To do this, My3DGen employs Generative Adversarial Networks (GANs). GANs have two components: a generator and a discriminator. Unlike prior 3D human face model work, My3DGen discards the discriminator component and focuses only on fine-tuning the generator. “Technically, if you fine-tune or personalize these two components together with limited data, it leads to overfitting and mode collapse,” he explains. “The generator is not able to have the generative power anymore. It’s overfitted to, let's say, 10 images or even one image. What we do is discard the discriminator and then fine-tune the generator using the selfies themselves. In this way, we’re trying to maintain both the generative power of the generator and make it personalized. Then the GAN will be able to learn all the knowledge you want it to learn.” While many research papers focus on theoretical advancements, this 12 DAILY WACV Sunday Oral Presentation
RkJQdWJsaXNoZXIy NTc3NzU=