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## Discussion
* **Moderation**: The weight from moderators tell us how much we should trust
the feature (information) from body image or part (head/hands) crops, the
animation figure shows the visualization of the weights: the lighter color
means it trust body more.
* **Face from SMPL-X model**: In PIXIE, we use a common shape variable for the body and the face.
The shape space of SMPL-X is learned from
3800 real full body scans and captures the correlation between body and
face shape. In the samples script we support the option of predicting
the shape of the full body from a face-only image. If you are only interested in
getting accurate face shape,
we suggest to try other face-specifc work, such as [DECA](https://github.com/YadiraF/DECA).
* **Model conversion**: If you want to convert the predicted SMPL-X body to a different body model,
e.g. SMPL, please take a look [here](https://github.com/vchoutas/smplx/blob/master/transfer_model/README.md)
### Limitations:
* **Cropping matters:** Even though we already did a lot of data
augmentations durining training, the results will still vary a bit due to
cropping differences for the input images. For
simplicity, we chose Faster-RCNN as a person detector,
while in the paper we used OpenPose keypoints to compute a bounding box.
* **Perspective projection**: We use a scaled-orthographic/weak-perspective camera model in PIXIE, which
does not work well for images with strong perpective distortion.
* **Misalignment issue**: For regression works that output model
(SMPL/SMPL-X) parameters, misalignment with the person in the image is always an issue.
This can be improved by using PIXIE results as initialization for an optimization method
like [SMPLify-X](https://github.com/vchoutas/smplify-x)
to refine the pose. Note that, the moderator weight could also be utilized
as a confidence measure during optimization.
* **Speed**: The main bottleneck of PIXIE is the need for three separate encoders
for the body, head and hand images. Changing the current
backbone, i.e. Resnet50 or HRNet, to a lighter one, like MobileNet, should
accelerate inference at the cost of performance. We will attempt to provide different
options when we release the **training** code.
* **Texture**: Similar to [DECA](https://github.com/YadiraF/DECA), we rely
upon the Basel face model for our albedo space. Its lack of ethnic
diversity in the albedo causes the model to often compensate for skin tone with lighting.
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