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Once you download and extract the zip with the pre-trained model you should have the following files:
- all_means.pkl : The mean pose parameters, which are used as the initial point for the iterative regression, in different pose representations ( axis-angle, PCA for the hands only, etc).
- shape_mean.npy: The mean shape parameters used to initialize the iterative regressor.
- SMPLX_to_J14.pkl: A linear regressor that computes the 14 LSP-like joints used to compute the mean per-joint point error (MPJPE).
- conf.yaml: Contains all the arguments needed to run ExPose.
- checkpoints: The pre-trained checkpoint.
- ExPose Dataset - Documentation
Downloading and extracting the curated fits zip should give you the following two files:
- train.npz
- img_fns: The name of the image to read.
- betas: A Nx10 numpy array with the shape coefficients of each instance.
- expression: A Nx10 numpy array with the expression coefficients of each instance.
- keypoints2D: The OpenPose keypoints used to generate the fits.
- pose: A numpy array that contains the estimated SMPL-X pose vector in axis-angle format.
- val.npz
- img_fns: The name of the image to read.
- betas: A Nx10 numpy array with the shape coefficients of each instance.
- expression: A Nx10 numpy array with the expression coefficients of each instance.
- keypoints2D: The OpenPose keypoints used to generate the fits.
- pose: A numpy array that contains the estimated SMPL-X pose vector in axis-angle format.
- vertices: A numpy array that contains the estimated SMPL-X vertices.
- joints: The 14 LSP-like joints used to compute the mean per-joint point error metric.
The data format is exactly the same as the one in SPIN, see the original page for more details.