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What are the limitations of text-to-image generation when creating photorealistic human figures?



Text-to-image generation, while powerful, faces several limitations when creating photorealistic human figures. One major challenge is accurately generating realistic facial features and expressions. Human faces are incredibly complex, and subtle variations in shape, texture, and lighting can significantly impact perceived realism. AI models often struggle to capture these nuances, leading to faces that look unnatural or uncanny. Generating realistic skin textures is another significant hurdle. Human skin has a complex microstructure and subsurface scattering effects that are difficult to replicate convincingly. AI models may produce skin that looks too smooth, plastic-like, or lacking in detail. Hands are particularly challenging for AI models to generate accurately. They have a complex skeletal structure and a wide range of possible poses, making it difficult for models to consistently produce realistic and anatomically correct hands. AI-generated hands often exhibit distortions, extra fingers, or unnatural poses. Controlling the pose and anatomy of the human figure is also difficult. While prompts can specify desired poses, the model may not always accurately translate these instructions into a realistic and anatomically plausible pose. Ethical considerations arise from the potential misuse of photorealistic human figures, including the creation of deepfakes and the impersonation of real individuals. The datasets used to train these models may contain biases that result in the generation of images that reinforce harmful stereotypes. Finally, fine details such as hair, clothing folds, and wrinkles require significant computational resources and model complexity to generate convincingly. Current models often struggle to produce these details at a level of realism comparable to photographs.