7 papers
IRPO: Boosting Image Restoration via Post-training GRPO
Haoxuan Xu, Yi Liu, Tianfu Li +7
Post-training has become effective for high-level generation, but its role in low-level vision remains underexplored. Existing image restoration methods often rely on fixed pixel-w…
CareCom: Generative Image Composition with Calibrated Reference Features
Jiaxuan Chen, Bo Zhang, Qingdong He +2
Image composition aims to seamlessly insert foreground object into background. Despite the huge progress in generative image composition, the existing methods are still struggling…
TokenAR: Multiple Subject Generation via Autoregressive Token-level enhancement
Haiyue Sun, Qingdong He, Jinlong Peng +5
Autoregressive Model (AR) has shown remarkable success in conditional image generation. However, these approaches for multiple reference generation struggle with decoupling differe…
UniCombine: Unified Multi-Conditional Combination with Diffusion Transformer
Haoxuan Wang, Jinlong Peng, Qingdong He +9
With the rapid development of diffusion models in image generation, the demand for more powerful and flexible controllable frameworks is increasing. Although existing methods can g…
PixelPonder: Dynamic Patch Adaptation for Enhanced Multi-Conditional Text-to-Image Generation
Yanjie Pan, Qingdong He, Zhengkai Jiang +9
Recent advances in diffusion-based text-to-image generation have demonstrated promising results through visual condition control. However, existing ControlNet-like methods struggle…
Mamba-YOLO-World: Marrying YOLO-World with Mamba for Open-Vocabulary Detection
Haoxuan Wang, Qingdong He, Jinlong Peng +3
Open-vocabulary detection (OVD) aims to detect objects beyond a predefined set of categories. As a pioneering model incorporating the YOLO series into OVD, YOLO-World is well-suite…
VI3DRM:Towards meticulous 3D Reconstruction from Sparse Views via Photo-Realistic Novel View Synthesis
Hao Chen, Jiafu Wu, Ying Jin +7
Recently, methods like Zero-1-2-3 have focused on single-view based 3D reconstruction and have achieved remarkable success. However, their predictions for unseen areas heavily rely…