Hello OpenAI team,
I’d like to share some suggestions to improve anatomical accuracy, particularly for hand generation, with DALL-E. After experimenting with the model, I’ve noticed some recurring challenges with achieving precise details in hands, especially with fingers. Here are some ideas that I believe could enhance DALL-E’s capabilities and offer more customization for users:
1. Specialized Datasets to Improve Anatomical Details
- Specific Training on Hands: A dataset focused on hands, with high-resolution images and diverse poses from various angles, could help refine the generation of fingers and joints. Each image could be annotated with detailed information (number of fingers, joint positioning, angle, skin texture) to help the model better understand and reproduce these elements realistically.
- Multi-Angle and High-Resolution Captures: Providing images of hands from multiple angles (top, side, palm, back) and with a variety of hand types (ages, genders, ethnicities) would enrich the model and help avoid common mistakes, like duplicate fingers.
2. Visual Validation Post-Generation
- Automated Anatomical Error Detection: Integrating a verification tool that analyzes the image post-generation (for example, confirming each hand has five distinct fingers without duplicates or anomalies) could greatly enhance visual accuracy before the final image is presented.
- User Feedback for Iterative Improvement: Allowing users to flag specific anatomical issues in generated images could help refine the model and strengthen its accuracy over time.
3. Training and Customization Options for Users
- User-Level Fine-Tuning Tools for DALL-E: Offering users the ability to fine-tune aspects of DALL-E with their own datasets, or adjust certain parameters, could meet specific needs—for example, for artists or creators with precise anatomical requirements.
- Enhanced Prompt Interpretation and Internal Adjustment: Adding functionality for DALL-E to automatically interpret and adapt prompts based on user experience or previous feedback would facilitate the generation of complex anatomical details without requiring constant prompt reformulation.
4. Documentation and Guides for Anatomical Precision
- Prompt Guide for Complex Details: Providing users with prompt examples to guide them in generating specific elements (like hands) could improve outcomes by explaining best practices for anatomical descriptions.
Thank you for considering these suggestions. DALL-E is an impressive tool, and I believe these adjustments could significantly enhance its ability to generate accurate and anatomically consistent images, especially for professional use.
Best regards, Salvatore