Abstract
Creating high-fidelity, animatable 3D talking heads is crucial for immersive applications, yet often hindered by the prevalence of low-quality image or video sources, which yield poor 3D reconstructions. In this paper, we introduce SuperHead, a novel framework for enhancing low-resolution, animatable 3D head avatars. The core challenge lies in synthesizing high-quality geometry and textures, while ensuring both 3D and temporal consistency during animation and preserving subject identity. Despite recent progress in image, video and 3D-based super-resolution (SR), existing SR techniques are ill-equipped to handle dynamic 3D inputs. To address this, SuperHead leverages the rich priors from pre-trained 3D generative models via a novel dynamics-aware 3D inversion scheme. This process optimizes the latent representation of the generative model to produce a super-resolved 3D Gaussian Splatting (3DGS) head model, which is subsequently rigged to an underlying parametric head model (e.g., FLAME) for animation. The inversion is jointly supervised using a sparse collection of upscaled 2D face renderings and corresponding depth maps, captured from diverse facial expressions and camera viewpoints, to ensure realism under dynamic facial motions. Experiments demonstrate that SuperHead generates avatars with fine-grained facial details under dynamic motions, significantly outperforming baseline methods in visual quality.
Abstract (translated)
创建高保真、可动画化的3D说话头像对于沉浸式应用至关重要，然而这一过程常常受到低质量图像或视频源的限制，这些来源会导致不佳的3D重建效果。在本文中，我们介绍了SuperHead，这是一种用于提升低分辨率、可动画化3D头部角色的新框架。核心挑战在于合成高质量的几何结构和纹理，并且确保在动态过程中保持三维一致性和时间一致性的同时保留主体身份。尽管图像、视频和基于3D的超分辨率（SR）技术有所进步，但现有的SR技术难以处理动态3D输入。为了解决这一问题，SuperHead通过一种新的感知动力学的3D反演方案利用了预训练3D生成模型中的丰富先验信息。该过程优化了生成模型的潜在表示以产生超分辨率的3DGaussian Splatting（3DGS）头部模型，并随后将其与底层参数化头部模型（例如FLAME）绑定，以便进行动画处理。反演过程中采用稀疏的、放大后的2D面部渲染图像及其对应深度图集合联合监督，这些数据涵盖了各种面部表情和相机视角，以确保在动态面部动作下的真实感。实验结果表明，SuperHead生成了具有细致面部细节的头像，在动态运动下显著优于基线方法，并且在视觉质量上表现出色。
URL
https://arxiv.org/abs/2602.06122
