9 citations · 12 across the 8 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…
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…
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…
MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results
Xin Jin, Chunle Guo, Xiaoming Li +39
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algor…
MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results
Yuekun Dai, Dafeng Zhang, Xiaoming Li +60
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algor…
Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution
Shangchen Zhou, Peiqing Yang, Jianyi Wang +2
Text-based diffusion models have exhibited remarkable success in generation and editing, showing great promise for enhancing visual content with their generative prior. However, ap…
PGDiff: Guiding Diffusion Models for Versatile Face Restoration via Partial Guidance
Peiqing Yang, Shangchen Zhou, Qingyi Tao +1
Exploiting pre-trained diffusion models for restoration has recently become a favored alternative to the traditional task-specific training approach. Previous works have achieved n…