The field of computer vision is moving towards more sophisticated and nuanced representations of images and faces. Researchers are exploring new methods for facial stylization, face editing, and image representation, with a focus on preserving semantic information and achieving high-fidelity results. One notable trend is the use of Gaussian-based approaches, such as Gaussian Splatting and HyperGaussians, which offer improved expressivity and efficiency. Another area of research is the development of more effective and efficient methods for virtual try-on, outfit retrieval, and recommendation. Overall, the field is advancing rapidly, with new techniques and architectures being proposed to address ongoing challenges in image and face representation. Notable papers include: Instant GaussianImage, which proposes a generalizable and self-adaptive image representation framework, and HyperGaussians, which introduces a novel extension of 3D Gaussian Splatting for high-quality animatable face avatars. DiffFit is also noteworthy, as it achieves high-fidelity virtual try-on through a two-stage latent diffusion framework.