Uncertainty-Guided Frame Interpolation for Artifact-Prone Animation
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Solution Overview
Problem
Conventional deep learning video frame interpolation methods fail to generalize their interpolation results to animated data due to complex lighting effects and large motion, leading to severe artifacts, and retraining for each specific use case is not viable.
Innovation Solution
A deep learning-based uncertainty-guided video frame interpolation method that incorporates known regions of intermediate frames, estimates expected error, and utilizes a training procedure to handle uncertainties, allowing for improved interpolation quality through a partial rendering pass.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional deep learning video frame interpolation methods are used, then processing speed is maintained, but interpolation quality deteriorates due to severe artifacts in animated data with complex lighting effects and large motion
Solution Approach 1:
The patent applies preliminary action by performing a first rendering pass to generate an intermediate frame before the final interpolation step. This preliminary intermediate frame serves as guidance for the second rendering pass, allowing the system to pre-identify regions that need refinement and thereby reduce artifacts in the final interpolated frame while maintaining processing efficiency
Solution Approach 2:
The patent segments the frame interpolation process into two distinct rendering passes. The first pass generates a preliminary intermediate frame, and the second pass refines specific regions based on error maps. This segmentation allows different processing strategies to be applied to different regions, improving overall interpolation quality without uniformly increasing computational cost
2Manufacturing precision
If a full rendering pass is performed to ensure high interpolation quality, then manufacturing precision improves, but processing time increases
Solution Approach 1:
The patent applies partial action by performing a second rendering pass only on specific regions identified as needing refinement through error map analysis, rather than re-rendering the entire frame. This selective partial rendering maintains high interpolation quality in critical regions while significantly reducing the computational time compared to a full rendering pass
Solution Approach 2:
The first rendering pass acts as a preliminary step that generates an intermediate frame and error maps, which guide the subsequent second rendering pass. This preliminary action enables the system to identify exactly where refinement is needed, avoiding unnecessary computation in regions that already have sufficient quality
3Adaptability or versatility
If conventional methods are used without uncertainty guidance, then device complexity is reduced, but adaptability deteriorates across different video content types
Solution Approach 1:
The patent achieves universality by training a single neural network model to perform multiple functions: generating interpolated frames, producing error maps, and guiding partial rendering passes. This multi-functional model adapts to different video content types (live-action, animation, etc.) without requiring separate specialized models, thereby improving generalization capability while controlling complexity through a unified architecture
Data Source
AI summary
A system includes a hardware processor, a memory storing software code, and a machine learning (ML) model-based video frame interpolator. The hardware processor executes the software code to provide first and second frames of a video sequence including a plurality of frames, respective binary masks for the first and second frames, and optionally an intermediate frame of the video sequence between the first and second frames and a binary mask for the intermediate frame, as interpolation inputs to the ML model-based video frame interpolator. The hardware processor further executes the software code to generate, using the ML model-based video frame interpolator and the interpolation inputs, an interpolated frame and an error map for the interpolated frame, wherein generating the interpolated frame and the error map includes a cross-backward warping of respective latent feature representations of each of the plurality of frames.