Virtual Human Image Generation from Multimodal 1D Inputs
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Solution Overview
Problem
Conventional conditional generative deep-learning models require 2D image data to generate virtual humans, limiting user control and flexibility in creating lifelike facial expressions and movements.
Innovation Solution
A novel encoder-decoder architecture that combines 1D vector inputs, such as head pose and audio data, with 2D image data to generate virtual humans, allowing for enhanced control over facial expressions and movements through a fusion of separate latent spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional conditional generative deep-learning models require 2D image data to generate virtual humans, then the input requirement is simple and standardized, but user control and flexibility in creating lifelike facial expressions and movements are limited
Solution Approach 1:
The patent transforms 1D vector inputs (audio features, head pose, blend shape coefficients) into a 2D spatial arrangement through encoding and reshaping operations. This dimensionality transformation enables the integration of multiple data types (audio, pose, expression) that would otherwise be difficult to combine, thereby enhancing user control and flexibility in generating virtual human facial expressions and movements.
2Adaptability or versatility
If multiple 1D vectors of different modalities are concatenated and processed, then user control over facial expressions and movements is enhanced, but the complexity of the encoder-decoder architecture increases
Solution Approach 1:
The patent segments the input data into distinct 1D vectors representing different modalities (audio features, head pose information, blend shape coefficients). Each vector is processed separately through encoding operations before being concatenated and reshaped into a 2D spatial arrangement. This segmentation approach manages complexity by handling each data type independently while maintaining the ability to integrate them synergistically.
Solution Approach 2:
The patent introduces an intermediary encoding and reshaping process that transforms 1D vectors into a 2D spatial arrangement. This intermediary transformation layer acts as a bridge between the diverse 1D input modalities and the 2D image generation process, facilitating their integration while managing architectural complexity through a standardized transformation pipeline.
3Adaptability or versatility
If 1D vector inputs are used instead of 2D image data, then diverse data modalities can be integrated, but the challenge of transforming 1D data into meaningful 2D image output increases
Solution Approach 1:
The patent applies dimensionality change by transforming 1D vector inputs into a 2D spatial arrangement through encoding and reshaping operations. This transformation enables the decoder to generate meaningful 2D image output from diverse 1D modalities, overcoming the challenge of converting non-spatial data into spatial image representations.
Data Source
AI summary
Image-to-image translations using 1D inputs includes concatenating multiple 1D vectors forming a concatenated 1D vector. The multiplicity of 1D vectors includes 1D vectors of at least two different modalities. An encoded 1D vector is generated by encoding the concatenated 1D vector. An encoded 2D array of features is generated by reshaping an arrangement of features of the encoded 1D feature vector. An image of a virtual human is generated by decoding the encoded 2D array.