Pareto-Enhanced Portrait Generation: Vision-Aligned Text Supervision for Alignment, Realism, and Aesthetics
arXiv:2605.20640v1 Announce Type: cross Abstract: Text-to-image diffusion models often face a severe trilemma in human portrait generation: text-image alignment, photorealism, and human-perceived aesthetics inherently inhibit one another. Supervised Fine-Tuning (SFT) is an effective method for enhancing the photorealism of image generation. However, it often leads to overfitting to the training dataset, corrupts pre-trained image priors, and degrades alignment or aesthetics. To break this bottleneck, we propose a feature supervision paradigm for Multimodal Diffusion Transformers (MM-DiT). Spec
The paper addresses a known trilemma in text-to-image diffusion models for portrait generation, which is a significant area of current AI research and development.
Improving the alignment, photorealism, and aesthetics of AI-generated human portraits has broad implications for media, virtual content creation, and potentially digital identity.
The proposed feature supervision paradigm and MM-DiT model suggest a method to overcome current limitations in high-quality portrait generation without sacrificing other key attributes.
- · AI content creators
- · Metaverse developers
- · Digital advertising
- · Diffusion model researchers
- · Traditional stock photography
- · Low-quality generative AI tools
Further advancements in generative AI models, specifically for human imagery, accelerate the creation of realistic digital humans.
The improved quality of AI-generated portraits could democratize access to high-fidelity imagery for a wider range of users and applications.
The proliferation of highly realistic synthetic human images could exacerbate challenges in distinguishing real from fake content, leading to new verification and authentication requirements.
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Read at arXiv cs.AI