The training code about dfnrmvs HOT 5 CLOSED

The training code is quite messy thus not easy to clean and use. I can provide loss functions and training parameters shortly. The rest is just normal training pipeline.

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@zqbai-jeremy
Thanks, wish for the loss functions and training parameters

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@ForrestPi
The loss functions and training parameters have been uploaded. Please let me know if you have any further questions. Thank you for your interest in our work.

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Is there any code or more details for training data processing?

The StirlingESRS_3Dface dataset contains 74 females and 64 different male subjects. Each subject with 8 expressions. As you describe in the paper 'Then, we render one image for each expression with different poses and same global illumination using Spherical Harmonics (SH).', the amount of training sample should be (74+64)*C(8,2) = 3864 samples. This number does not conform to 8K as you mentioned 'As a result, around 8K training samples are generated.'
Is there any misunderstanding?

Thanks for any reply~!

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@moranli19
For each identity, we generate 100 samples with randomly sampled 2 expressions, 2 poses, and 1 illumination. Then we detect landmarks on rendered images and check whether they are consistent with landmarks on 3D scans (since the landmark detector and the NICP registration may have errors). If not, the sample is discard. As a result, we finally have around 8K samples.
Please let me if you have any further questions. Thank you!

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