One-Shot Feed-Forward 360° Animatable Avatar via Inpainted UV-Space Gaussian Modeling

Building one-shot 3D animatable head avatars is an impor-tant yet challenging problem. Existing methods generally collapse underlarge camera pose variations, compromising the realism of 3D avatars.In this work, we propose a new framework to tackle the novel settingof one-shot 3D full-head animatable avatar reconstruction in a singleforward pass via inpainted UV-space Gaussian modeling, enabling 360◦rendering views and real-time animation. To facilitate efficient animationcontrol, we model 3D head avatars with Gaussian primitives embeddedon the surface of a parametric face model within the UV space, andproject the input image features to the UV space, resulting in incom-plete local UV feature maps. To inpaint the missing regions, we obtainknowledge of full-head geometry and textures from rich 3D full-head pri-ors within a pretrained 3D generative adversarial network (GAN) forglobal full-head feature extraction and multi-view supervision. Specifi-cally, to enhance the fidelity of 3D reconstruction during inpainting, wetake advantage of the symmetric nature of the UV space and humanfaces to fuse incomplete yet detailed local UV feature maps with theextracted global full-head textures, resulting in inpainted UV Gaussianattribute maps for avatar modeling. Extensive experiments demonstratethat our method is the first to achieve high-quality 3D full-head animat-able avatar modeling, significantly improving side and back views whileoutperforming state-of-the-art animation approaches, thereby improvingthe realism of 3D animatable avatars. The project page is available athttps://shaelynz.github.io/fhavatar/.