  Synthesizing photorealistic 4D human head avatars from videos is essential
for VR/AR, telepresence, and video game applications. Although existing Neural
Radiance Fields (NeRF)-based methods achieve high-fidelity results, the
computational expense limits their use in real-time applications. To overcome
this limitation, we introduce BakedAvatar, a novel representation for real-time
neural head avatar synthesis, deployable in a standard polygon rasterization
pipeline. Our approach extracts deformable multi-layer meshes from learned
isosurfaces of the head and computes expression-, pose-, and view-dependent
appearances that can be baked into static textures for efficient rasterization.
We thus propose a three-stage pipeline for neural head avatar synthesis, which
includes learning continuous deformation, manifold, and radiance fields,
extracting layered meshes and textures, and fine-tuning texture details with
differential rasterization. Experimental results demonstrate that our
representation generates synthesis results of comparable quality to other
state-of-the-art methods while significantly reducing the inference time
required. We further showcase various head avatar synthesis results from
monocular videos, including view synthesis, face reenactment, expression
editing, and pose editing, all at interactive frame rates.
