Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Works


MaGGIe
3.1. Efficient Masked Guided Instance Matting
3.2. Feature-Matte Temporal Consistency


Instance Matting Datasets
4.1. Image Instance Matting and 4.2. Video Instance Matting


Experiments
5.1. Pre-training on image data
5.2. Training on video data


Discussion and References Related Works Related Works Related Works MaGGIe
3.1. Efficient Masked Guided Instance Matting
3.2. Feature-Matte Temporal Consistency MaGGIe 3.1. Efficient Masked Guided Instance Matting 3.1. Efficient Masked Guided Instance Matting 3.2. Feature-Matte Temporal Consistency 3.2. Feature-Matte Temporal Consistency Instance Matting Datasets
4.1. Image Instance Matting and 4.2. Video Instance Matting Instance Matting Datasets Instance Matting Datasets 4.1. Image Instance Matting and 4.2. Video Instance Matting 4.1. Image Instance Matting and 4.2. Video Instance Matting Experiments
5.1. Pre-training on image data
5.2. Training on video data Experiments Experiments 5.1. Pre-training on image data 5.1. Pre-training on image data 5.2. Training on video data 5.2. Training on video data Discussion and References Discussion and References Discussion and References Supplementary Material Supplementary Material Architecture details


Image matting
8.1. Dataset generation and preparation
8.2. Training details
8.3. Quantitative details
8.4. More qualitative results on natural images


Video matting
9.1. Dataset generation
9.2. Training details
9.3. Quantitative details
9.4. More qualitative results Architecture details Architecture details Architecture details Image matting
8.1. Dataset generation and preparation
8.2. Training details
8.3. Quantitative details
8.4. More qualitative results on natural images Image matting 8.1. Dataset generation and preparation 8.1. Dataset generation and preparation 8.2. Training details 8.2. Training details 8.3. Quantitative details 8.3. Quantitative details 8.4. More qualitative results on natural images 8.4. More qualitative results on natural images Video matting
9.1. Dataset generation
9.2. Training details
9.3. Quantitative details
9.4. More qualitative results Video matting 9.1. Dataset generation 9.1. Dataset generation 9.2. Training details 9.2. Training details 9.3. Quantitative details 9.3. Quantitative details 9.4. More qualitative results 9.4. More qualitative results 9.4. More qualitative results For a more immersive and detailed understanding of our model’s performance, we recommend viewing the examples on our website which includes comprehensive results and comparisons with previous methods. Additionally, we have highlighted outputs from specific frames in Fig. 19. Regarding temporal consistency, SparseMat and our framework exhibit comparable results, but our model demonstrates more accurate outcomes. Notably, our output maintains a level of detail on par with InstMatt, while ensuring consistent alpha values across the video, particularly in background and foreground regions. This balance between detail preservation and temporal consistency highlights the advanced capabilities of our model in handling the complexities of video instance matting. For each example, the first-frame human masks are generated by r101 fpn 400e and propagated by XMem for the rest of the video. Authors:
(1) Chuong Huynh, University of Maryland, College Park (chuonghm@cs.umd.edu);
(2) Seoung Wug Oh, Adobe Research (seoh,jolee@adobe.com);
(3) Abhinav Shrivastava, University of Maryland, College Park (abhinav@cs.umd.edu);
(4) Joon-Young Lee, Adobe Research (jolee@adobe.com). Authors: Authors: (1) Chuong Huynh, University of Maryland, College Park (chuonghm@cs.umd.edu); (2) Seoung Wug Oh, Adobe Research (seoh,jolee@adobe.com); (3) Abhinav Shrivastava, University of Maryland, College Park (abhinav@cs.umd.edu); (4) Joon-Young Lee, Adobe Research (jolee@adobe.com). This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Quant

Video Instance Matting: Comparing Temporal Consistency and Detail Preservation

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

3D Mapping Initialization: Using RGB-D Images and Camera Parameters

It Is Okay If You Don't Know What You Like. We Do (feat. Deep Recommendation Algorithms)

10 Machine Learning, Data Science, and Deep Learning Courses for Programmers in 2020

10 Computer Vision Startups on Product Hunt with the Most Upvotes

10 Best Entry Level Machine Learning Tutorials

10 Best + Free Machine Learning Courses Collection

3D Mapping Initialization: Using RGB-D Images and Camera Parameters

It Is Okay If You Don't Know What You Like. We Do (feat. Deep Recommendation Algorithms)

10 Machine Learning, Data Science, and Deep Learning Courses for Programmers in 2020

10 Computer Vision Startups on Product Hunt with the Most Upvotes

10 Best Entry Level Machine Learning Tutorials

10 Best + Free Machine Learning Courses Collection

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps