GASP: 带有合成先验的高斯化身
GASP: Gaussian Avatars with Synthetic Priors
AI总结：
针对现有高斯化身方法依赖多相机设备或仅支持固定视角渲染的问题，提出GASP方法，利用合成数据训练高斯化身先验，经单目数据适配微调后可实现360°实时高质量可驱动化身渲染。
摘要：
高斯溅射（Gaussian Splatting）彻底改变了实时照片级真实感渲染的格局。高斯溅射最受欢迎的应用之一是创建可驱动的化身，即高斯化身（Gaussian Avatars）。近期的研究在质量和渲染效率上不断突破，但存在两个主要局限：要么需要昂贵的多相机设备才能生成支持自由视角渲染的化身，要么虽可通过单相机训练，但仅能在该固定视角下实现高质量渲染。理想的模型应能利用现有硬件（如网络摄像头）拍摄的短单目视频或图像进行训练，且支持任意视角渲染。为此，我们提出GASP：带有合成先验的高斯化身（Gaussian Avatars with Synthetic Priors）。为克服现有数据集的局限，我们利用合成数据像素级精准的特性来训练高斯化身先验。通过将该先验模型适配到单张照片或视频并进行微调，我们得到了支持360°渲染的高质量高斯化身。该先验仅在适配阶段需要，推理阶段无需使用，因此可支持实时应用。通过我们的方法，我们从有限数据中获得了高质量、可驱动的化身，在商用硬件上可实现70fps的动画渲染与播放。更多结果请参见我们的项目页面（https://microsoft.github.io/GASP/）。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
