Akash Sengupta

Hey, I'll be putting up evaluation code eventually, but am rather busy so now so cannot promise any timelines unfortunately.

Hi, Pytorch3D 0.5.0 had some [breaking changes](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.5.0) regarding camera conventions. If you want to use a Pytorch3D 0.5.0 or later, you will need to modify the camera code in the...

Cool, thanks @asluborski. Are the visualisations as expected? If so, I will point future issues on this topic to this thread.

Formatting ``` if projection_type == 'perspective': self.cameras = PerspectiveCameras(device=device, R=cam_R, T=cam_t, focal_length=perspective_focal_length, principal_point=((img_wh/2., img_wh/2.),), image_size=((img_wh, img_wh),), in_ndc=False) elif projection_type == 'orthographic': self.cameras = OrthographicCameras(device=device, R=cam_R, T=cam_t, focal_length=orthographic_scale*(img_wh/2.), principal_point=((img_wh / 2.,...

To go from a rotation matrix to the 6D representation, I believe you simply take the first two columns of the 3x3 rotation matrix (and concatenate them together if needed).

Hi, The mode of the predicted distribution over SMPL shape and pose can be obtained from [here](https://github.com/akashsengupta1997/HierarchicalProbabilistic3DHuman/blob/d80b7faa11e3824a3a4ccdaa8d20912228cae949/predict/predict_poseMF_shapeGaussian_net.py#L103-L104). Specifically, mode body pose is saved as 23 3x3 rotation matrices in `pred_pose_rotmats_mode`,...

SMPL pose parameters are the 3D rotations of each joint in the kinematic tree. There are 23 body joints + 1 root joint, so 24 in total. If you represent...

Hey! That's odd, IIRC the script used to work with the dataset as extracted. I will take a look this weekend and get back to you.