中国科学院机构知识库网格系统: 检索

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	PyMAF-X: Towards Well-Aligned Full-Body Model Regression From Monocular Images 期刊论文 OAI收割 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 10, 页码: 12287-12303 作者: Zhang, Hongwen; Tian, Yating; Zhang, Yuxiang; Li, Mengcheng; An, Liang \| 收藏 \| 浏览/下载：25/0 \| 提交时间：2023/11/16 Expressive human mesh recovery full-body motion capture mesh alignment feedback monocular 3D reconstruction
	Human motion tracking with less constraint of initial posture from a single rgb-d sensor 期刊论文 OAI收割 Sensors, 2021, 卷号: 21, 期号: 9, 页码: 1-16 作者: Liu, Chen; Wang AN(王安娜); Bu CG(卜春光); Wang, Wenhui; Sun, Haijing \| 收藏 \| 浏览/下载：26/0 \| 提交时间：2021/05/10 4D reconstruction human motion capture RGB-D sensor
	Data-driven human model estimation for realtime motion capture 期刊论文 OAI收割 JOURNAL OF VISUAL LANGUAGES AND COMPUTING, 2018, 卷号: 48, 页码: 10-18 作者: Zhai, Wenpeng; Su, Le; Xia, Shihong; Liao, Lianjun \| 收藏 \| 浏览/下载：37/0 \| 提交时间：2019/04/03 Human model estimation Data driven Human motion capture Depth image
	A Survey on Human Performance Capture and Animation 期刊论文 OAI收割 JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2017, 卷号: 32, 期号: 3, 页码: 536-554 作者: Xia, Shihong; Gao, Lin; Lai, Yu-Kun; Yuan, Ming-Ze; Chai, Jinxiang \| 收藏 \| 浏览/下载：28/0 \| 提交时间：2019/12/12 human surface reconstruction body motion capture motion synthesis physics-based motion simulation
	Mining Spatial-Temporal Patterns and Structural Sparsity for Human Motion Data Denoising 期刊论文 OAI收割 ieee transactions on cybernetics, 2015, 卷号: 45, 期号: 12, 页码: 2693-2706 作者: Feng, Yinfu; Ji, Mingming; Xiao, Jun; Yang, Xiaosong; Zhang, Jian J. 收藏 \| 浏览/下载：30/0 \| 提交时间：2015/12/25 Human motion denoising l(2 Microsoft Kinect motion capture data robust dictionary learning robust structured sparse coding p)-norm
	三维人体骨架模型的运动编辑与合成学位论文 OAI收割工学硕士, 中国科学院自动化研究所: 中国科学院研究生院, 2011 郭晓乐收藏 \| 浏览/下载：74/0 \| 提交时间：2015/09/02 运动编辑运动合成人体骨架模型运动捕获数据 motion editing motion synthesis human skeleton model motion capture data
	The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China 作者: Sun H.; Han H.-X.; Sun H. 收藏 \| 浏览/下载：57/0 \| 提交时间：2013/03/25 Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring precision and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure but in order to capture the change of the state space it need a certain amount of particles to ensure samples is enough and this number will increase in accompany with dimension and increase exponentially this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining" we expand the classic Mean Shift tracking framework.Based on the previous perspective we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information this approach also inhibit interference from the background ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
	An Efficient Skeleton-Free Pose Retargetting Method for Triangular Meshes 期刊论文 OAI收割 CHINESE JOURNAL OF ELECTRONICS, 2008, 卷号: 17, 期号: 4, 页码: 659-664 作者: Wu Huaiyu; Pan Chunhong; Zeng Kun; Yang Qing; Ma Songde 收藏 \| 浏览/下载：22/0 \| 提交时间：2015/11/08 Pose retargetting Human motion animation Skeleton-free mesh deformation Motion capture data