniruddha Mukherjee Hao Kang Xiangrui Kong Gang Hua Tianyi Zhang Bedrich Benes Aniket Bera Purdue University Adobe Inc. Rutgers University Google Inc. Huazhong University of Science and Technology Wormpex AI Research

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However, existing scene-level datasets for deep learning-based 3D vision, limited to ei-ther synthetic environments or a narrow selection of real-world scenes, are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap, we present DL3DV-10K, a large-scale scene dataset, featuring 51.2 million frames from 10,510 videos captured from 65 types of point- of-interest (POI) locations, covering both bounded and unbounded scenes, with different levels of reflection, transparency, and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K, which revealed valuable insights for future research in NVS. In addition, we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K, which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation. Our DL3DV-10K dataset, benchmark results, and models will be publicly accessible.

关键词： Representation learning Solid modeling Three-dimensional displays Lighting Benchmark testing Neural radiance field Reflection

来源：评论

学校读者我要写书评

暂无评论

3D Facial Expressions through Analysis-by-Neural-Synthesis

3D Facial Expressions through Analysis-by-Neural-Synthesis

引用

conference on computer vision and pattern recognition (CVPR)

作者： George Retsinas Panagiotis P. Filntisis Radek Daněček Victoria F. Abrevaya Anastasios Roussos Timo Bolkarr Petros Maragos Institute of Robotics Athena Research Center Maroussi Greece MPI for Intelligent Systems Tübingen Germany Institute of Computer Science (ICS) Foundation for Research & Technology - Hellas (FORTH) Greece School of Electrical & Computer Engineering National Technical University of Athens Greece

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

While existing methods for 3D face reconstruction from in-the-wild images excel at recovering the overall face shape, they commonly miss subtle, extreme, asymmetric, or rarely observed expressions. We improve upon these meth-ods with SMIRK (Spatial Modeling for Image-based Reconstruction of Kinesics), which faithfully reconstructs expres-sive 3D faces from images. We identify two key limitations in existing methods: shortcomings in their self-supervised training formulation, and a lack of expression diversity in the training images. For training, most methods employ differentiable rendering to compare a predicted face mesh with the input image, along with a plethora of additional loss functions. This differentiable rendering loss not only has to provide supervision to optimize for 3D face geom-etry, camera, albedo, and lighting, which is an ill-posed optimization problem, but the domain gap between ren-dering and input image further hinders the learning pro-cess. Instead, SMIRK replaces the differentiable rendering with a neural rendering module that, given the ren-dered predicted mesh geometry, and sparsely sampled pix-els of the input image, generates a face image. As the neural rendering gets color information from sampled im-age pixels, supervising with neural rendering-based reconstruction loss can focus solely on the geometry. Further it enables us to generate images of the input identity with varying expressions while training. These are then utilized as input to the reconstruction model and used as supervision with ground truth geometry. This effectively augments the training data and enhances the generalization for di-verse expressions. Our qualitative, quantitative and partic-ularly our perceptual evaluations demonstrate that SMIRK achieves the new state-of-the art performance on accurate expression reconstruction. For our method's source code, demo video and more, please visit our project webpage: https://***/smirk/.

关键词： Training Geometry Solid modeling Three-dimensional displays Accuracy Face recognition Training data

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466页 << < 457 458 459 460 461 462 463 464 465 466 > >>

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：