Recently, researchers from ByteDance and ShanghaiTech University have developed a study named "HeadGAP" that has garnered significant attention. The research team has proposed a new method that can quickly create highly realistic, animatable 3D virtual human head models from just three photographs of a target individual from different perspectives, and can synchronize facial expressions based on reference videos.
The research team demonstrated how personalized avatars can be created with minimal data in real-world scenarios. In this study, researchers first conducted a phase called "prior learning," where they extracted prior information about 3D heads from a large multi-view dynamic dataset. This prior information helps the system understand different head features and expressions. Subsequently, in the "avatar creation" phase, researchers utilized this prior information for personalized customization, generating virtual avatars of the target individuals.
The entire process employs a Gaussian point cloud-based self-decoding network, combined with partial dynamic modeling. This method allows the system to quickly capture each individual's uniqueness and perform personalized avatar optimization. The team also employed techniques such as inversion and fine-tuning strategies to make the personalized avatar process more efficient, ultimately achieving photo-realistic rendering effects and multi-view consistency.
In experiments, the research team demonstrated the performance of their method in various scenarios. The results showed that the generated 3D avatars maintained high quality and stable animation effects whether in controlled or real-world environments. This achievement not only has broad application prospects in virtual social interactions, game development, and other fields but also provides new ideas and methods for personalized 3D avatar creation.
Product Entry: https://top.aibase.com/tool/headgap
Key Points:
🎨 The research team, through the "HeadGAP" method, can create realistic 3D virtual head models with just a few photographs.
🚀 This method employs a Gaussian point network and dynamic modeling technology to achieve personalized avatar customization and optimization.
🖼️ Experimental results show that the generated avatars have excellent rendering quality and animation performance, suitable for multiple application scenarios.