A Controllable and Personalized UV Map Generative Model for 3D Avatar Creation

Core Concepts

A novel method, UVMap-ID, that enables controllable and personalized UV map generation for 3D avatar creation by fine-tuning a pre-trained text-to-image diffusion model with a face fusion module.

Abstract

The paper introduces UVMap-ID, a method for generating personalized and controllable UV texture maps for 3D avatars. The key highlights are: UVMap-ID fine-tunes a pre-trained text-to-image diffusion model (Stable Diffusion) with a face fusion module to enable ID-driven personalized texture generation. This allows generating diverse textures that preserve the identity of a given reference face image. The authors create a small-scale attribute-balanced dataset of around 750 high-quality UV texture maps with annotated text prompts and face IDs to support the fine-tuning strategy. Novel evaluation metrics are proposed to assess the quality of generated textures, including Inception Score, Semantic Structure Preservation, Deep Face Recognition, and CLIP-Text alignment. Extensive experiments demonstrate the effectiveness of UVMap-ID in generating high-quality, personalized, and controllable UV textures, outperforming the baseline method SMPLitex. The authors also release a new dataset, CelebA-HQ-UV, containing 5k UV map-ID pairs. The method enables applications in 3D avatar creation, where personalized textures can be generated based on user-provided face images and controlled via text prompts.

Stats

"Our dataset comprises around 750 image-ID pairs: the textures map with annotated text prompts, the corresponding portrait faces." "We utilize our method with personalized generation to produce a new dataset, CelebA-HQ-UV, which contains 5k UVMap-ID pairs."

Quotes

"Our method can synthesize high-quality textures while enabling a controllable and personalized generation with the given text prompts and Face ID." "Remarkably, our model achieves high-quality and diverse texture synthesis within just several hours of training, while also supporting controllable and personalized synthesis with the user-provided image ID."

Key Insights Distilled From

UVMap-ID: A Controllable and Personalized UV Map Generative Model

by Weijie Wang,... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14568.pdf

Deeper Inquiries

How can the proposed method be extended to handle more diverse facial attributes beyond race and gender, such as age, expression, or accessories?

The proposed method can be extended to handle more diverse facial attributes by incorporating additional features into the training process. To include attributes like age, expression, or accessories, the model can be trained on a more extensive dataset that includes a wide range of facial variations. This dataset should encompass images with diverse age groups, various expressions, and different types of accessories worn by individuals. By exposing the model to a more comprehensive set of facial attributes during training, it can learn to generate personalized textures that reflect these characteristics accurately. Furthermore, the model architecture can be modified to include specific modules or branches dedicated to different facial attributes. For example, separate branches can be designed to focus on age estimation, expression recognition, or accessory detection. These branches can work in parallel with the existing framework, allowing the model to capture and incorporate a broader spectrum of facial features into the texture generation process. By enhancing the training data and adapting the model architecture to accommodate a wider range of facial attributes, the proposed method can be extended to handle diverse characteristics beyond race and gender, enabling more detailed and personalized texture generation for a variety of facial attributes.

How can the proposed method be extended to handle more diverse facial attributes beyond race and gender, such as age, expression, or accessories?

The proposed method can be extended to handle more diverse facial attributes by incorporating additional features into the training process. To include attributes like age, expression, or accessories, the model can be trained on a more extensive dataset that includes a wide range of facial variations. This dataset should encompass images with diverse age groups, various expressions, and different types of accessories worn by individuals. By exposing the model to a more comprehensive set of facial attributes during training, it can learn to generate personalized textures that reflect these characteristics accurately. Furthermore, the model architecture can be modified to include specific modules or branches dedicated to different facial attributes. For example, separate branches can be designed to focus on age estimation, expression recognition, or accessory detection. These branches can work in parallel with the existing framework, allowing the model to capture and incorporate a broader spectrum of facial features into the texture generation process. By enhancing the training data and adapting the model architecture to accommodate a wider range of facial attributes, the proposed method can be extended to handle diverse characteristics beyond race and gender, enabling more detailed and personalized texture generation for a variety of facial attributes.

How can the proposed method be extended to handle more diverse facial attributes beyond race and gender, such as age, expression, or accessories?

The proposed method can be extended to handle more diverse facial attributes by incorporating additional features into the training process. To include attributes like age, expression, or accessories, the model can be trained on a more extensive dataset that includes a wide range of facial variations. This dataset should encompass images with diverse age groups, various expressions, and different types of accessories worn by individuals. By exposing the model to a more comprehensive set of facial attributes during training, it can learn to generate personalized textures that reflect these characteristics accurately. Furthermore, the model architecture can be modified to include specific modules or branches dedicated to different facial attributes. For example, separate branches can be designed to focus on age estimation, expression recognition, or accessory detection. These branches can work in parallel with the existing framework, allowing the model to capture and incorporate a broader spectrum of facial features into the texture generation process. By enhancing the training data and adapting the model architecture to accommodate a wider range of facial attributes, the proposed method can be extended to handle diverse characteristics beyond race and gender, enabling more detailed and personalized texture generation for a variety of facial attributes.

How can the proposed method be extended to handle more diverse facial attributes beyond race and gender, such as age, expression, or accessories?

The proposed method can be extended to handle more diverse facial attributes by incorporating additional features into the training process. To include attributes like age, expression, or accessories, the model can be trained on a more extensive dataset that includes a wide range of facial variations. This dataset should encompass images with diverse age groups, various expressions, and different types of accessories worn by individuals. By exposing the model to a more comprehensive set of facial attributes during training, it can learn to generate personalized textures that reflect these characteristics accurately. Furthermore, the model architecture can be modified to include specific modules or branches dedicated to different facial attributes. For example, separate branches can be designed to focus on age estimation, expression recognition, or accessory detection. These branches can work in parallel with the existing framework, allowing the model to capture and incorporate a broader spectrum of facial features into the texture generation process. By enhancing the training data and adapting the model architecture to accommodate a wider range of facial attributes, the proposed method can be extended to handle diverse characteristics beyond race and gender, enabling more detailed and personalized texture generation for a variety of facial attributes.

How can the proposed method be extended to handle more diverse facial attributes beyond race and gender, such as age, expression, or accessories?

The proposed method can be extended to handle more diverse facial attributes by incorporating additional features into the training process. To include attributes like age, expression, or accessories, the model can be trained on a more extensive dataset that includes a wide range of facial variations. This dataset should encompass images with diverse age groups, various expressions, and different types of accessories worn by individuals. By exposing the model to a more comprehensive set of facial attributes during training, it can learn to generate personalized textures that reflect these characteristics accurately. Furthermore, the model architecture can be modified to include specific modules or branches dedicated to different facial attributes. For example, separate branches can be designed to focus on age estimation, expression recognition, or accessory detection. These branches can work in parallel with the existing framework, allowing the model to capture and incorporate a broader spectrum of facial features into the texture generation process. By enhancing the training data and adapting the model architecture to accommodate a wider range of facial attributes, the proposed method can be extended to handle diverse characteristics beyond race and gender, enabling more detailed and personalized texture generation for a variety of facial attributes.