Enhancing Perception and Immersion in Pre-Captured Environments through
  Learning-Based Eye Height Adaptation
Qi Feng Hubert P. H. Shum Shigeo Morishima
Pre-captured immersive environments using omnidirectional cameras provide a
wide range of virtual reality applications. Previous research has shown that
manipulating the eye height in egocentric virtual environments can
significantly affect distance perception and immersion. However, the influence
of eye height in pre-captured real environments has received less attention due
to the difficulty of altering the perspective after finishing the capture
process. To explore this influence, we first propose a pilot study that
captures real environments with multiple eye heights and asks participants to
judge the egocentric distances and immersion. If a significant influence is
confirmed, an effective image-based approach to adapt pre-captured real-world
environments to the user's eye height would be desirable. Motivated by the
study, we propose a learning-based approach for synthesizing novel views for
omnidirectional images with altered eye heights. This approach employs a
multitask architecture that learns depth and semantic segmentation in two
formats, and generates high-quality depth and semantic segmentation to
facilitate the inpainting stage. With the improved omnidirectional-aware
layered depth image, our approach synthesizes natural and realistic visuals for
eye height adaptation. Quantitative and qualitative evaluation shows favorable
results against state-of-the-art methods, and an extensive user study verifies
improved perception and immersion for pre-captured real-world environments.