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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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no |
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Admin @ si @ MEB2022 |
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3652 |
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Author |
Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Roca; Felipe Lumbreras |
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Title |
Multi-part body segmentation based on depth maps for soft biometry analysis |
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Journal Article |
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Year |
2015 |
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Pattern Recognition Letters |
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PRL |
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56 |
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14-21 |
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3D shape context; 3D point cloud alignment; Depth maps; Human body segmentation; Soft biometry analysis |
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This paper presents a novel method extracting biometric measures using depth sensors. Given a multi-part labeled training data, a new subject is aligned to the best model of the dataset, and soft biometrics such as lengths or circumference sizes of limbs and body are computed. The process is performed by training relevant pose clusters, defining a representative model, and fitting a 3D shape context descriptor within an iterative matching procedure. We show robust measures by applying orthogonal plates to body hull. We test our approach in a novel full-body RGB-Depth data set, showing accurate estimation of soft biometrics and better segmentation accuracy in comparison with random forest approach without requiring large training data. |
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HuPBA; ISE; ADAS; 600.076;600.049; 600.063; 600.054; 302.018;MILAB |
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Admin @ si @ MEG2015 |
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2588 |
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Author |
Meysam Madadi; Sergio Escalera; Alex Carruesco Llorens; Carlos Andujar; Xavier Baro; Jordi Gonzalez |
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Title |
Top-down model fitting for hand pose recovery in sequences of depth images |
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2018 |
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Image and Vision Computing |
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IMAVIS |
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79 |
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63-75 |
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State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. We evaluate our approach on a new created synthetic hand dataset along with NYU and MSRA real datasets. Results demonstrate that the proposed method outperforms the most recent pose recovering approaches, including those based on CNNs. |
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HUPBA; 600.098 |
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Admin @ si @ MEC2018 |
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3203 |
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Author |
Meysam Madadi; Hugo Bertiche; Sergio Escalera |
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Title |
SMPLR: Deep learning based SMPL reverse for 3D human pose and shape recovery |
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Journal Article |
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Year |
2020 |
Publication |
Pattern Recognition |
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PR |
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106 |
Issue |
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Pages |
107472 |
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Deep learning; 3D Human pose; Body shape; SMPL; Denoising autoencoder; Volumetric stack hourglass |
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In this paper we propose to embed SMPL within a deep-based model to accurately estimate 3D pose and shape from a still RGB image. We use CNN-based 3D joint predictions as an intermediate representation to regress SMPL pose and shape parameters. Later, 3D joints are reconstructed again in the SMPL output. This module can be seen as an autoencoder where the encoder is a deep neural network and the decoder is SMPL model. We refer to this as SMPL reverse (SMPLR). By implementing SMPLR as an encoder-decoder we avoid the need of complex constraints on pose and shape. Furthermore, given that in-the-wild datasets usually lack accurate 3D annotations, it is desirable to lift 2D joints to 3D without pairing 3D annotations with RGB images. Therefore, we also propose a denoising autoencoder (DAE) module between CNN and SMPLR, able to lift 2D joints to 3D and partially recover from structured error. We evaluate our method on SURREAL and Human3.6M datasets, showing improvement over SMPL-based state-of-the-art alternatives by about 4 and 12 mm, respectively. |
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HuPBA; no proj |
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no |
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Call Number |
Admin @ si @ MBE2020 |
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3439 |
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Author |
Meysam Madadi; Hugo Bertiche; Sergio Escalera |
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Title |
Deep unsupervised 3D human body reconstruction from a sparse set of landmarks |
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Journal Article |
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Year |
2021 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
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Volume |
129 |
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Pages |
2499–2512 |
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In this paper we propose the first deep unsupervised approach in human body reconstruction to estimate body surface from a sparse set of landmarks, so called DeepMurf. We apply a denoising autoencoder to estimate missing landmarks. Then we apply an attention model to estimate body joints from landmarks. Finally, a cascading network is applied to regress parameters of a statistical generative model that reconstructs body. Our set of proposed loss functions allows us to train the network in an unsupervised way. Results on four public datasets show that our approach accurately reconstructs the human body from real world mocap data. |
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HUPBA; no proj |
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no |
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Admin @ si @ MBE2021 |
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3654 |
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