SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Training stability. We have observed that training 3D GANs can be rather unstable. Some experiments are highly sensitive to hyper-parameters such as the  $\gamma$  value of R1 regularization [34], learning rate, etc. However, our study does not investigate this aspect in depth. Future research addressing hyper-parameter sensitivity and training stability may lead to significant reductions in training costs and more compelling results.
Universality. We trained all our 3D-aware image synthesis models on simple, object-level datasets, such as FFHQ [26] for face generation, and our conclusions are based on these datasets. However, our paper does not explore the extension of 3D GANs to a higher degree of universality, which represents a promising research direction for the future. This universality pertains to generating diverse objects (e.g., ImageNet [11] or Microsoft CoCo [31]), dynamic objects or scenes, and large-scale scenes.