AI-generated visuals, including faces, can sometimes appear distorted due to a few key factors inherent in the technology used to create them:
Training Data Limitations: The AI models used to generate images are trained on vast datasets of existing images. If the training data is limited, biased, or of low quality, this can lead to imperfections in the generated images, including distorted features.
Model Constraints: The specific architecture and capabilities of the AI model also influence the output. Some models may struggle with maintaining proportion and symmetry, especially in areas requiring high detail like facial features.
Interpolation Challenges: AI often needs to interpolate or 'fill in gaps' from its training on what a face should look like under different conditions. This process can sometimes produce unrealistic or physically improbable features if the model's prediction steps stray too far from typical human features.
Regularization Techniques: To prevent overfitting, AI models use regularization techniques that can sometimes prioritize generalization over precision. This can lead to a loss of detail, causing features to blend together or deform, particularly in less commonly represented poses or expressions.
Resolution and Compression: At times, the resolution and compression settings used when generating or storing AI images can affect the quality of the output. Lower resolutions and higher compression can distort images, making details like facial features appear unnatural.
The field of AI-generated imagery is rapidly evolving, and improvements in model architectures, training methods, and data quality are continuously being made to address these issues and produce more accurate and lifelike images.
