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Working of PifuHD:
Pifu stands for Pixel-aligned implicit function (Pifu) ,it converts 2-D images into 3-D object.
A highly effective implicit representation that locally aligns pixels of 2-D images to 3-D object.
Pifu can produce high-resolution surfaces including largely unseen region such as back of the person.
Commands:
1.One minute video representation is given so that anyone can see the working of the pifuHD on youtube.
2.Five minute video representation is also given so that user can see the working of pifuHD on youtube.
Requirements:
1)Python 3
2)PyTorch
3)json
4)PIL
5)skimage
6)tqdm
7)numpy
8)cv2
Installation of Pytorch3d for Rendering:
• Use the batching features of the pytorch3d API to render the mesh from different viewpoints.
• Vary the rendering settings such as lighting and camera position.
Installation of pytorch3d requirements
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Clone PIFuHD repository:
We have to clone pifu repository into the colab and after cloning the repository into colab we have to create input data in which we can add any custom image to it.
Clone lightweight-human-pose-estimation pytorch:
We have to clone lightweight-human-pose-estimation pytorch for the working of pifuHD.
Checkpoint:
After cloning we have to generate checkpoints for our model image.
Download the Pretrained Model:
A model created by some one else to solve a similar problem. Instead of building a model from scratch to solve a similar problem we can use the model trained on other problem as a starting point.
Run PifuHD:
The final step is to run pifuHD and the result will be downloaded under the folder name results .
Here the result folder will have a png file as well as it’s object file both will be saved under results folder.
We can view our 3-D object in online 3-D Viewer here we have to drag and drop our object file that is saved inside result folder and we get the final result there.
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