  Speech-driven 3D facial animation aims at generating facial movements that
are synchronized with the driving speech, which has been widely explored
recently. Existing works mostly neglect the person-specific talking style in
generation, including facial expression and head pose styles. Several works
intend to capture the personalities by fine-tuning modules. However, limited
training data leads to the lack of vividness. In this work, we propose AdaMesh,
a novel adaptive speech-driven facial animation approach, which learns the
personalized talking style from a reference video of about 10 seconds and
generates vivid facial expressions and head poses. Specifically, we propose
mixture-of-low-rank adaptation (MoLoRA) to fine-tune the expression adapter,
which efficiently captures the facial expression style. For the personalized
pose style, we propose a pose adapter by building a discrete pose prior and
retrieving the appropriate style embedding with a semantic-aware pose style
matrix without fine-tuning. Extensive experimental results show that our
approach outperforms state-of-the-art methods, preserves the talking style in
the reference video, and generates vivid facial animation. The supplementary
video and code will be available at https://adamesh.github.io.
