Learning rate is decayed by 0.5 after 40 epochs.

2.7. Real video dataset pre-processing

Our training and testing videos are all captured with one single ﬁxed camera. For training, we record two videos: one
head rotation video to capture the full facial appearance from different angles, and one talking video to capture common and
mild expressions in a speech sequence. For testing, we ask the subjects to perform strong unseen expressions such as a big
smile, jaw opening, pouting, and rising of the eyebrows.

For both training and testing videos, we use DECA [21] to regress the initial FLAME [35] shape, expression, and pose
parameters. Unfortunately, the eye poses (gaze directions) are not tracked in our pre-processing pipeline. To reﬁne the
regressed FLAME parameters, we estimate the facial keypoint with [6] and optimize the regressed parameters and global
translation vectors jointly. The primary optimization objective is the keypoint error:
I M Avatar- Implicit Morphable Head Avatars from Videos