  Recent advancements in deep learning and computer vision have led to a surge
of interest in generating realistic talking heads. This paper presents a
comprehensive survey of state-of-the-art methods for talking head generation.
We systematically categorises them into four main approaches: image-driven,
audio-driven, video-driven and others (including neural radiance fields (NeRF),
and 3D-based methods). We provide an in-depth analysis of each method,
highlighting their unique contributions, strengths, and limitations.
Furthermore, we thoroughly compare publicly available models, evaluating them
on key aspects such as inference time and human-rated quality of the generated
outputs. Our aim is to provide a clear and concise overview of the current
landscape in talking head generation, elucidating the relationships between
different approaches and identifying promising directions for future research.
This survey will serve as a valuable reference for researchers and
practitioners interested in this rapidly evolving field.
