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Queries in regards to shape parameters and results #69
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Hi
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Thank you so much for your reply! Just some followups... 1.) Just to confirm, meshnet takes the concatenated 2D and 3D pose as input and sends it to a spectral graph convolution and it gives the 3D mesh as output? 2.) In regards to training, you have used groundtruth mesh generated using SMPLify-X by fitting SMPL parameters, right? And these parameters were generated using the SMPL parameters json file for COCO (mentioned in the readme file), right? 3.) Also, is there any particular reason for you not using the shape parameters? I was wondering because I am thinking that will help to generalise the shape of the mesh better. Let me know when you find the time. Thank you! |
Hey! Thank you for your work! I have few queries in regards to your work.
1.) If I understood correctly, your algorithm takes an input image, finds 2D pose using PoseNet and then sends the 2D pose to MeshNet which estimates the 3D mesh of the person. And from your paper, I understood that you wont use any shape parameters, rather MeshNet will automatically learn to fit the shape based on the 2D pose. Am I correct?
2.) I actually have the shape and pose (17+2 joints) of the person. Do you know of any approach which I can use to find the mesh with these 17+2 joints and shape parameters? Most of the algorithm that I looked into use more than 20 joint positions, so I am not able to use their models.
3.) When I tried Pose2Mesh, the shape tends to be really bad considering my inputs are low res, and have been induced with motion blur. I have attached an image from Pose2Mesh demo below:
![Screenshot from 2023-06-19 13-43-37](https://private-user-images.githubusercontent.com/65438757/246898119-84fd3d85-5c04-4e88-927a-b7a863026610.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.1I0oSpyWK6x3_xY3CUxXqXeEIWCfuQYMJy6y0QILzHA)
I am guessing the model is not able to fit in for 2D poses like this. Do you think giving the image along with the 2D pose as input help solve these problems with MeshNet and help generalizing better?
Let me know when you find the time. Thank you!
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