2 citations · 4 across the 15 of their papers we have counts for
7 papers · 1 filter
MatAnyone 2: Scaling Video Matting via a Learned Quality Evaluator
Peiqing Yang, Shangchen Zhou, Kai Hao +1
Video matting remains limited by the scale and realism of existing datasets. While leveraging segmentation data can enhance semantic stability, the lack of effective boundary super…
STream3R: Scalable Sequential 3D Reconstruction with Causal Transformer
Yushi Lan, Yihang Luo, Fangzhou Hong +7
We present STream3R, a novel approach to 3D reconstruction that reformulates pointmap prediction as a decoder-only Transformer problem. Existing state-of-the-art methods for multi-…
Trans-Adapter: A Plug-and-Play Framework for Transparent Image Inpainting
Yuekun Dai, Haitian Li, Shangchen Zhou +1
RGBA images, with the additional alpha channel, are crucial for any application that needs blending, masking, or transparency effects, making them more versatile than standard RGB…
SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training
Jianyi Wang, Shanchuan Lin, Zhijie Lin +10
Recent advances in diffusion-based video restoration (VR) demonstrate significant improvement in visual quality, yet yield a prohibitive computational cost during inference. While…
Precise Object and Effect Removal with Adaptive Target-Aware Attention
Jixin Zhao, Zhouxia Wang, Peiqing Yang +1
Object removal requires eliminating not only the target object but also its associated visual effects such as shadows and reflections. However, diffusion-based inpainting and remov…
Bokeh Diffusion: Defocus Blur Control in Text-to-Image Diffusion Models
Armando Fortes, Tianyi Wei, Shangchen Zhou +1
Recent advances in large-scale text-to-image models have revolutionized creative fields by generating visually captivating outputs from textual prompts; however, while traditional…
MatAnyone: Stable Video Matting with Consistent Memory Propagation
Peiqing Yang, Shangchen Zhou, Jixin Zhao +2
Auxiliary-free human video matting methods, which rely solely on input frames, often struggle with complex or ambiguous backgrounds. To address this, we propose MatAnyone, a robust…