avatar
instancing
instancing's Blog
Instancing
instancing
instancing's Blog
Instancing
  • Instancing.TECH
About Instancing
Login
  • About Instancing
  • Login

Optimizing Video Matting: Curriculum Learning and Motion Blur Augmentation

cover
21 Dec 2025

Table of Links

Abstract and 1. Introduction

  1. Related Works

  2. MaGGIe

    3.1. Efficient Masked Guided Instance Matting

    3.2. Feature-Matte Temporal Consistency

  3. Instance Matting Datasets

    4.1. Image Instance Matting and 4.2. Video Instance Matting

  4. Experiments

    5.1. Pre-training on image data

    5.2. Training on video data

  5. Discussion and References

Supplementary Material

  1. Architecture details

  2. Image matting

    8.1. Dataset generation and preparation

    8.2. Training details

    8.3. Quantitative details

    8.4. More qualitative results on natural images

  3. Video matting

    9.1. Dataset generation

    9.2. Training details

    9.3. Quantitative details

    9.4. More qualitative results

9.2. Training details

Authors:

(1) Chuong Huynh, University of Maryland, College Park ([email protected]);

(2) Seoung Wug Oh, Adobe Research (seoh,[email protected]);

(3) Abhinav Shrivastava, University of Maryland, College Park ([email protected]);

(4) Joon-Young Lee, Adobe Research ([email protected]).


This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.


← Previous

Video Data Synthesis: Categorizing Matting Difficulty by Instance Overlap

Up Next →

MaGGIe vs. Baselines: Quantitative Superiority in Video Instance Matting

avatar
instancing
instancing's Blog
Instancing
instancing
instancing's Blog
Instancing
  • About
  • Stories
  • Random Story
  • Terms
  • Privacy
  • Publish Your Story