  Facial expressions, vital in non-verbal human communication, have found
applications in various computer vision fields like virtual reality, gaming,
and emotional AI assistants. Despite advancements, many facial expression
generation models encounter challenges such as low resolution (e.g., 32x32 or
64x64 pixels), poor quality, and the absence of background details. In this
paper, we introduce FacEnhance, a novel diffusion-based approach addressing
constraints in existing low-resolution facial expression generation models.
FacEnhance enhances low-resolution facial expression videos (64x64 pixels) to
higher resolutions (192x192 pixels), incorporating background details and
improving overall quality. Leveraging conditional denoising within a diffusion
framework, guided by a background-free low-resolution video and a single
neutral expression high-resolution image, FacEnhance generates a video
incorporating the facial expression from the low-resolution video performed by
the individual with background from the neutral image. By complementing
lightweight low-resolution models, FacEnhance strikes a balance between
computational efficiency and desirable image resolution and quality. Extensive
experiments on the MUG facial expression database demonstrate the efficacy of
FacEnhance in enhancing low-resolution model outputs to state-of-the-art
quality while preserving content and identity consistency. FacEnhance
represents significant progress towards resource-efficient, high-fidelity
facial expression generation, Renewing outdated low-resolution methods to
up-to-date standards.
