RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image
  Generation
Authors: name: Aviv Slobodkin, name: Hagai Taitelbaum, name: Yonatan Bitton, name: Brian Gordon, name: Michal Sokolik, name: Nitzan Bitton Guetta, name: Almog Gueta, name: Royi Rassin, name: Itay Laish, name: Dani Lischinski, name: Idan Szpektor

Subject-driven text-to-image (T2I) generation aims to produce images that
align with a given textual description, while preserving the visual identity
from a referenced subject image. Despite its broad downstream applicability --
ranging from enhanced personalization in image generation to consistent
character representation in video rendering -- progress in this field is
limited by the lack of reliable automatic evaluation. Existing methods either
assess only one aspect of the task (i.e., textual alignment or subject
preservation), misalign with human judgments, or rely on costly API-based
evaluation. To address this, we introduce RefVNLI, a cost-effective metric that
evaluates both textual alignment and subject preservation in a single
prediction. Trained on a large-scale dataset derived from video-reasoning
benchmarks and image perturbations, RefVNLI outperforms or matches existing
baselines across multiple benchmarks and subject categories (e.g.,
Animal, Object), achieving up to 6.4-point gains in textual
alignment and 8.5-point gains in subject consistency. It also excels with
lesser-known concepts, aligning with human preferences at over 87\% accuracy.