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Computer-generated holography (CGH) simulates the propagation and interference of complex light waves, allowing it to reconstruct realistic images captured from a specific viewpoint by solving the corresponding Maxwell equations. However, in applications such as virtual and augmented reality, viewers should freely observe holograms from arbitrary viewpoints, much as how we naturally see the physical world. In this work, we train a neural network to generate holograms at any view in a scene. Our result is the Neural Holographic Field: the first artificial-neural-network-based representation for light wave propagation in free space and transform sparse 2D photos into holograms that are not only 3D but also freely viewable from any perspective. We demonstrate by visualizing various smartphone-captured scenes from arbitrary six-degree-of-freedom viewpoints on a prototype holographic display. To this end, we encode the measured light intensity from photos into a neural network representation of underlying wavefields. Our method implicitly learns the amplitude and phase surrogates of the underlying incoherent light waves under coherent light display conditions. During playback, the learned model predicts the underlying continuous complex wavefront propagating to arbitrary views to generate holograms. 

Display power consumption is an emerging concern for untethered devices. This goes double for augmented and virtual extended reality (XR) displays, which target high refresh rates and high resolutions while conforming to an ergonomically light form factor. A number of image mapping techniques have been proposed to extend battery usage. However, there is currently no comprehensive quantitative understanding of how the power savings provided by these methods compare to their impact on visual quality. We set out to answer this question. To this end, we present a perceptual evaluation of algorithms (PEA) for power optimization in XR displays (PODs). Consolidating a portfolio of six power-saving display mapping approaches, we begin by performing a large-scale perceptual study to understand the impact of each method on perceived quality in the wild. This results in a unified quality score for each technique, scaled in just-objectionable-difference (JOD) units. In parallel, each technique is analyzed using hardware-accurate power models. The resulting JOD-to-Milliwatt transfer function provides a first-of-its-kind look into tradeoffs offered by display mapping techniques, and can be directly employed to make architectural decisions for power budgets on XR displays. Finally, we leverage our study data and power models to address important display power applications like the choice of display primary, power implications of eye tracking, and more¹. 

While tremendous advances in visual and auditory realism have been made for virtual and augmented reality (VR/AR), introducing a plausible sense of physicality into the virtual world remains challenging. Closing the gap between real-world physicality and immersive virtual experience requires a closed interaction loop: applying user-exerted physical forces to the virtual environment and generating haptic sensations back to the users. However, existing VR/AR solutions either completely ignore the force inputs from the users or rely on obtrusive sensing devices that compromise user experience. By identifying users' muscle activation patterns while engaging in VR/AR, we design a learning-based neural interface for natural and intuitive force inputs. Specifically, we show that lightweight electromyography sensors, resting non-invasively on users' forearm skin, inform and establish a robust understanding of their complex hand activities. Fuelled by a neural-network-based model, our interface can decode finger-wise forces in real-time with 3.3% mean error, and generalize to new users with little calibration. Through an interactive psychophysical study, we show that human perception of virtual objects' physical properties, such as stiffness, can be significantly enhanced by our interface. We further demonstrate that our interface enables ubiquitous control via finger tapping. Ultimately, we envision our findings to push forward research towards more realistic physicality in future VR/AR.