MaGGIe: Masked Guided Gradual Human Instance Matting (2404.16035v1)

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.

• The paper introduces an efficient one-pass instance matting framework that progressively refines human masks using transformer attention and sparse convolution.
• It ensures temporal consistency in videos through a bidirectional Conv-GRU and fusion mechanisms to harmonize predictions across frames.
• The study also presents new synthesized datasets and benchmarks, demonstrating robust performance and generalization for instance-aware human matting.

MaGGIe Framework: Enhanced Approach for Instance-Aware Human Matting in Images and Videos

Overview of the MaGGIe Framework

MaGGIe (Masked Guided Gradual Human Instance Matting) is an innovative framework designed to address the challenges of instance-aware human matting, where multiple human figures and details are separated and extracted from background imagery in both single images and video sequences. The method leverages a guided progressive approach, incorporating transformer attention and sparse convolution techniques. It aims to efficiently predict instance alpha mattes in a single forward pass, maintaining high precision and computational effectiveness through tailored architectural adjustments and the use of modern deep learning tools.

Core Contributions

1. Efficient Instance Matting: MaGGIe proposes a highly optimized pipeline where individual instances are processed and refined within one cohesive network pass.
2. Temporal Consistency in Videos: The framework ensures that the alpha matting is consistent across video frames through a novel temporal consistency module, addressing challenges often associated with video matting tasks.
3. Rich Dataset and Benchmark Creation: Beyond existing benchmarks, MaGGIe introduces robust image and video matting datasets specifically synthesized to test the breadth of instance-aware matting challenges.

Methodological Details

Instance Matte Prediction

• Initial Guidance Mapping: Instance masks are transformed into an embedding, reducing input channel complexity.
• Coarse Matte Prediction: Utilizing scalable dot-product attention, coarse instance mattes are derived from downscaled feature maps, incorporating spatial and instance-specific details.
• Progressive Refinement: A progressive refinement strategy is employed, focusing on uncertain regions through sparse convolution, enhancing mattes' granularity while conservatively using computational resources.

Temporal Consistency Enhancement

• Feature and Output-level Temporal Strategies: Both feature maps and output alpha mattes are temporally adjusted using bidirectional Conv-GRU and predicted variance among consecutive frames.
• Temporal Fusion: Outputs are harmonized using a fusion mechanism that combines predictions from subsequent frames, mitigating artifacts caused by inconsistent instance information across frames.

Experimental Validation

Rich experimental insights underline the practical and theoretical relevance of the MaGGIe framework. The system was trained and validated against the newly proposed benchmarks, revealing its strengths in handling multiple instances efficiently without computational overhead typically introduced by separate instance processing.

• Synthetic and Natural Datasets: Tested on synthesized as well as natural datasets, showing substantial robustness and generalization capabilities.
• Efficient Training and Processing: Achieved competitive matting precision with notably lower inference times and resource usage compared to existing methods.
• Temporal Consistency: Demonstrated superior performance in maintaining temporal coherence within video sequences, crucial for dynamic content processing.

Future Prospects and Implications

The approach sets a new standard for handling complex instance-aware matting scenarios in both images and videos. It opens avenues for various practical applications, particularly in media production, virtual reality, and video conferencing backgrounds. Future work may explore extending these techniques toward fully unsupervised learning regimes and enhancing model generalization across diverse, unseen real-world scenarios.

Conclusion

The MaGGIe framework offers a refined solution to instance-aware matting challenges, enhancing processing efficiency, accuracy, and temporal consistency. Its comprehensive testing through newly developed benchmarks demonstrates robustness and broad applicability, potentially serving as a new benchmark for future developments in the field of image and video matting.