Persistent Identity Preservation in Generative Image Models: A Benchmark and Evaluation System
Generative image models can now produce high-quality images, follow complex instructions, and support precise edits, but they still struggle to preserve who or what is being depicted. When generating or editing images of a specific subject, identity may drift as the pose, expression, appearance, viewpoint, or surrounding scene changes. Existing subject-driven methods make fundamentally different choices about where identity is represented: through the input context (GPT-Image-2, NB2), as trainable subject-specific model parameters (LoRA), or as a persistent identity layer (PHOTA IDENTITY) reusable across generations and edits. We systematically benchmark these paradigms across subject-dri...
| Authors | Mengwei Ren, Xuaner Zhang, Zhihao Xia |
|---|---|
| Primary category | cs.CV |
| Published | 3 September 2026 |
| Last updated at source | 10 September 2026 |
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