My GAN generates faces.
But many look distorted or unnatural.
Eyes and mouths appear in wrong positions.
The training seems stable, but outputs are flawed.
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My GAN generates faces.
But many look distorted or unnatural.
Eyes and mouths appear in wrong positions.
The training seems stable, but outputs are flawed.
This happens when the model fails to learn correct spatial relationships between facial features. If the training data or architecture is weak, the generator learns textures without structure.
High-resolution faces require strong inductive biases such as convolutional layers, attention, or progressive growing to maintain geometry.
Better architectures and higher-quality aligned training data significantly improve realism.
Common mistakes: Low-resolution training, Poor alignment, Weak generator
The practical takeaway is that realism requires learning both texture and structure.