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| Author | Meysam Madadi; Hugo Bertiche; Sergio Escalera | ||||
| Title | SMPLR: Deep learning based SMPL reverse for 3D human pose and shape recovery | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 106 | Issue | Pages | 107472 | |
| Keywords | Deep learning; 3D Human pose; Body shape; SMPL; Denoising autoencoder; Volumetric stack hourglass | ||||
| Abstract | 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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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ MBE2020 | Serial | 3439 | ||
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| Author | Meysam Madadi; Hugo Bertiche; Sergio Escalera | ||||
| Title | Deep unsupervised 3D human body reconstruction from a sparse set of landmarks | Type | Journal Article | ||
| Year | 2021 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 129 | Issue | Pages | 2499–2512 | |
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| Abstract | 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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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ MBE2021 | Serial | 3654 | ||
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