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Fanghua Yu

7 accepted papers

2026

Enhanced Latent-Space Adversarial Training for Super-Resolution

ICML 2026poster

Real-world super-resolution (SR) is challenging due to complex degradations. HYPIR, a recent state-of-the-art diffusion-based restoration model, struggles to deal with this task in a single step. Although a naive two-step cascade improves the results, over-saturation, limited fine-grained details, a…

Cited by 0SourceScholar
2026

SimpleGVR: A Simple Baseline for Latent-Cascaded Generative Video Super-Resolution

ICLR 2026poster

Cascaded pipelines, which use a base text-to-video (T2V) model for low-resolution content and a video super-resolution (VSR) model for high-resolution details, are a prevailing strategy for efficient video synthesis. However, current works suffer from two key limitations: an inefficient pixel-space…

Cited by 0SourcecodeScholar
2025

UniCon: Unidirectional Information Flow for Effective Control of Large-Scale Diffusion Models

ICLR 2025poster

We introduce UniCon, a novel architecture designed to enhance control and efficiency in training adapters for large-scale diffusion models. Unlike existing methods that rely on bidirectional interaction between the diffusion model and control adapter, UniCon implements a unidirectional flow from the…

Cited by 0SourcePDFScholar
2024

DiffBIR: Toward Blind Image Restoration with Generative Diffusion Prior

ECCV 2024poster

"We present DiffBIR, a general restoration pipeline that could handle different blind image restoration tasks in a unified framework. DiffBIR decouples blind image restoration problem into two stages: 1) degradation removal: removing image-independent content; 2) information regeneration: generating…

2024

Generate Like Experts: Multi-Stage Font Generation by Incorporating Font Transfer Process into Diffusion Models

CVPR 2024poster

Few-shot font generation (FFG) produces stylized font images with a limited number of reference samples which can significantly reduce labor costs in manual font designs. Most existing FFG methods follow the style-content disentanglement paradigm and employ the Generative Adversarial Network (GAN) t…

2024

Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild

CVPR 2024poster

We introduce SUPIR (Scaling-UP Image Restoration) a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up. Leveraging multi-modal techniques and advanced generative prior SUPIR marks a significant advance in intelligent and realistic image restorat…

Cited by 49SourcePDFScholar
2023

OSRT: Omnidirectional Image Super-Resolution With Distortion-Aware Transformer

CVPR 2023poster

Omnidirectional images (ODIs) have obtained lots of research interest for immersive experiences. Although ODIs require extremely high resolution to capture details of the entire scene, the resolutions of most ODIs are insufficient. Previous methods attempt to solve this issue by image super-resoluti…