Optimizing Video Matting: Curriculum Learning and Motion Blur Augmentation
Table of Links
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
\ 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
9.2. Training details
\
:::info 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).
:::
:::info This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.
:::
\
You May Also Like
Dogecoin (DOGE) Breaks Below Key Levels With Possible Upside Toward $0.18
‘Love Island Games’ Season 2 Release Schedule—When Do New Episodes Come Out?