4 citations · 4 across the 5 of their papers we have counts for
6 papers
VideoPhysEdit: Physical Counterfactual Video Editing via Rigid-Body Physical Scene Reconstruction
Conghan Yue, Yuanjie Chen, Yue Han +4
Video editing has advanced substantially in recent years, with methods increasingly accounting for the visual consequences of edits, such as changes to shadows and occlusions. Howe…
DEIG: Detail-Enhanced Instance Generation with Fine-Grained Semantic Control
Shiyan Du, Conghan Yue, Xinyu Cheng +1
Multi-Instance Generation has advanced significantly in spatial placement and attribute binding. However, existing approaches still face challenges in fine-grained semantic underst…
Exploring Diffusion with Test-Time Training on Efficient Image Restoration
Rongchang Lu, Tianduo Luo, Yunzhi Jiang +4
Image restoration faces challenges including ineffective feature fusion, computational bottlenecks and inefficient diffusion processes. To address these, we propose DiffRWKVIR, a n…
Improving Fine-Grained Control via Aggregation of Multiple Diffusion Models
Conghan Yue, Zhengwei Peng, Shiyan Du +4
While many diffusion models perform well when controlling particular aspects such as style, character, and interaction, they struggle with fine-grained control due to dataset limit…
Enhanced Control for Diffusion Bridge in Image Restoration
Conghan Yue, Zhengwei Peng, Junlong Ma +1
Image restoration refers to the process of restoring a damaged low-quality image back to its corresponding high-quality image. Typically, we use convolutional neural networks to di…
Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge
Conghan Yue, Zhengwei Peng, Junlong Ma +3
Diffusion models exhibit powerful generative capabilities enabling noise mapping to data via reverse stochastic differential equations. However, in image restoration, the focus is…