Gaussian Splatting has changed the game for real-time photo-realistic rendering. One of the most popular applications of Gaussian Splatting is to create animatable avatars, known as Gaussian Avatars. Recent works have pushed the boundaries of quality and rendering efficiency but suffer from two main limitations. Either they require expensive multi-camera rigs to produce avatars with free-view rendering, or they can be trained with a single camera but only rendered at high quality from this fixed viewpoint. An ideal model would be trained using a short monocular video or image from available hardware, such as a webcam, and rendered from any view. To this end, we propose GASP: Gaussian Avatars with Synthetic Priors. To overcome the limitations of existing datasets, we exploit the pixel-perfect nature of synthetic data to train a Gaussian Avatar prior. By fitting this prior model to a single photo or video and fine-tuning it, we get a high-quality Gaussian Avatar, which supports 360$^\circ$ rendering. Our prior is only required for fitting, not inference, enabling real-time application. Through our method, we obtain high-quality, animatable Avatars from limited data which can be animated and rendered at 70fps on commercial hardware. See our project page (https://microsoft.github.io/GASP/) for results.

翻译：高斯泼溅技术彻底改变了实时照片级真实感渲染的格局。创建可动画化身（即高斯化身）是该技术最热门的应用之一。近期研究在质量和渲染效率方面不断突破，但仍存在两大局限：要么需要昂贵的多相机阵列才能生成支持自由视角渲染的化身，要么虽能通过单相机训练却只能从固定视角进行高质量渲染。理想模型应能利用网络摄像头等现有硬件采集的短单目视频或图像进行训练，并支持任意视角渲染。为此，我们提出GASP：基于合成先验的高斯化身。为克服现有数据集的局限性，我们利用合成数据像素级精确的特性训练高斯化身先验模型。通过将单张照片或视频适配至此先验模型并进行微调，即可获得支持360$^\circ$渲染的高质量高斯化身。该先验仅用于适配过程而非推理阶段，从而保障实时应用。通过本方法，我们能够从有限数据中获取高质量可动画化身，在商用硬件上实现70fps的动画渲染速率。实验结果请参见项目页面（https://microsoft.github.io/GASP/）。