lrxjason / attention3dhumanpose Goto Github PK

Thank you for your amazing work! But...
I meet some problem when I tried to run the code of real-time human pose reconstruction.
In line45,46 of run.py (if args.causal: \n from common.causal_model import *), the code had import causal_model, but it do not exist in the common folder. Did you miss it when you packing the code?
Looking forward to your early reply.

谢谢您出色的工作！但是…
我在试图运行实时人体姿态重建代码时遇到了一些问题。
在run.py的第45、第46行 (if args.causal: \n from common.causal_model import *)，引入了causal_model，但它并不在common文件夹中。请问是在打包代码的时候遗漏了吗？
期待您的早日回复。

Could you please tell me how to run your code with custom video

Hi! Very nice work and thanks for sharing the codes. I'll be really appreciate if you could share the weights of the pretrained 2D cpn model to produce the 2D h36m format keypoints for inference in the wild. Thanks!!

Hey! Thank you for an amazing work. I am looking forward to the release of your code.
In your paper you report the results of training your model on CPN detections for HumanEva. Would it be possible to share the CPN 2D detections file (similarly to the CPN detections for Human3.6M which you are already sharing)?
Additionally, you reference the results for HumanEva from Pavllo et al. [35] (VideoPose3D). For Walk S3 you report 27.2, when in their paper they mention that "The high error on “Walk” of S3 is due to corrupted mocap data" and so they get 46.6. How did you fix this issue?
Thank you

In this paper, the data enhancement is to flip the action and add it to the test set. In my understanding, this method of data expansion will lead to poor test results, why it is better here. Is it because of the high accuracy after flipping.

Thanks for sharing your great works! But I am still confused about generating visual effect of my custom video. I have done all the modification needed to generate a video, but finally I find that your code in this project may assume that the 2d joint positions are in Human3.6 style; while the instructions from VideoPose3D will only generate custom detection result in COCO style. Is there any possible solution for this circumstance? Or this code is designed only for Human3.6m data as described in README.

Thanks in advance for anyone who is willing to answer me.

As the figure 1 of the paper mentioned Link, the demo is a retargeting cartoon image rather a human pose leading to better display.
Could you tell me how to generate it?

Hi~ Thank you for sharing such great works. I have a question about the test time of real-time inference.
Can you provide me with the details of the test time of real-time inference?
It would be great if you could provide the test code.

Best wishes

thank you

I am interested in your model framework. The animation model in your demo system is Kung Fu Panda, I would like to know how to animate the skeleton, can you share the tools or code? Thank you very much!

Videopose3d use UnChunkedGenerator during validating and testing while Attention3DHumanPose use ChunkedGenerator. With ChunckedGenerator, sequences would be cropped into some chunks with the same chunk length and padding will be added if the chunk has not enough frames. Does it make your test dataset containing some "fake data pairs"?

Thank you for your great work.

I tried -arc 3x3x3 or 3x3x3x3 to test 27 or 81 receptive field, but the script gives error It seem the code only support 3x3x3x3x3.
May I know what I should modify when I test other receptive field besides Ac 3x3x3 or 3x3x3x3 to test 27 or 81 receptive field, but the script gives error
May I know what should I modify when I test other receptive field besides "-arc"?

Thank you in advance.

Thank you for your amazing work!
I do not know how to animate avatar, could you share the tools or code about it.