AI image generators are biased towards faces and objects, but lack sufficient examples of text and small objects like hands, leading to poor text and face quality.
The training data for image generators lacks diversity in text styles, fonts, and languages, making it difficult for AI to generate high-quality text.
AI image generators treat text within an image as just another part of the image, rather than understanding it as text, leading to poorly rendered text.
The complexity of facial features, such as the shape of the nose, eyes, and lips, makes it challenging for AI to generate realistic faces.
The 3D geometry of faces and text is difficult for AI to conceptualize, resulting in distorted or blurry text and faces.
AI image generators struggle to understand the nuances of human skin tones, leading to unrealistic or biased skin tones in generated faces.
The lack of high-quality, diverse, and well-annotated training data is a significant limitation for AI image generators.
AI image generators are not designed to understand the meaning or context of text, making it difficult for them to generate coherent text.
The algorithms used in AI image generators are optimized for generating visually appealing images, rather than accurately rendering text or faces.
The capacity for AI image generators to generate high-quality text and faces is limited by their architecture and training objectives.
The task of generating realistic faces and text is inherently more challenging than generating objects or scenes, due to the complexity and variability of human faces and text.
AI image generators often rely on shortcuts, such as copying and pasting text or facial features from the training data, rather than generating new ones.
The evaluation metrics used to train AI image generators, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), do not accurately capture the quality of text and faces.
AI image generators can generate faces that are more realistic than actual human faces, but this can also lead to difficulties in distinguishing between real and generated images.
The lack of media literacy and critical thinking skills among consumers of AI-generated images can lead to the proliferation of poorly rendered text and faces.
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