GAN Avatar Generation From Images Without High-End Graphics Hardware
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Solution Overview
Problem
Existing methods for generating three-dimensional avatars using Computer Graphics (CG) result in single and personalized avatars, requiring significant time and manpower, and high-performance graphics hardware, making it difficult to achieve diversification and efficient generation.
Innovation Solution
A method and apparatus using a first generator trained on a first and second sample image set, including real faces and three-dimensional models, to generate avatars based on user input, utilizing Generative Adversarial Networks (GAN) for efficient and diversified avatar creation, reducing hardware performance requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If Computer Graphics (CG) rendering is used to generate three-dimensional avatars, then visual effects such as character fidelity and light and shadow complexity are improved, but hardware performance requirements become extremely high and rendering time increases significantly
Solution Approach 1:
The patent uses a pre-trained generator model (copying the rendering capability) to generate avatars directly from input images, bypassing the need for real-time CG rendering. The generator learns the mapping from real images to avatar images during training, then reproduces this transformation during inference, eliminating the need for complex graphics hardware.
Solution Approach 2:
The patent replaces the mechanical CG rendering system with a neural network-based generative model. Instead of using traditional rendering pipelines that require high-performance graphics cards, the system uses a trained generator that performs avatar generation through neural network inference, which can be executed on standard hardware.
2Adaptability or versatility
If Computer Graphics (CG) modeling is modified to create various avatars, then avatar diversity and personalization are improved, but production time increases and manpower costs increase
Solution Approach 1:
The system enables users to generate their own personalized avatars by simply providing an input image. The pre-trained generator automatically processes the input and generates the corresponding avatar without requiring manual intervention from professional modelers, making the service self-serve and highly scalable.
Solution Approach 2:
The generator model is pre-trained on a large dataset of real images and corresponding avatar images before deployment. This preliminary training action captures the mapping between real appearances and avatar styles, so that during actual use, the model can quickly generate diverse avatars without requiring time-consuming manual modeling adjustments.
3Manufacturing precision
If traditional CG avatar generation is used, then high-quality visual effects are achieved, but it is difficult to deploy on hardware devices with limited performance such as cellphones
Solution Approach 1:
The patent replaces expensive, high-performance graphics hardware with a lightweight neural network model that can run on mobile devices. The generator model, once trained, can be deployed as a standalone application on cellphones and other devices with limited computational resources, making avatar generation accessible on common consumer hardware.
Data Source
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AI summary
Embodiments of the present disclosure relates to an avatar method, apparatus and device, and a medium. The method comprises: acquiring a target image in response to a user input; and obtaining an avatar corresponding to the target image by using a first generator; the first generator being trained and obtained on the basis of a first sample image set and a second sample image set generated by a three-dimensional model. In the present disclosure, by using the first generator, the generation method for an avatar is effectively simplified, thus improving generation efficiency, and avatars in one-to-one correspondence to target image can be generated, so that the avatars are more diversified. Additionally, the first generator is easy to deploy in various production environments, thus reducing the performance requirements on hardware devices.