arXiv:2601.12770cs.CV2026-01中稿 · ECCV

仅用一张图实现360度可动画头像,实时渲染且细节逼真。

One-Shot Feed-Forward 360$^{\circ}$ Animatable Avatar via Inpainted UV-Space Gaussian Modeling

  • 在UV空间用高斯分布建模,结合局部特征与全局先验补全缺失信息。
  • 首次实现单次前向传播完成高质量全头建模,侧视和背面效果显著提升。
  • 适合虚拟人、数字主播等需快速生成真实感头像的场景。

构建单张图像驱动的3D可动画头部化身是重要但具挑战性的问题。现有方法在大视角变化下常失效,影响3D化身的真实感。本文提出一种新框架,通过补全的UV空间高斯建模,在单次前向传播中实现360°渲染视角与实时动画。利用参数化人脸模型上的高斯原语嵌入UV空间,将输入图像特征投影至UV空间,形成不完整的局部特征图。为补全缺失区域,从预训练的3D生成对抗网络(GAN)中提取全头几何与纹理先验,实现全局特征提取与多视角监督。具体地,借助UV空间与人脸的对称性,融合局部详细特征与全局纹理,生成补全后的UV高斯属性图用于化身建模。大量实验表明,本方法首次实现高质量360°全头可动画化身建模,在侧视与后视效果上显著优于当前最优方案,大幅提升了3D可动画化身的真实感。

原文摘要 · Abstract (English)

Building one-shot 3D animatable head avatars is an important yet challenging problem. Existing methods generally collapse under large camera pose variations, compromising the realism of 3D avatars. In this work, we propose a new framework to tackle the novel setting of one-shot 3D full-head animatable avatar reconstruction in a single forward pass via inpainted UV-space Gaussian modeling, enabling 360$^\circ$ rendering views and real-time animation. To facilitate efficient animation control, we model 3D head avatars with Gaussian primitives embedded on the surface of a parametric face model within the UV space, and project the input image features to the UV space, resulting in incomplete local UV feature maps. To inpaint the missing regions, we obtain knowledge of full-head geometry and textures from rich 3D full-head priors within a pretrained 3D generative adversarial network (GAN) for global full-head feature extraction and multi-view supervision. Specifically, to enhance the fidelity of 3D reconstruction during inpainting, we take advantage of the symmetric nature of the UV space and human faces to fuse incomplete yet detailed local UV feature maps with the extracted global full-head textures, resulting in inpainted UV Gaussian attribute maps for avatar modeling. Extensive experiments demonstrate that our method is the first to achieve high-quality 3D full-head animatable avatar modeling, significantly improving side and back views while outperforming state-of-the-art animation approaches, thereby improving the realism of 3D animatable avatars.

3D建模头像生成高斯建模单图生成

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