Image Matting
96 papers with code • 8 benchmarks • 8 datasets
Image Matting is the process of accurately estimating the foreground object in images and videos. It is a very important technique in image and video editing applications, particularly in film production for creating visual effects. In case of image segmentation, we segment the image into foreground and background by labeling the pixels. Image segmentation generates a binary image, in which a pixel either belongs to foreground or background. However, Image Matting is different from the image segmentation, wherein some pixels may belong to foreground as well as background, such pixels are called partial or mixed pixels. In order to fully separate the foreground from the background in an image, accurate estimation of the alpha values for partial or mixed pixels is necessary.
Source: Automatic Trimap Generation for Image Matting
Image Source: Real-Time High-Resolution Background Matting
Benchmarks
These leaderboards are used to track progress in Image Matting
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Libraries
Use these libraries to find Image Matting models and implementationsDatasets
Composition-1K
AIM-500
Distinctions-646
P3M-10k
PPM-100
withoutbg100 dataset
Latest papers with no code
Training-Free Neural Matte Extraction for Visual Effects
Alpha matting is widely used in video conferencing as well as in movies, television, and social media sites.
Color-aware Deep Temporal Backdrop Duplex Matting System
In addition, the proposed studio set is actor friendly, and produces high-quality, temporal consistent alpha and color estimations that include a superior color spill compensation.
NEMTO: Neural Environment Matting for Novel View and Relighting Synthesis of Transparent Objects
We propose NEMTO, the first end-to-end neural rendering pipeline to model 3D transparent objects with complex geometry and unknown indices of refraction.
Mask-Guided Matting in the Wild
Mask-guided matting has shown great practicality compared to traditional trimap-based methods.
Treating Pseudo-labels Generation as Image Matting for Weakly Supervised Semantic Segmentation
To solve this problem, we develop a Double Decoupled Class Activation Map (D2CAM) for Mat-Label to generate a high-quality trimap.
Privileged Prior Information Distillation for Image Matting
Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance.
FactorMatte: Redefining Video Matting for Re-Composition Tasks
Based on this observation, we present a method for solving the factor matting problem that produces useful decompositions even for video with complex cross-layer interactions like splashes, shadows, and reflections.
Wider and Higher: Intensive Integration and Global Foreground Perception for Image Matting
This paper reviews recent deep-learning-based matting research and conceives our wider and higher motivation for image matting.
Hierarchical and Progressive Image Matting
In this paper, we propose an end-to-end Hierarchical and Progressive Attention Matting Network (HAttMatting++), which can better predict the opacity of the foreground from single RGB images without additional input.
3D Matting: A Benchmark Study on Soft Segmentation Method for Pulmonary Nodules Applied in Computed Tomography
In this work, we introduce the image matting into the 3D scenes and use the alpha matte, i. e., a soft mask, to describe lesions in a 3D medical image.
