6 papers indexed
Mingyang Chen, Zhilu Zhang, Honglei Xu, Renlong Wu, Xiaohe Wu, Wangmeng Zuo
High-quality, large-scale paired data is essential for training learning-based image deblurring models. However, synthetic blurry images generally lack realism, while real-world captured images require complex and inflexible camera systems. In this work, we propose GS-RealBlur, a…
Shihao Yuan, Yuanze Li, Ruyi Zhang, Ming Liu, Wangmeng Zuo
Despite the advancements of Large Multimodal Models (LMMs) in RGB vision, their ability to generalize to unseen visual modalities remains a largely unexplored challenge. We argue that different visual modalities are merely distinct samplings of the same physical world. Therefore,…
Xiaoyu Liu, Huan Wang, Fan Li, Zhixin Wang, Jiaqi Xu, Ming Liu, et al.
Controllable image generation methods, such as ControlNet, have demonstrated a remarkable capacity to introduce visual conditions(e.g., depth maps) to guide image generation. However, these methods often struggle with complex multi-instance scenes, frequently leading to attribute…
Bo Wei, Xianhui Lin, Yi Dong, Zhongzhong Li, Zonghui Li, Zirui Wang, et al.
Makeup transfer applies a reference cosmetic style to a source face while preserving its identity and geometry. However, this task is severely hindered by the lack of real paired training data. Current methods rely on either weak priors or synthetic pseudo-targets from large-scal…
Chonghuinan Wang, Zhikai Chen, Chunwei Wang, Yecong Wan, Junwei Yang, Zhixin Wang, et al.
The advancement of generative AI models capable of producing text and image marks a critical step forward in the realm of multimodal intelligence, particularly for tasks involving the interleaving of both modalities. To advance this intelligence to the next stage, it is crucial f…
Haoran Bai, Xiaoxu Chen, Xiaoyu Liu, Zongsheng Yue, Sibin Deng, Wangmeng Zuo, et al.
While diffusion models excel in video restoration, their reliance on extensive iterative steps limits efficiency. Conversely, aggressive single-step distillation often compromises fine texture recovery. To achieve an optimal balance, we present SATB-VR, a few-step paradigm that j…