Why Generating Two Recognisable People in One AI Image Remains Unsolved

Creating a convincing AI-generated image of two specific people together remains a significant technical challenge, even as single-subject generation has become largely reliable. When two reference identities are fed into the same generation, models lack an inherent mechanism to keep them distinct, often blending facial features or transferring attributes between subjects. Separate generation and compositing preserves individual likenesses but produces visibly fake results due to mismatched lighting, colour temperature, and perspective. Techniques such as spatial conditioning, per-region identity embeddings, and dedicated handling of contact poses offer partial solutions, but each addresses only one dimension of the problem. A developer working on a tool called DuoPortrait notes that while specific fixes evolve quickly, the core failure modes — identity bleed, anatomical errors at contact points, and lighting inconsistency — have proven stubbornly persistent.