Stellar: Systematic Evaluation of Human-Centric Personalized
  Text-to-Image Methods
Panos Achlioptas Alexandros Benetatos Iordanis Fostiropoulos Dimitris Skourtis
In this work, we systematically study the problem of personalized
text-to-image generation, where the output image is expected to portray
information about specific human subjects. E.g., generating images of oneself
appearing at imaginative places, interacting with various items, or engaging in
fictional activities. To this end, we focus on text-to-image systems that input
a single image of an individual to ground the generation process along with
text describing the desired visual context. Our first contribution is to fill
the literature gap by curating high-quality, appropriate data for this task.
Namely, we introduce a standardized dataset (Stellar) that contains
personalized prompts coupled with images of individuals that is an order of
magnitude larger than existing relevant datasets and where rich semantic
ground-truth annotations are readily available. Having established Stellar to
promote cross-systems fine-grained comparisons further, we introduce a rigorous
ensemble of specialized metrics that highlight and disentangle fundamental
properties such systems should obey. Besides being intuitive, our new metrics
correlate significantly more strongly with human judgment than currently used
metrics on this task. Last but not least, drawing inspiration from the recent
works of ELITE and SDXL, we derive a simple yet efficient, personalized
text-to-image baseline that does not require test-time fine-tuning for each
subject and which sets quantitatively and in human trials a new SoTA. For more
information, please visit our project's website:
https://stellar-gen-ai.github.io/.