Abstract: Face animation, one of the hottest topics in computer vision, has achieved a
promising performance with the help of generative models. However, it remains a
critical challenge to generate identity preserving and photo-realistic images
due to the sophisticated motion deformation and complex facial detail modeling.
To address these problems, we propose a Face Neural Volume Rendering (FNeVR)
network to fully explore the potential of 2D motion warping and 3D volume
rendering in a unified framework. In FNeVR, we design a 3D Face Volume
Rendering (FVR) module to enhance the facial details for image rendering.
Specifically, we first extract 3D information with a well-designed
architecture, and then introduce an orthogonal adaptive ray-sampling module for
efficient rendering. We also design a lightweight pose editor, enabling FNeVR
to edit the facial pose in a simple yet effective way. Extensive experiments
show that our FNeVR obtains the best overall quality and performance on widely
used talking-head benchmarks.