HeadGAP - 3D Avatar Generation Model Launched by ByteDance

What is HeadGAP?

HeadGAP is a 3D avatar generation model jointly developed by ByteDance and ShanghaiTech University. It quickly generates realistic 3D avatars using only a small number of images. It employs a framework of prior learning and personalized creation, based on 3D head prior information derived from a large-scale, multi-view dynamic dataset. Through a Gaussian Splatting auto-decoding network and partial dynamic modeling, combined with identity-sharing encoding and personalized latent codes, HeadGAP achieves high-fidelity and animable 3D avatars with multi-view consistency and stable animation effects.

HeadGAP's main functions

• Few-shot learningIt can create realistic 3D avatars from a very small number of images (even just one).
• High fidelityThe generated 3D avatars have photorealistic rendering quality, rich in detail and realistic.
• Animation robustnessThe avatars are not only visually realistic, but also feature smooth animation that adapts to different facial expressions and movements.
• Personalized customizationThrough prior learning and personalization phases, it is possible to perform customized processing based on the specific characteristics of users.
• Multi-perspective consistencyThe profile picture remains consistent from different angles, whether it's a front view, a side view, or an oblique view.

HeadGAP's technical principles

• Prior learning phaseAt this stage, the system learns and extracts the general features and shape priors of the head by analyzing 3D head models in a large-scale multi-view dynamic dataset.
• Gaussian Splatting NetworkAn autodecoder network based on Gaussian distribution can represent 3D head data distribution as a set of Gaussian primitives, capturing the complex geometric structure of the head.
• Identity sharing coding and personalized potential codesIt employs shared coding to learn common features across different identities, while simultaneously generating personalized latent codes for each individual to learn their unique attributes.
• Partial dynamic modelingBy using a component-based modeling approach, the various parts of an avatar can be dynamically adjusted to adapt to different facial expressions and movements.

HeadGAP's project address

• Project official websiteheadgap.github.io
• GitHub repository:https://github.com/headgap/headgap.github.io
• arXiv technical paper:https://arxiv.org/pdf/2408.06019

How to use HeadGAP

• Data preparationPrepare a small number of pictures of the target person. The pictures can be taken from different angles. HeadGAP can learn the characteristics of the person from multiple perspectives.
• Upload ImageUpload these images to the HeadGAP system. This can be done by accessing the official website or the provided API.
• Prior learningThe system will use the uploaded images for prior learning to extract prior information of 3D heads from a large-scale, multi-view dynamic dataset.
• Avatar CreationBased on the learned prior information, the system will enter the avatar creation stage. In this stage, the system will generate an initial 3D avatar model.

Application scenarios of HeadGAP

• Virtual socialIn virtual reality or augmented reality social platforms, users can use 3D avatars generated by HeadGAP as their virtual avatars.
• Game developmentGame developers can use HeadGAP technology to create realistic 3D avatars for game characters, enhancing the immersion and personalization of the game experience.
• Film and animation productionFor film or animation production, HeadGAP can generate or reconstruct facial features of characters, achieving more realistic and vivid performance capture.
• Education and trainingIn simulation training or educational software, HeadGAP can be used to create realistic virtual teacher or student avatars to enhance the learning experience.