RefGC-SR$^2$: Reference-guided Generated Content Super-Resolution and Refinement

2026-06-13 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

RefGC-SR^2 introduces a novel task: reference-guided generated content super-resolution-refinement, addressing critical limitations in existing reference-guided generation pipelines. Current methods downsample high-resolution reference images (HRRI) to low-resolution (LR) before processing, leading to detail loss and introducing generative artifacts like identity distortion. While reference-guided content refinement (RefGCR) corrects some artifacts in LR, and reference-guided super-resolution (RefSR) recovers resolution for natural images, neither fully tackles the combined problem. RefGC-SR^2 reuses the original HRRI during post-processing to simultaneously recover lost details, refine generative artifacts, and upscale the output. The authors developed the first real-world triplet data generation pipeline for this task, employing a diptych-conditioned generator to synthesize paired low-quality anchors. They also present a frequency-aware diffusion transformer model specifically designed for RefGC-SR^2, which selectively injects fine details from the HRRI while removing artifacts. Experiments confirm RefGC-SR^2's success in faithfully refining object identity and recovering high-resolution details, yielding significantly higher quality and more usable results than RefGCR and RefSR baselines.

Key takeaway

For Computer Vision Engineers developing reference-guided generation systems, you should integrate RefGC-SR^2's approach to preserve fine details and mitigate generative artifacts. By reusing the original high-resolution reference image in a post-processing stage, your outputs will achieve significantly higher quality and practical usability compared to traditional RefGCR or RefSR methods. Consider adopting a frequency-aware diffusion transformer for superior detail recovery and artifact refinement.

Key insights

Reusing high-resolution references post-generation can simultaneously recover details, refine artifacts, and upscale generated content.

Principles

• Downsampling HRRI before generation discards fine details.
• Generative pipelines introduce specific artifact distributions.
• Post-processing with original HRRI improves fidelity and resolution.

Method

Construct a real-world triplet data generation pipeline using a diptych-conditioned generator to synthesize low-quality anchors, then train a frequency-aware diffusion transformer model.

In practice

• Apply HRRI post-processing to improve generated image quality.
• Develop custom data generation pipelines for novel refinement tasks.

Topics

• Reference-guided Generation
• Super-Resolution
• Image Refinement
• Generative Artifacts
• Diffusion Transformer
• Computer Vision

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.