Motion Hologram: Jointly optimized hologram generation and motion planning for photorealistic 3D displays via reinforcement learning

Holography is capable of rendering three-dimensional scenes with full-depth control and delivering transformative experiences across numerous domains, including virtual and augmented reality, education, and communication. However, traditional holography presents 3D scenes with unnatural defocus and severe speckles due to the limited space bandwidth product of the spatial light modulator (SLM). Here, we introduce Motion Hologram, a holographic technique that accurately portrays photorealistic and speckle-free 3D scenes. This technique leverages a single hologram and a learnable motion trajectory, which are jointly optimized within a deep reinforcement learning framework. Specifically, we experimentally demonstrated that the proposed technique could achieve a 4- to 5-dB PSNR improvement of focal stacks in comparison with traditional holography and could successfully depict speckle-free, high-fidelity, and full-color 3D displays using only a commercial SLM. We believe that the proposed method promises a prospective form of holographic displays that will offer immersive viewing experiences for audiences. Motion Hologram was proposed to accurately portray photorealistic and speckle-free 3D scenes via reinforcement learning.