11 papers
T2LDM++: A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation
Wentao Qu, Qi Zhang, Chenxu Wang +5
Recent progress in Text-to-Image generation benefits from large-scale Text-Image pairs. However, the scarcity of Text-LiDAR pairs often causes over-smoothed scenes and limited cont…
SAMA: Factorized Semantic Anchoring and Motion Alignment for Instruction-Guided Video Editing
Xinyao Zhang, Wenkai Dong, Yuxin Song +10
Current instruction-guided video editing models struggle to simultaneously balance precise semantic modifications with faithful motion preservation. While existing approaches rely…
OnlinePG: Online Open-Vocabulary Panoptic Mapping with 3D Gaussian Splatting
Hongjia Zhai, Qi Zhang, Xiaokun Pan +5
Open-vocabulary scene understanding with online panoptic mapping is essential for embodied applications to perceive and interact with environments. However, existing methods are pr…
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…
TinySR: Pruning Diffusion for Real-World Image Super-Resolution
Linwei Dong, Qingnan Fan, Yuhang Yu +4
Real-world image super-resolution (Real-ISR) focuses on recovering high-quality images from low-resolution inputs that suffer from complex degradations like noise, blur, and compre…
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…
TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution
Linwei Dong, Qingnan Fan, Yihong Guo +5
Pre-trained text-to-image diffusion models are increasingly applied to real-world image super-resolution (Real-ISR) task. Given the iterative refinement nature of diffusion models,…