1 citations · 1 across the 7 of their papers we have counts for
10 papers
TEXTS-Diff: TEXTS-Aware Diffusion Model for Real-World Text Image Super-Resolution
Haodong He, Xin Zhan, Yancheng Bai +3
Real-world text image super-resolution aims to restore overall visual quality and text legibility in images suffering from diverse degradations and text distortions. However, the s…
Real-world Reinforcement Learning from Suboptimal Interventions
Yinuo Zhao, Huiqian Jin, Lechun Jiang +9
Real-world reinforcement learning (RL) offers a promising approach to training precise and dexterous robotic manipulation policies in an online manner, enabling robots to learn fro…
RAGSR: Regional Attention Guided Diffusion for Image Super-Resolution
Haodong He, Yancheng Bai, Rui Lan +4
The rich textual information of large vision-language models (VLMs) combined with the powerful generative prior of pre-trained text-to-image (T2I) diffusion models has achieved imp…
RealisMotion: Decomposed Human Motion Control and Video Generation in the World Space
Jingyun Liang, Jingkai Zhou, Shikai Li +5
Generating human videos with realistic and controllable motions is a challenging task. While existing methods can generate visually compelling videos, they lack separate control ov…
SCALAR: Scale-wise Controllable Visual Autoregressive Learning
Ryan Xu, Dongyang Jin, Yancheng Bai +4
Controllable image synthesis, which enables fine-grained control over generated outputs, has emerged as a key focus in visual generative modeling. However, controllable generation…
UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning
Sule Bai, Mingxing Li, Yong Liu +5
Traditional visual grounding methods primarily focus on single-image scenarios with simple textual references. However, extending these methods to real-world scenarios that involve…