Neural Network Visual Dubbing for Film Localization
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Solution Overview
Problem
The integration of advanced visual dubbing technologies into the filmmaking pipeline is hindered by the serial nature of tasks performed by post-production and localization houses, leading to inefficiencies and the need for secondary language versions to be returned to post-production houses, which is undesirable, especially when creating multiple language versions.
Innovation Solution
A computer-implemented method using neural networks to generate and edit animated representations of objects within video data, allowing for interactive and iterative editing of object geometry, with a two-stage neural network approach for low-resolution interactive editing and high-resolution finalization, enabling seamless compositing and reducing the need for returning visually dubbed material to post-production houses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If visual dubbing is performed at a localization house using conventional pipelines, then foreign language versions can be produced, but the material must be returned to post-production house for integration, causing time loss and operational inefficiency
Solution Approach 1:
The patent merges the visual dubbing process with the existing post-production pipeline by enabling the localization house to perform visual dubbing and deliver ready-to-integrate output directly to the distribution house, eliminating the need to return material to the post-production house. This integration of functions across previously separate stages resolves the time loss and operational inefficiency.
Solution Approach 2:
The patent uses neural networks to generate synthetic video data that replicates the visual appearance of actors performing dialogue in the target language, creating a virtual copy of the performance. This digital copying allows the localization house to produce multilingual versions without physical reshoots or material return, maintaining quality while eliminating the return-trip time loss.
2Adaptability or versatility
If conventional visual dubbing is used, then mouth shapes can be modified, but the quality and nuance of the original film is lost
Solution Approach 1:
The patent replaces conventional mechanical video editing and compositing methods with neural network-based synthetic generation. The neural networks analyze the original performance and generate new video frames that preserve the actor's original expressions, gestures, and nuances while changing the mouth shapes to match target language dialogue, thereby maintaining manufacturing precision while enabling language adaptability.
Solution Approach 2:
The patent changes the parameters of the video data by using neural networks to generate new pixel-level information that maintains the original visual characteristics while adapting the mouth movements to different languages. This parameter transformation at the neural network level preserves quality and nuance while achieving the adaptability required for multilingual versions.
3Ease of operation
If a two-stage neural network approach is used with low-resolution interactive editing, then real-time editing is enabled, but final resolution may be limited without high-resolution processing
Solution Approach 1:
The patent segments the neural network processing into two stages: a first neural network that performs low-resolution interactive editing to enable real-time user feedback and control, and a second neural network that processes the output at high resolution for final quality generation. This segmentation allows the system to achieve both ease of interactive operation and high manufacturing precision by assigning different resolution requirements to different processing stages.
Data Source
AI summary
Obtaining input video data depicting footage of an object, obtaining current values of a set of adjustable parameters of an object representation model comprising a neural network and arranged to generate animated representations of the object using the neural network in which a geometry of the object is controllable by the set of adjustable parameters. For a plurality of iterations, using the object representation model to generate a video layer comprising an animated representation of the object in which the geometry of the object corresponds to the current values of the set of adjustable parameters, presenting composite video data comprising the video layer overlaid on the object in the input video data, and updating the current values of the set of adjustable parameters in response to user input via the user interface.