  Assessing the artness of AI-generated images continues to be a challenge
within the realm of image generation. Most existing metrics cannot be used to
perform instance-level and reference-free artness evaluation. This paper
presents ArtScore, a metric designed to evaluate the degree to which an image
resembles authentic artworks by artists (or conversely photographs), thereby
offering a novel approach to artness assessment. We first blend pre-trained
models for photo and artwork generation, resulting in a series of mixed models.
Subsequently, we utilize these mixed models to generate images exhibiting
varying degrees of artness with pseudo-annotations. Each photorealistic image
has a corresponding artistic counterpart and a series of interpolated images
that range from realistic to artistic. This dataset is then employed to train a
neural network that learns to estimate quantized artness levels of arbitrary
images. Extensive experiments reveal that the artness levels predicted by
ArtScore align more closely with human artistic evaluation than existing
evaluation metrics, such as Gram loss and ArtFID.
