Better results about smplitex HOT 5 CLOSED

Hi, there are a few things
(1) To get rid of the black pixels, we compute a tight silhouette and apply some shrinking before projecting the pixels to the UV. This is to make sure no background pixels are projected from the image to the partial texturemap. I will upload a script soon to do this.
(2) For inpainting, I usually get better results with the A1111 approach, not the diffusers. I will also upload some script to run A1111 from Python, but you could play a little with the web interface meanwhile.
(3) After inpaiting, I usually do an extra img2img step with very low noise factor. This basically fixes a lot of artifacts, but produces a texture that is slightly different than the original subject. If the noise parameter is very low, usually the identity is well kept.

I will try to upload a script that does (1), (2), and (3) exactly how I do in the results from the paper.

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Hello @StGorazd I've updated the repository with the scripts to create the partial texturemap from image, and the inpainting with Automatic1111.

https://github.com/dancasas/SMPLitex?tab=readme-ov-file#smpl-texture-estimation-from-single-image

Please, check it out and let me know. Thanks!

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Hi @dancasas ,
thank you for notifying me. I am currently trying to run it, but I ran into some problems. I followed the provided tutorial; however, for some reason, I am getting a different dense pose, which subsequently causes issues while generating the partial texture. I attempted to fix it myself but have not been successful so far. Do you have any advice or suggestions?

Here is the debug output:

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@StGorazd you are right! I just committed a fix for this, there was an issue with the DensePose IUV ids. Now it should be fixed, could you update and re-run it?

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Thank you for your prompt response. I have re-run it, and the results are significantly improved.

Tomorrow, I will try using inpaint part. I will let you know if I encounter any problems.
Thank you for your excellent work.

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