Core Concepts
The author presents a novel method for video-driven animation of high-quality 3D neural head avatars, emphasizing the integration of realism and convenience in virtual environments.
Abstract
The content introduces an innovative approach for animating high-quality 3D neural head avatars from video input, focusing on person-independent animation. The method combines personalized head models with multi-person facial performance capture to enhance realism and convenience in VR experiences. By utilizing LSTM-based animation networks and neural rendering techniques, the approach achieves seamless integration of personalized head avatars into multi-person video-based animations. The content discusses related work, the employed hybrid head representation, video-based neural animation process, experimental results, limitations, conclusions, and acknowledgments.
Stats
"Our method overcomes the limitations of existing techniques by seamlessly integrating (photo) realism of personalized head avatars into multi-person video-based animation."
"We train the network for 15000 iterations using the Adam optimizer with a learning rate of 1.0e−4."
"The captured data was split into four sequences with a total length of approximately 3 minutes for training."
Quotes
"We present a new method for the animation of 3D neural head models."
"Our contribution enhances the fidelity and realism of video-driven facial animations."
"Our intuition on why the residual features improve animation during inference is that they reduce the likelihood that the network learns spurious correlations between input expression features and target animation parameters."