5 papers
GGT-100K: Generative Ground Truth for Generalizable Real-World Image Restoration
Xiangtao Kong, Jixin Zhao, Lingchen Sun +2
Real-world image restoration (IR) is bottlenecked by the scarcity of high-quality paired training data. Synthetic datasets are abundant but often fail to model real-world degradati…
FlashClear: Ultra-Fast Image Content Removal via Efficient Step Distillation and Feature Caching
Yixin Tang, Jiawei Guo, Junxian Li +6
Recently, diffusion-based object removal models have achieved impressive results in eliminating objects and their associated visual effects. However, they indiscriminately denoise…
VOSR: A Vision-Only Generative Model for Image Super-Resolution
Rongyuan Wu, Lingchen Sun, Zhengqiang Zhang +4
Most of the recent generative image super-resolution (SR) methods rely on adapting large text-to-image (T2I) diffusion models pretrained on web-scale text-image data. While effecti…
Precise Object and Effect Removal with Adaptive Target-Aware Attention
Jixin Zhao, Zhouxia Wang, Peiqing Yang +1
Object removal requires eliminating not only the target object but also its associated visual effects such as shadows and reflections. However, diffusion-based inpainting and remov…
MatAnyone: Stable Video Matting with Consistent Memory Propagation
Peiqing Yang, Shangchen Zhou, Jixin Zhao +2
Auxiliary-free human video matting methods, which rely solely on input frames, often struggle with complex or ambiguous backgrounds. To address this, we propose MatAnyone, a robust…