BodyMetric: Evaluating the Realism of Human Bodies in Text-to-Image Generation

TL;DR

BodyMetric is a learnable, scalable metric for evaluating the realism of human bodies in text-to-image generation, trained on a new dataset and leveraging 3D body priors to improve artifact detection.

Contribution

We introduce BodyMetric, a novel learnable metric for assessing human body realism in generated images, supported by a new annotated dataset and 3D prior integration.

Findings

BodyMetric accurately predicts body realism artifacts.

It outperforms general preference metrics in body-specific evaluation.

It enables large-scale benchmarking and ranking of text-to-image models.

Abstract

Accurately generating images of human bodies from text remains a challenging problem for state of the art text-to-image models. Commonly observed body-related artifacts include extra or missing limbs, unrealistic poses, blurred body parts, etc. Currently, evaluation of such artifacts relies heavily on time-consuming human judgments, limiting the ability to benchmark models at scale. We address this by proposing BodyMetric, a learnable metric that predicts body realism in images. BodyMetric is trained on realism labels and multi-modal signals including 3D body representations inferred from the input image, and textual descriptions. In order to facilitate this approach, we design an annotation pipeline to collect expert ratings on human body realism leading to a new dataset for this task, namely, BodyRealism. Ablation studies support our architectural choices for BodyMetric and the importance of leveraging a 3D human body prior in capturing body-related artifacts in 2D images. In comparison to concurrent metrics which evaluate general user preference in images, BodyMetric specifically reflects body-related artifacts. We demonstrate the utility of BodyMetric through applications that were previously infeasible at scale. In particular, we use BodyMetric to benchmark the generation ability of text-to-image models to produce realistic human bodies. We also demonstrate the effectiveness of BodyMetric in ranking generated images based on the predicted realism scores.