Single-Pass Instance Matting With Temporal Guidance for Multi-Object Video
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Solution Overview
Problem
Conventional object matting systems are inflexible, inefficient, and inaccurate, particularly when dealing with multiple objects in digital images and videos, often requiring multiple passes and failing to provide temporal consistency.
Innovation Solution
An instance matting system using a neural network that incorporates transformer attention, sparse convolutions, and an instance guidance module to generate refined mattes for multiple objects in a single pass, with temporal aggregation at both feature and matte levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional object matting systems process multiple objects, then they require multiple passes through the system, but this increases processing time and reduces efficiency
Solution Approach 1:
The patent combines multiple object matting operations into a single unified neural network pass. The system processes multiple instances simultaneously by integrating instance segmentation and matting generation in one forward pass, eliminating the need for sequential multiple-pass processing of conventional systems.
Solution Approach 2:
The neural network is designed with multi-functionality to handle both instance segmentation and matting generation within a single architecture. This universal system can process multiple objects of different types and scales simultaneously, replacing specialized single-purpose systems that required separate processing passes.
2Measurement precision
If the system generates refined mattes for multiple instances, then accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the processing into distinct neural network components: an instance segmentation branch and a matting generation branch. This segmentation allows each component to specialize in its function while working together, achieving high accuracy without requiring the entire system to be overly complex.
Solution Approach 2:
The system introduces instance embeddings as an additional dimensional layer that captures instance-specific information. This dimensional addition allows the network to distinguish and process multiple instances effectively, improving accuracy while maintaining manageable computational complexity through structured feature representation.
3Reliability
If conventional systems process digital videos with multiple objects, then they fail to provide temporal consistency, but achieving consistency requires additional processing passes
Solution Approach 1:
The patent implements continuity by integrating temporal aggregation directly into the neural network's forward pass. The system continuously aggregates features across video frames within the same processing operation, ensuring temporal consistency is maintained without requiring separate post-processing passes or iterative refinements.
4Measurement precision
If the neural network processes high-resolution images for accurate mattes, then quality improves, but computational resources increase significantly
Solution Approach 1:
The system dynamically adjusts feature processing based on instance characteristics. The neural network adaptively allocates computational resources to different instances and regions, focusing detailed processing only where necessary for accurate matte generation while using coarser processing for simpler regions, thereby maintaining quality while reducing overall computational resource consumption.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate mattes for objects portrayed in digital images and/or digital videos. For example, in some embodiments, the disclosed systems receive a digital image portraying one or more objects. The disclosed systems generate, via an instance matting neural network and using the digital image and a guidance mask for each object from the one or more objects, a coarse matte prediction for each object. The disclosed systems further generate, using an instance guidance model of the instance matting neural network, a refined matte prediction for each object from the coarse matte prediction for each object. The disclosed systems provide, for display, a modified digital image generated from the refined matte prediction for each object.