  Holography stands at the forefront of visual technology innovation, offering
immersive, three-dimensional visualizations through the manipulation of light
wave amplitude and phase. Contemporary research in hologram generation has
predominantly focused on image-to-hologram conversion, producing holograms from
existing images. These approaches, while effective, inherently limit the scope
of innovation and creativity in hologram generation. In response to this
limitation, we present Holo-VQVAE, a novel generative framework tailored for
phase-only holograms (POHs). Holo-VQVAE leverages the architecture of Vector
Quantized Variational AutoEncoders, enabling it to learn the complex
distributions of POHs. Furthermore, it integrates the Angular Spectrum Method
into the training process, facilitating learning in the image domain. This
framework allows for the generation of unseen, diverse holographic content
directly from its intricately learned latent space without requiring
pre-existing images. This pioneering work paves the way for groundbreaking
applications and methodologies in holographic content creation, opening a new
era in the exploration of holographic content.
