  Real-time and robust photorealistic avatars for telepresence in AR/VR have
been highly desired for enabling immersive photorealistic telepresence.
However, there still exists one key bottleneck: the considerable computational
expense needed to accurately infer facial expressions captured from
headset-mounted cameras with a quality level that can match the realism of the
avatar's human appearance. To this end, we propose a framework called
Auto-CARD, which for the first time enables real-time and robust driving of
Codec Avatars when exclusively using merely on-device computing resources. This
is achieved by minimizing two sources of redundancy. First, we develop a
dedicated neural architecture search technique called AVE-NAS for avatar
encoding in AR/VR, which explicitly boosts both the searched architectures'
robustness in the presence of extreme facial expressions and hardware
friendliness on fast evolving AR/VR headsets. Second, we leverage the temporal
redundancy in consecutively captured images during continuous rendering and
develop a mechanism dubbed LATEX to skip the computation of redundant frames.
Specifically, we first identify an opportunity from the linearity of the latent
space derived by the avatar decoder and then propose to perform adaptive latent
extrapolation for redundant frames. For evaluation, we demonstrate the efficacy
of our Auto-CARD framework in real-time Codec Avatar driving settings, where we
achieve a 5.05x speed-up on Meta Quest 2 while maintaining a comparable or even
better animation quality than state-of-the-art avatar encoder designs.
