CAP4D：利用可变形多视图扩散模型创建可动画化的4D人像头像
CAP4D: Creating Animatable 4D Portrait Avatars with Morphable Multi-View Diffusion Models
AI总结：
CAP4D提出一种可变形多视图扩散模型，从1至100张参考图像重建逼真4D人像头像并实时动画化，在单、少、多图像重建中达到最先进性能，弥合了单图与多视图技术的视觉保真度差距。
摘要：
从图像中重建逼真的动态人像头像对于广告、视觉效果和虚拟现实等许多应用至关重要。根据应用的不同，头像重建涉及不同的采集设置和约束——例如，视觉效果工作室使用相机阵列来捕获数百张参考图像，而内容创作者可能希望动画化从互联网下载的单张人像图像。因此，存在一个庞大且异构的头像重建方法生态系统。基于多视图立体或神经渲染的技术实现了最高质量的结果，但需要数百张参考图像。最近的生成模型可以从单张参考图像生成令人信服的头像，但视觉保真度仍落后于多视图技术。在此，我们提出CAP4D：一种使用可变形多视图扩散模型的方法，可以从任意数量的参考图像（即1到100张）重建逼真的4D（动态3D）人像头像，并实时动画化和渲染它们。我们的方法在单图像、少图像和多图像4D人像头像重建中展示了最先进的性能，并采取措施弥合单图像和多视图重建技术之间的视觉保真度差距。
英文摘要：
Reconstructing photorealistic and dynamic portrait avatars from images is essential to many applications including advertising, visual effects, and virtual reality. Depending on the application, avatar reconstruction involves different capture setups and constraints $-$ for example, visual effects studios use camera arrays to capture hundreds of reference images, while content creators may seek to animate a single portrait image downloaded from the internet. As such, there is a large and heterogeneous ecosystem of methods for avatar reconstruction. Techniques based on multi-view stereo or neural rendering achieve the highest quality results, but require hundreds of reference images. Recent generative models produce convincing avatars from a single reference image, but visual fidelity yet lags behind multi-view techniques. Here, we present CAP4D: an approach that uses a morphable multi-view diffusion model to reconstruct photoreal 4D (dynamic 3D) portrait avatars from any number of reference images (i.e., one to 100) and animate and render them in real time. Our approach demonstrates state-of-the-art performance for single-, few-, and multi-image 4D portrait avatar reconstruction, and takes steps to bridge the gap in visual fidelity between single-image and multi-view reconstruction techniques.
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