An efficient volumetric framework for shape tracking

Allain, Benjamin; Franco, Jean-Sébastien; Boyer, Edmond

doi:10.1109/cvpr.2015.7298623

Cited by 43 publications

(47 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A comparison of the result with those of three state-of-the-art methods (Liu et al, 2010;Bilir and Yemez, 2012;Allain et al, 2015) is listed in Table 3. The results support the intuitiveness of our method efficiency between neighboring frames.…”

Section: Model-level Interpolationmentioning

confidence: 99%

“…A comparison with state-of-the-art methods (Liu et al, 2010;Bilir and Yemez, 2012;Allain et al, 2015) is shown in Table 4, which shows quantitative evaluation of the average reconstruction time per frame. Although the method of Bilir and Yemez (2012) showed a good result, our method still sped up the reconstruction.…”

Section: Model-level Interpolationmentioning

confidence: 99%

“…Because λ max and γ max have a strong impact on quality as well as speed, and determine the denseness of the point cloud, to obtain the balance between speed and denseness, an appropriate value should be selected according to the different scenes and requirements. Bilir and Yemez (2012) 67.45 Allain et al (2015) 80.85 Our method 52.59…”

Section: Model-level Interpolationmentioning

confidence: 99%

“…Redskirt Liu et al (2010) 145.45 Bilir and Yemez (2012) 68.45 Allain et al (2015) 80.78 Our method 56.54 …”

Section: Model-level Interpolationmentioning

confidence: 99%

“…Recently, with the development of acquisition techniques, 3D reconstruction of real dynamic scenes has been attracting more research effort (Matsuyama et al, 2004;Hasler et al, 2009;Vlasic et al, 2009;Liu et al, 2010;Taneja et al, 2011;Zhang et al, 2011;Bilir Frontiers of Information Technology & Electronic Engineering www.zju.edu.cn/jzus;engineering.cae.cn;www.springerlink.com ISSN 2095-9184 (print); ISSN 2095-9230 (online) E-mail: jzus@zju.edu.cn and Yemez, 2012; Nakajima et al, 2012;Nakazawa et al, 2012). In particular, tracking for human shape and motion capture has become an important research area (Hofmann and Davrila, 2009;Kanaujia et al, 2011;Huang et al, 2014b;Allain et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A multiscale-contour-based interpolation framework for generating a time-varying quasi-dense point cloud sequence

Huang

Diao

2016

Frontiers Inf Technol Electronic Eng

View full text Add to dashboard Cite

To speed up the reconstruction of 3D dynamic scenes in an ordinary hardware platform, we propose an efficient framework to reconstruct 3D dynamic objects using a multiscale-contour-based interpolation from multi-view videos. Our framework takes full advantage of spatio-temporal-contour consistency. It exploits the property to interpolate single contours, two neighboring contours which belong to the same model, and two contours which belong to the same view at different times, corresponding to point-, contour-, and model-level interpolations, respectively. The framework formulates the interpolation of two models as point cloud transport rather than non-rigid surface deformation. Our framework speeds up the reconstruction of a dynamic scene while improving the accuracy of point-pairing which is used to perform the interpolation. We obtain a higher frame rate, spatio-temporal-coherence, and a quasi-dense point cloud sequence with color information. Experiments with real data were conducted to test the efficiency of the framework.

show abstract

Section: Model-level Interpolationmentioning

confidence: 99%

Section: Model-level Interpolationmentioning

confidence: 99%

Section: Model-level Interpolationmentioning

confidence: 99%

“…Redskirt Liu et al (2010) 145.45 Bilir and Yemez (2012) 68.45 Allain et al (2015) 80.78 Our method 56.54 …”

Section: Model-level Interpolationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A multiscale-contour-based interpolation framework for generating a time-varying quasi-dense point cloud sequence

Huang

Diao

2016

Frontiers Inf Technol Electronic Eng

View full text Add to dashboard Cite

show abstract

Analyzing Clothing Layer Deformation Statistics of 3D Human Motions

Yang

Franco

Hétroy-Wheeler

et al. 2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Recent capture technologies and methods allow not only to retrieve 3D model sequence of moving people in clothing, but also to separate and extract the underlying body geometry, motion component and the clothing as a geometric layer. So far this clothing layer has only been used as raw offsets for individual applications such as retargeting a different body capture sequence with the clothing layer of another sequence, with limited scope, e.g. using identical or similar motions. The structured, semantics and motion-correlated nature of the information contained in this layer has yet to be fully understood and exploited. To this purpose we propose a comprehensive analysis of the statistics of this layer with a simple two-component model, based on PCA subspace reduction of the layer information on one hand, and a generic parameter regression model using neural networks on the other hand, designed to regress from any semantic parameter whose variation is observed in a training set, to the layer parameterization space. We show that this model not only allows to reproduce previous retargeting works, but generalizes the data generation capabilities to other semantic parameters such as clothing variation and size, or physical material parameters with synthetically generated training sequence, paving the way for many kinds of capture data-driven creation and augmentation applications.

show abstract