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Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Jordi Gonzalez |
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Title |
A survey on model based approaches for 2D and 3D visual human pose recovery |
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Journal Article |
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2014 |
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Sensors |
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SENS |
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14 |
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3 |
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4189-4210 |
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human pose recovery; human body modelling; behavior analysis; computer vision |
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Abstract |
Human Pose Recovery has been studied in the field of Computer Vision for the last 40 years. Several approaches have been reported, and significant improvements have been obtained in both data representation and model design. However, the problem of Human Pose Recovery in uncontrolled environments is far from being solved. In this paper, we define a general taxonomy to group model based approaches for Human Pose Recovery, which is composed of five main modules: appearance, viewpoint, spatial relations, temporal consistence, and behavior. Subsequently, a methodological comparison is performed following the proposed taxonomy, evaluating current SoA approaches in the aforementioned five group categories. As a result of this comparison, we discuss the main advantages and drawbacks of the reviewed literature. |
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HuPBA; ISE; 600.046; 600.063; 600.078;MILAB |
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Admin @ si @ PEA2014 |
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2443 |
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Oscar Lopes; Miguel Reyes; Sergio Escalera; Jordi Gonzalez |
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Title |
Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments |
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Journal Article |
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2014 |
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IEEE Transactions on Systems, Man and Cybernetics (Part B) |
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TSMCB |
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44 |
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12 |
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2379-2390 |
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The use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, such as Kinect. In this context, there is a strong need of powerful 3-D shape descriptors able to generate rich object representations. Although several 3-D descriptors have been already proposed in the literature, the research of discriminative and computationally efficient descriptors is still an open issue. In this paper, we propose a novel point cloud descriptor called spherical blurred shape model (SBSM) that successfully encodes the structure density and local variabilities of an object based on shape voxel distances and a neighborhood propagation strategy. The proposed SBSM is proven to be rotation and scale invariant, robust to noise and occlusions, highly discriminative for multiple categories of complex objects like the human hand, and computationally efficient since the SBSM complexity is linear to the number of object voxels. Experimental evaluation in public depth multiclass object data, 3-D facial expressions data, and a novel hand poses data sets show significant performance improvements in relation to state-of-the-art approaches. Moreover, the effectiveness of the proposal is also proved for object spotting in 3-D scenes and for real-time automatic hand pose recognition in human computer interaction scenarios. |
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2168-2267 |
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HuPBA; ISE; 600.078;MILAB |
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Admin @ si @ LRE2014 |
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2442 |
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Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez |
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Title |
End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth data |
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Journal Article |
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Year |
2022 |
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IET Computer Vision |
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IETCV |
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16 |
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1 |
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50-66 |
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Computer vision; data acquisition; human computer interaction; learning (artificial intelligence); pose estimation |
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Despite recent advances in 3D pose estimation of human hands, especially thanks to the advent of CNNs and depth cameras, this task is still far from being solved. This is mainly due to the highly non-linear dynamics of fingers, which make hand model training a challenging task. In this paper, we exploit a novel hierarchical tree-like structured CNN, in which branches are trained to become specialized in predefined subsets of hand joints, called local poses. We further fuse local pose features, extracted from hierarchical CNN branches, to learn higher order dependencies among joints in the final pose by end-to-end training. Lastly, the loss function used is also defined to incorporate appearance and physical constraints about doable hand motion and deformation. Finally, we introduce a non-rigid data augmentation approach to increase the amount of training depth data. Experimental results suggest that feeding a tree-shaped CNN, specialized in local poses, into a fusion network for modeling joints correlations and dependencies, helps to increase the precision of final estimations, outperforming state-of-the-art results on NYU and SyntheticHand datasets. |
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HUPBA; ISE; 600.098; 600.119 |
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Admin @ si @ MEB2022 |
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3652 |
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Author |
Egils Avots; Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Baro; Paul Pallin; Gholamreza Anbarjafari |
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Title |
From 2D to 3D geodesic-based garment matching |
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Journal Article |
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Year |
2019 |
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Multimedia Tools and Applications |
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MTAP |
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78 |
Issue |
18 |
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25829–25853 |
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Shape matching; Geodesic distance; Texture mapping; RGBD image processing; Gaussian mixture model |
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A new approach for 2D to 3D garment retexturing is proposed based on Gaussian mixture models and thin plate splines (TPS). An automatically segmented garment of an individual is matched to a new source garment and rendered, resulting in augmented images in which the target garment has been retextured using the texture of the source garment. We divide the problem into garment boundary matching based on Gaussian mixture models and then interpolate inner points using surface topology extracted through geodesic paths, which leads to a more realistic result than standard approaches. We evaluated and compared our system quantitatively by root mean square error (RMS) and qualitatively using the mean opinion score (MOS), showing the benefits of the proposed methodology on our gathered dataset. |
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HuPBA; ISE; 600.098; 600.119; 602.133 |
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Admin @ si @ AME2019 |
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3317 |
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Author |
Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon |
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Title |
Looking at People Special Issue |
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Journal Article |
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Year |
2018 |
Publication |
International Journal of Computer Vision |
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IJCV |
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Volume |
126 |
Issue |
2-4 |
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141-143 |
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HUPBA; ISE; 600.119 |
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Admin @ si @ EGJ2018 |
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3093 |
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