Generative Pre-training for Subjective Tasks: A Diffusion Transformer-Based Framework for Facial Beauty Prediction
This work addresses the problem of subjective aesthetic assessment in computer vision for applications like beauty analysis, though it is incremental as it adapts existing generative models to a specific domain.
The paper tackles the challenge of Facial Beauty Prediction (FBP) by proposing a two-stage framework that uses generative pre-training with a Diffusion Transformer to learn domain-specific features, achieving a Pearson Correlation Coefficient of 0.932 on the FBP5500 benchmark, significantly outperforming prior methods.
Facial Beauty Prediction (FBP) is a challenging computer vision task due to its subjective nature and the subtle, holistic features that influence human perception. Prevailing methods, often based on deep convolutional networks or standard Vision Transformers pre-trained on generic object classification (e.g., ImageNet), struggle to learn feature representations that are truly aligned with high-level aesthetic assessment. In this paper, we propose a novel two-stage framework that leverages the power of generative models to create a superior, domain-specific feature extractor. In the first stage, we pre-train a Diffusion Transformer on a large-scale, unlabeled facial dataset (FFHQ) through a self-supervised denoising task. This process forces the model to learn the fundamental data distribution of human faces, capturing nuanced details and structural priors essential for aesthetic evaluation. In the second stage, the pre-trained and frozen encoder of our Diffusion Transformer is used as a backbone feature extractor, with only a lightweight regression head being fine-tuned on the target FBP dataset (FBP5500). Our method, termed Diff-FBP, sets a new state-of-the-art on the FBP5500 benchmark, achieving a Pearson Correlation Coefficient (PCC) of 0.932, significantly outperforming prior art based on general-purpose pre-training. Extensive ablation studies validate that our generative pre-training strategy is the key contributor to this performance leap, creating feature representations that are more semantically potent for subjective visual tasks.