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[NeurIPS 23] Official repository for NeurIPS 2023 paper "Global-correlated 3D-decoupling Transformer for Clothed Avatar Reconstruction"

Home Page: https://river-zhang.github.io/GTA-projectpage/

Python 98.14% Shell 0.53% GLSL 1.34%
3d clothed-humans clothed-people-digitalization digital human reconstruction vision neurips-2023 python pytorch

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gta's Issues

Ask for training code

Excellent work! I would like to ask if the training code is included in the repository?

inference time

Hi, thanks for your great work.
I read your paper, but didn't see any mention of inference time for a single image.
Do you have a rough idea of what it would be on a modern GPU?

thanks!

The test results seems to be inconsistent with the paper

I tried your code in Ubuntu20.04 cuda 11.8 pytorch2.0.1
I have got the results:

GTA.mp4

but the corresponding results in your paper is much better obviously, as shown in the following:
GTA

I want to know if my result is reasonable。

Expecting a demo

Hi, River-Zhang
I'm studying papers on human body reconstruction, and I have read your paper. It is a very nice work! May I ask when will you update the open source? Expecting your demo~

THuman 2.0 evaluation protocol

Hi authors, I have a question regarding the THuman 2.0 evaluation protocol in your Table 1.

  • How do you create train/test split?
  • For the test set, how many views do you render per subject, and what is the FOV?

Thank you in advance!

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