Avatar Animation Using Neural Translation Vectors
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Solution Overview
Problem
Current social media platforms lack the ability to effectively convey non-verbal information such as facial expressions and emotional states, which are crucial for empathetic interactions, as they rely on text, audio, and video but not on real-time emotional mirroring.
Innovation Solution
The use of avatar image animation through translation vectors, generated by an autoencoder based on artificial neural networks, to create animated avatars that mirror and respond to user emotions, providing empathetic or complementary expressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If social media platforms use text, audio, and video for communication, then information transmission is achieved, but non-verbal information such as facial expressions and emotional states cannot be effectively conveyed
Solution Approach 1:
The patent creates animated avatar copies that replicate human facial expressions and emotional states. The system generates synthetic emotive faces that mirror the user's actual facial movements and expressions, allowing non-verbal communication to be transmitted through the avatar representation without requiring direct video transmission of the user's face.
Solution Approach 2:
The system transforms physical facial movement parameters into emotional state parameters. By analyzing facial muscle movements, eye movements, and head pose, the system translates these physical parameters into emotional interpretations (such as happiness, sadness, anger) and applies them to the avatar's animated expressions, enabling emotional information transmission.
2Adaptability or versatility
If traditional social media interfaces are used, then ease of operation is maintained, but empathetic interactions cannot occur due to lack of emotional mirroring
Solution Approach 1:
The animated avatar serves as an intermediary between the user and other social media participants. Instead of directly transmitting raw video or requiring complex emotional analysis interfaces, the system uses the avatar as a mediator that translates the user's emotional states into animated expressions that others can perceive and respond to empathetically.
Solution Approach 2:
The system introduces dynamic emotional mirroring capabilities to the social media interface. The avatar's expressions dynamically change in real-time based on the user's actual facial movements and emotional states, creating a living, responsive representation that adapts to the user's emotional condition rather than using static profile pictures or emojis.
3Measurement precision
If avatar image animation with full emotion metrics is implemented, then emotional expression accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts and applies only the most relevant translation vectors corresponding to the dominant emotional state. Instead of applying all possible emotion metrics simultaneously, the system identifies the primary emotion being expressed and selectively applies the corresponding translation vectors to the avatar, reducing computational overhead while maintaining emotional expression accuracy.
Solution Approach 2:
The system uses a subset of translation vectors rather than the complete set. By applying only the necessary portion of emotion metrics needed to accurately represent the current emotional state, the system achieves sufficient emotional expression accuracy without the computational burden of processing all possible emotional parameters.
Data Source
AI summary
Techniques are described for image generation for avatar image animation using translation vectors. An avatar image is obtained for representation on a first computing device. An autoencoder is trained, on a second computing device comprising an artificial neural network, to generate synthetic emotive faces. A plurality of translation vectors is identified corresponding to a plurality of emotion metrics, based on the training. A bottleneck layer within the autoencoder is used to identify the plurality of translation vectors. A subset of the plurality of translation vectors is applied to the avatar image, wherein the subset represents an emotion metric input. The emotion metric input is obtained from facial analysis of an individual. An animated avatar image is generated for the first computing device, based on the applying, wherein the animated avatar image is reflective of the emotion metric input and the avatar image includes vocalizations.