CAP4D: Creating Animatable 4D Portrait Avatars with Morphable Multi-View Diffusion Models
This addresses the need for high-quality avatar creation in applications like advertising and virtual reality, bridging the gap between single-image and multi-view methods.
The paper tackles the problem of reconstructing photorealistic and dynamic 4D portrait avatars from varying numbers of reference images, achieving state-of-the-art performance across single-, few-, and multi-image scenarios.
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.