Sign Language Avatar Generation for Immersive Video Captions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video content systems lack the ability to automatically generate and present sign language avatars that correspond to dialogue, failing to provide an immersive experience for users who prefer sign language-based captions over text-based alternatives.
Innovation Solution
Implementing neural networks and machine learning algorithms to analyze video and audio content, determine sign language gestures and facial expressions, and generate digital avatars that perform sign language translations, enhancing user experience by providing immersive sign language-based captions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If text-based captions are used for video content, then accessibility is provided, but user experience is limited for users who prefer sign language
Solution Approach 1:
The patent creates digital avatar copies that perform sign language gestures based on the video content, allowing users to access sign language captions without requiring manual production of separate sign language videos. The avatar replicates the communication function of human signers through automated gesture generation.
Solution Approach 2:
The system transforms text-based caption data into visual sign language gestures by changing the representation parameters from textual to spatial-temporal movements. The avatar's hand positions, orientations, and motion trajectories are dynamically adjusted to convey linguistic information through sign language parameters.
2Productivity
If automated neural networks are used to generate sign language avatars, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent divides the complex avatar generation task into separate neural network modules: one network analyzes video content to extract gesture information, another generates the avatar animations, and additional networks handle facial expressions and body language. This segmentation allows each module to be optimized independently while maintaining overall system productivity.
Solution Approach 2:
The system introduces intermediate data representations between the video input and final avatar output, including extracted gesture sequences, facial expression parameters, and body pose data. These intermediaries simplify the transformation process by breaking down the complex mapping from video to avatar into manageable stages that can be processed by specialized neural networks.
3Ease of operation
If detailed facial expressions and gestures are included in sign language avatars, then immersion is improved, but file size increases
Solution Approach 1:
The patent implements selective detail rendering where the avatar includes detailed facial expressions and hand gestures only when linguistically relevant, while simplifying less important body movements. This partial action approach maintains immersion by focusing computational resources on the most expressive and communication-critical elements rather than uniformly detailing all movements.
Solution Approach 2:
The system dynamically adjusts the level of detail in avatar rendering based on the contextual importance of different gestures and expressions. When sign language gestures carry critical linguistic information, the avatar renders high-fidelity hand and facial details; during transitional or less important moments, the detail level is reduced to optimize file size while preserving overall immersion.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for systems and methods for automated generation and presentation of sign language avatars for video content. Example methods may include determining, by one or more computer processors coupled to memory, a first segment of video content, the first segment including a first set of frames, first audio content, and first subtitle data, where the first subtitle data comprises a first word and a second word. Methods may include determining, using a first machine learning model, a first sign gesture associated with the first word, determining first motion data associated with the first sign gesture, and determining first facial expression data. Methods may include generating an avatar configured to perform the first sign gesture using the first motion data, where a facial expression of the avatar while performing the first sign gesture is based on the first facial expression data.