MatAnyone: Stable Video Matting with Consistent Memory Propagation (2501.14677v2)

Abstract: Auxiliary-free human video matting methods, which rely solely on input frames, often struggle with complex or ambiguous backgrounds. To address this, we propose MatAnyone, a robust framework tailored for target-assigned video matting. Specifically, building on a memory-based paradigm, we introduce a consistent memory propagation module via region-adaptive memory fusion, which adaptively integrates memory from the previous frame. This ensures semantic stability in core regions while preserving fine-grained details along object boundaries. For robust training, we present a larger, high-quality, and diverse dataset for video matting. Additionally, we incorporate a novel training strategy that efficiently leverages large-scale segmentation data, boosting matting stability. With this new network design, dataset, and training strategy, MatAnyone delivers robust and accurate video matting results in diverse real-world scenarios, outperforming existing methods.

• The paper introduces a novel memory-based framework that improves temporal consistency by aggregating features across video frames.
• It employs a Consistent Memory Propagation module with region-adaptive fusion to stabilize target objects and preserve fine boundary details.
• The training strategy uses a new high-quality dataset and segmentation data to boost robustness and outperform existing methods.

"MatAnyone: Stable Video Matting with Consistent Memory Propagation" (2501.14677) presents a framework for target-assigned video matting designed to overcome the limitations commonly observed in auxiliary-free methods, particularly instability and inaccuracies when dealing with complex or ambiguous backgrounds. The core contribution lies in enhancing temporal consistency and detail preservation through a novel memory propagation mechanism.

Problem Context and Motivation

Auxiliary-free video matting approaches, which operate solely on input RGB frames without explicit trimaps or background captures, frequently suffer from temporal inconsistencies and flickering artifacts in the predicted alpha mattes. These methods often struggle to maintain stable segmentation of the target object, especially in regions with low contrast, motion blur, or complex background textures that semantically resemble the foreground. Existing techniques may fail to effectively leverage temporal information, leading to jittery boundaries or unstable alpha values within the object's core regions across frames. MatAnyone aims to address these shortcomings by explicitly incorporating and refining temporal context through a memory-based architecture.

MatAnyone Framework Architecture

The MatAnyone framework is fundamentally built upon a memory-based paradigm. This approach leverages information aggregated from previous frames to inform the matting prediction for the current frame. The general architecture likely involves:

1. Feature Extraction: A backbone network (e.g., CNN or Transformer) processes the current input frame $I_t$ and potentially a reference frame or initial mask defining the target object to extract high-level spatial features $F_t$.
2. Memory Module: A mechanism stores relevant features or intermediate representations from past frames (e.g., $F_{t-1}$, $M_{t-1}$ where $M$ represents memory).
3. Consistent Memory Propagation (CMP): The core innovation, detailed below, which updates and propagates the memory $M_{t-1}$ to generate context-aware memory $M'_t$ for the current frame.
4. Matting Head: A prediction head takes the current features $F_t$ and the propagated memory $M'_t$ to estimate the alpha matte $\alpha_t$ and potentially the foreground colors $FG_t$.

The memory component is crucial for maintaining temporal coherence by providing historical context about the object's appearance and position.

Consistent Memory Propagation Module

The paper introduces the Consistent Memory Propagation (CMP) module as the key component for ensuring stability. CMP utilizes Region-Adaptive Memory Fusion (RAMF) to integrate memory from the previous frame ($M_{t-1}$) with current frame information. The "region-adaptive" nature implies that the fusion process is not uniform across the spatial domain but rather adapts based on local characteristics.

The RAMF mechanism aims to:

• Ensure Semantic Stability: In core regions of the target object, where appearance is generally consistent, memory features from the previous frame should be strongly propagated to prevent fluctuations in the predicted alpha.
• Preserve Fine-Grained Details: Along object boundaries or in areas undergoing significant change (e.g., appearance variation, complex motion), the fusion should adaptively incorporate more information from the current frame while still leveraging past context to refine details like hair strands or semi-transparent regions.

While the exact formulation of RAMF is not detailed in the abstract, it likely involves spatially varying gating or attention mechanisms that weigh the contribution of past memory ($M_{t-1}$) versus current features ($F_t$) based on estimated motion, feature similarity, or predicted region type (e.g., core vs. boundary). This adaptive fusion ensures that stable regions benefit from strong temporal priors, while dynamic regions are updated accurately based on current observations, balancing stability and detail fidelity.

Training Methodology and Dataset

MatAnyone incorporates two significant enhancements to the training process:

1. New High-Quality Dataset: The authors curated a large-scale, high-quality, and diverse video matting dataset. The increased scale and diversity compared to previous datasets are intended to improve the model's generalization capabilities and robustness across various real-world scenarios, including different object types, backgrounds, and lighting conditions.
2. Novel Training Strategy: A key aspect is a training strategy that leverages large-scale segmentation data. This suggests a multi-task learning or pre-training/fine-tuning approach where the model learns robust object localization and feature representation from abundant segmentation annotations before or during the matting training. By incorporating segmentation data, the model can potentially learn more stable object representations, which aids the matting task, especially in ambiguous regions, thereby boosting overall matting stability. This hybrid training approach efficiently utilizes readily available segmentation datasets to improve performance on the more challenging matting task, which typically suffers from smaller specialized datasets.

The training objective likely combines standard alpha prediction losses (e.g., L1 loss, compositional loss) with potential auxiliary losses derived from the segmentation data or losses designed to enforce temporal consistency explicitly.

Performance and Practical Implications

The paper claims that the combination of the proposed network design (CMP with RAMF), the new dataset, and the novel training strategy enables MatAnyone to achieve robust and accurate video matting results. It reportedly outperforms existing state-of-the-art methods in diverse real-world scenarios.

Practical Implementation Considerations:

• Computational Cost: Memory-based architectures inherently increase computational and memory requirements compared to frame-by-frame methods, as past features or states need to be stored and processed. The complexity of the RAMF module will influence inference latency.
• Memory Management: Effective implementation requires careful management of the memory buffer, deciding the scope (e.g., number of past frames) and representation of the stored information.
• Initialization: The performance might depend on the quality of the initial frame's prediction or the reference target information provided.
• Error Propagation: Errors in memory from earlier frames could potentially propagate, although the RAMF is designed to mitigate this by adaptively incorporating current information.
• Deployment: The increased resource requirements might necessitate more powerful hardware for real-time applications compared to simpler matting approaches.

In practice, MatAnyone offers a promising direction for applications requiring high-quality, temporally stable video matting without auxiliary inputs, such as video editing, special effects compositing, and virtual conferencing backgrounds. The use of segmentation data during training is a practical strategy to enhance robustness by leveraging larger, more accessible datasets.

Conclusion

MatAnyone introduces a memory-based video matting framework emphasizing temporal stability through its Consistent Memory Propagation module and Region-Adaptive Memory Fusion. By adaptively integrating past information, it aims to stabilize core object regions while preserving boundary details. Complemented by a new large-scale dataset and a training strategy utilizing segmentation data, the method demonstrates improved performance and robustness compared to existing auxiliary-free techniques, presenting a valuable advancement for practical video matting applications.