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 Abstract Human matting is a foundation task in image and video processing where human foreground pixels are extracted from the input. Prior works either improve the accuracy by additional guidance or improve the temporal consistency of a single instance across frames. We propose a new framework MaGGIe, Masked Guided Gradual Human Instance Matting, which predicts alpha mattes progressively for each human instances while maintaining the computational cost, precision, and consistency. Our method leverages modern architectures, including transformer attention and sparse convolution, to output all instance mattes simultaneously without exploding memory and latency. Although keeping constant inference costs in the multiple-instance scenario, our framework achieves robust and versatile performance on our proposed synthesized benchmarks. With the higher quality image and video matting benchmarks, the novel multi-instance synthesis approach from publicly available sources is introduced to increase the generalization of models in real-world scenarios. Our code and datasets are available at https://maggie-matt.github.io. Human matting is a foundation task in image and video processing where human foreground pixels are extracted from the input. Prior works either improve the accuracy by additional guidance or improve the temporal consistency of a single instance across frames. We propose a new framework MaGGIe, Masked Guided Gradual Human Instance Matting, which predicts alpha mattes progressively for each human instances while maintaining the computational cost, precision, and consistency. Our method leverages modern architectures, including transformer attention and sparse convolution, to output all instance mattes simultaneously without exploding memory and latency. Although keeping constant inference costs in the multiple-instance scenario, our framework achieves robust and versatile performance on our proposed synthesized benchmarks. With the higher quality image and video matting benchmarks, the novel multi-instance synthesis approach from publicly available sources is introduced to increase the generalization of models in real-world scenarios. Our code and datasets are available at https://maggie-matt.github.io. MaGGIe M G G H I M 1. Introduction In image matting, a trivial solution is to predict the pixel transparency - alpha matte α ∈ [0, 1] for precise background removal. Considering a saliency image I with two main components, foreground F and background B, the image I is expressed as I = αF + (1 − α)B. Because of the ambiguity in detecting the foreground region, for example, whether a person’s belongings are a part of the human foreground or not, many methods [11, 16, 31, 37] leverage additional guidance, typically trimaps, defining foreground, background, and unknown or transition regions. However, creating trimaps, especially for videos, is resourceintensive. Alternative binary masks [39, 56] are simpler to obtain by human drawings or off-the-shelf segmentation models while offering greater flexibility without hardly con- straint output values of regions as trimaps. Our work focuses but is not limited to human matting because of the higher number of available academic datasets and user demand in many applications [1, 2, 12, 15, 44] compared to other objects. When working with video input, the problem of creating trimap guidance is often resolved by guidance propagation [17, 45] where the main idea coming from video object segmentation [8, 38]. However, the performance of trimap propagation degrades when video length grows. The failed trimap predictions, which miss some natures like the alignment between foreground-unknown-background regions, lead to incorrect alpha mattes. We observe that using binary masks for each frame gives more robust results. However, the consistency between the frame’s output is still important for any video matting approach. For example, holes appearing in a random frame because of wrong guidance should be corrected by consecutive frames. Many works [17, 32, 34, 45, 53] constrain the temporal consistency at feature maps between frames. Since the alpha matte values are very sensitive, feature-level aggregation is not an absolute guarantee of the problem. Some methods [21, 50] in video segmentation and matting compute the incoherent regions to update values across frames. We propose a temporal consistency module that works in both feature and output spaces to produce consistent alpha mattes. Besides the temporal consistency, when extending the instance matting to videos containing a large number of frames and instances, the careful network design to prevent the explosion in the computational cost is also a key challenge. In this work, we propose several adjustments to the popular mask-guided progressive refinement architecture [56]. Firstly, by using the mask guidance embedding inspired by AOT [55], the input size reduces to a constant number of channels. Secondly, with the advancement of transformer attention in various vision tasks [40–42], we inherit the query-based instance segmentation [7, 19, 23] to predict instance mattes in one forward pass instead of separated estimation. It also replaces the complex refinement in previous work with the interaction between instances by attention mechanism. To save the high cost of transformer attention, we only perform multi-instance prediction at the coarse level and adapt the progressive refinement at multiple scales [18, 56]. However, using full convolution for the refinement as previous works are inefficient as less than 10% of values are updated at each scale, which is also mentioned in [50]. The replacement of sparse convolution [36] saves the inference cost significantly, keeping the constant complexity of the algorithm since only interested locations are refined. Nevertheless, the lack of information at a larger scale when using sparse convolution can cause a dominance problem, which leads to the higher-scale prediction copying the lower outputs without adding fine-grained details. We propose an instance guidance method to help the coarser prediction guide but not contribute to the finer alpha matte. In addition to the framework design, we propose a new training video dataset and benchmarks for instance-awareness matting. Besides the new large-scale high-quality synthesized image instance matting, an extension of the current instance image matting benchmark adds more robustness with different guidance quality. For video input, our synthesized training and benchmark are constructed from various public instance-agnostic datasets with three levels of difficulty. In summary, our contributions include: • A highly efficient instance matting framework with mask guidance that has all instances interacting and processed in a single forward pass. • A novel approach that considers feature-matte levels to maintain matte temporal consistency in videos. • Diverse training datasets and robust benchmarks for image and video instance matting that bridge the gap between synthesized and natural cases. 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

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MaGGIe: Achieving Temporal Consistency in Video Instance Matting

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