Temporal-MPI: Enabling Multi-Plane Images for Dynamic Scene Modelling via Temporal Basis Learning

Novel view synthesis of static scenes has achieved remarkable advancements in producing photo-realistic results. However, key challenges remain for immersive rendering for dynamic contents. For example, one of the seminal image-based rendering frameworks, the multi-plane image (MPI) produces high novel-view synthesis quality for static scenes but faces difficulty in modeling dynamic parts. In addition, modeling dynamic variations through MPI may require huge storage space and long inference time, which hinders its application in real-time scenarios. In this paper, we propose a novel Temporal-MPI representation which is able to encode the rich 3D and dynamic variation information throughout the entire video as compact temporal basis. Novel-views at arbitrary time-instance will be able to be rendered real-time with high visual quality due to the highly compact and expressive latent basis and the coefficients jointly learned. We show that given comparable memory consumption, our proposed Temporal-MPI framework is able to generate a time-instance MPI with only 0.002 seconds, which is up to 3000 times faster, with 3dB higher average view-synthesis PSNR as compared with other state-of-the-art dynamic scene modelling frameworks.

Scale-Consistent Fusion: from Heterogeneous Local Sampling to Global Immersive Rendering

Image-based geometric modeling and novel view synthesis based on sparse, large-baseline samplings are challenging but important tasks for emerging multimedia applications such as virtual reality and immersive telepresence. Existing methods fail to produce satisfactory results due to the limitation on inferring reliable depth information over such challenging reference conditions. With the popularization of commercial light field (LF) cameras, capturing LF images (LFIs) is as convenient as taking regular photos, and geometry information can be reliably inferred. This inspires us to use a sparse set of LF captures to render high-quality novel views globally. However, fusion of LF captures from multiple angles is challenging due to the scale inconsistency caused by various capture settings. To overcome this challenge, we propose a novel scale-consistent volume rescaling algorithm that robustly aligns the disparity probability volumes (DPV) among different captures for scale-consistent global geometry fusion. Based on the fused DPV projected to the target camera frustum, novel learning-based modules have been proposed (i.e., the attention-guided multi-scale residual fusion module, and the disparity field guided deep re-regularization module) which comprehensively regularize noisy observations from heterogeneous captures for high-quality rendering of novel LFIs. Both quantitative and qualitative experiments over the Stanford Lytro Multi-view LF dataset show that the proposed method outperforms state-of-the-art methods significantly under different experiment settings for disparity inference and LF synthesis.