How Far 3D Shapes Can Be Understood from 2D Silhouettes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Heterogeneous Deformation Model for 3D Shape and Motion Recovery from Multi-Viewpoint Images
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Robust spectral 3D-bodypart segmentation along time
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
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This paper presents a new scheme for acquiring 3D kinematic structure and motion from time-series volume data, in particular, focusing on human body. Our basic strategy is to first represent the shape structure of the target in each frame by using aMRG, augmented Multiresolution Reeb Graph [6], and then deform each of the shape structures so that all of them can be identified as a common kinematic structure throughout the input frames. Although the shape structures can be very different from frame to frame, we propose to derive a unique kinematic structure by way of clustering some nodes of graph, based on the fact that they are partly coherent. The only assumption we make is that human body can be approximated by an articulated body with certain number of end-points and branches. We demonstrate the efficacy of the proposed scheme through some experiments