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MaGGIe balances temporal consistency and detail preservation, outperforming SparseMat in accuracy and matching InstMatt's high-fidelity outputMaGGIe balances temporal consistency and detail preservation, outperforming SparseMat in accuracy and matching InstMatt's high-fidelity output

Video Instance Matting: Comparing Temporal Consistency and Detail Preservation

Author: Hackernoon

Source: Hackernoon

2025/12/23 10:59

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

9.4. 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.

\ Table 15. Our framework also reduces the errors of trimap propagation baselines. When replacing those models’ matte decoders with ours, the number in all error metrics was reduced by a large margin. Gray rows denote the module from public weights without retraining on our V-HIM2K5 dataset.

:::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.

:::

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