7 papers
One-Shot Refiner: Boosting Feed-forward Novel View Synthesis via One-Step Diffusion
Yitong Dong, Qi Zhang, Minchao Jiang +6
We present a novel framework for high-fidelity novel view synthesis (NVS) from sparse images, addressing key limitations in recent feed-forward 3D Gaussian Splatting (3DGS) methods…
Ultra-High-Definition Reference-Based Landmark Image Super-Resolution with Generative Diffusion Prior
Zhenning Shi, Zizheng Yan, Yuhang Yu +6
Reference-based Image Super-Resolution (RefSR) aims to restore a low-resolution (LR) image by utilizing the semantic and texture information from an additional reference high-resol…
GSFixer: Improving 3D Gaussian Splatting with Reference-Guided Video Diffusion Priors
Xingyilang Yin, Qi Zhang, Jiahao Chang +6
Reconstructing 3D scenes using 3D Gaussian Splatting (3DGS) from sparse views is an ill-posed problem due to insufficient information, often resulting in noticeable artifacts. Whil…
BokehDiff: Neural Lens Blur with One-Step Diffusion
Chengxuan Zhu, Qingnan Fan, Qi Zhang +4
We introduce BokehDiff, a novel lens blur rendering method that achieves physically accurate and visually appealing outcomes, with the help of generative diffusion prior. Previous…
Hero-SR: One-Step Diffusion for Super-Resolution with Human Perception Priors
Jiangang Wang, Qingnan Fan, Qi Zhang +4
Owing to the robust priors of diffusion models, recent approaches have shown promise in addressing real-world super-resolution (Real-SR). However, achieving semantic consistency an…
CoMPaSS: Enhancing Spatial Understanding in Text-to-Image Diffusion Models
Gaoyang Zhang, Bingtao Fu, Qingnan Fan +5
Text-to-image (T2I) diffusion models excel at generating photorealistic images but often fail to render accurate spatial relationships. We identify two core issues underlying this…