Depth map compression via 3D region-based representation

In 3D video, view synthesis is used to create new virtual views between encoded camera views. Errors in the coding of the depth maps introduce geometry inconsistencies in synthesized views. In this paper, a new 3D plane representation of the scene is presented which improves the performance of current standard video codecs in the view synthesis domain. Two image segmentation algorithms are proposed for generating a color and depth segmentation. Using both partitions, depth maps are segmented into regions without sharp discontinuities without having to explicitly signal all depth edges. The resulting regions are represented using a planar model in the 3D world scene. This 3D representation allows an efficient encoding while preserving the 3D characteristics of the scene. The 3D planes open up the possibility to code multiview images with a unique representation.Postprint (author's final draft

Efficient rendering for three-dimensional displays

This thesis explores more efficient methods for visualizing point data sets on three-dimensional (3D) displays. Point data sets are used in many scientific applications, e.g. cosmological simulations. Visualizing these data sets in {3D} is desirable because it can more readily reveal structure and unknown phenomena. However, cutting-edge scientific point data sets are very large and producing/rendering even a single image is expensive. Furthermore, current literature suggests that the ideal number of views for 3D (multiview) displays can be in the hundreds, which compounds the costs. The accepted notion that many views are required for {3D} displays is challenged by carrying out a novel human factor trials study. The results suggest that humans are actually surprisingly insensitive to the number of viewpoints with regard to their task performance, when occlusion in the scene is not a dominant factor. Existing stereoscopic rendering algorithms can have high set-up costs which limits their use and none are tuned for uncorrelated {3D} point rendering. This thesis shows that it is possible to improve rendering speeds for a low number of views by perspective reprojection. The novelty in the approach described lies in delaying the reprojection and generation of the viewpoints until the fragment stage of the pipeline and streamlining the rendering pipeline for points only. Theoretical analysis suggests a fragment reprojection scheme will render at least 2.8 times faster than na\"{i}vely re-rendering the scene from multiple viewpoints. Building upon the fragment reprojection technique, further rendering performance is shown to be possible (at the cost of some rendering accuracy) by restricting the amount of reprojection required according to the stereoscopic resolution of the display. A significant benefit is that the scene depth can be mapped arbitrarily to the perceived depth range of the display at no extra cost than a single region mapping approach. Using an average case-study (rendering from a 500k points for a 9-view High Definition 3D display), theoretical analysis suggests that this new approach is capable of twice the performance gains than simply reprojecting every single fragment, and quantitative measures show the algorithm to be 5 times faster than a naïve rendering approach. Further detailed quantitative results, under varying scenarios, are provided and discussed

New visual coding exploration in MPEG: Super-MultiView and free navigation in free viewpoint TV

ISO/IEC MPEG and ITU-T VCEG have recently jointly issued a new multiview video compression standard, called 3D-HEVC, which reaches unpreceded compression performances for linear,dense camera arrangements. In view of supporting future highquality,auto-stereoscopic 3D displays and Free Navigation virtual/augmented reality applications with sparse, arbitrarily arranged camera setups, innovative depth estimation and virtual view synthesis techniques with global optimizations over all camera views should be developed. Preliminary studies in response to the MPEG-FTV (Free viewpoint TV) Call for Evidence suggest these targets are within reach, with at least 6% bitrate gains over 3DHEVC technology

Dense light field coding: a survey

Light Field (LF) imaging is a promising solution for providing more immersive and closer to reality multimedia experiences to end-users with unprecedented creative freedom and flexibility for applications in different areas, such as virtual and augmented reality. Due to the recent technological advances in optics, sensor manufacturing and available transmission bandwidth, as well as the investment of many tech giants in this area, it is expected that soon many LF transmission systems will be available to both consumers and professionals. Recognizing this, novel standardization initiatives have recently emerged in both the Joint Photographic Experts Group (JPEG) and the Moving Picture Experts Group (MPEG), triggering the discussion on the deployment of LF coding solutions to efficiently handle the massive amount of data involved in such systems. Since then, the topic of LF content coding has become a booming research area, attracting the attention of many researchers worldwide. In this context, this paper provides a comprehensive survey of the most relevant LF coding solutions proposed in the literature, focusing on angularly dense LFs. Special attention is placed on a thorough description of the different LF coding methods and on the main concepts related to this relevant area. Moreover, comprehensive insights are presented into open research challenges and future research directions for LF coding.info:eu-repo/semantics/publishedVersio

Learning Detailed Radiance Manifolds for High-Fidelity and 3D-Consistent Portrait Synthesis from Monocular Image

A key challenge for novel view synthesis of monocular portrait images is 3D consistency under continuous pose variations. Most existing methods rely on 2D generative models which often leads to obvious 3D inconsistency artifacts. We present a 3D-consistent novel view synthesis approach for monocular portrait images based on a recent proposed 3D-aware GAN, namely Generative Radiance Manifolds (GRAM), which has shown strong 3D consistency at multiview image generation of virtual subjects via the radiance manifolds representation. However, simply learning an encoder to map a real image into the latent space of GRAM can only reconstruct coarse radiance manifolds without faithful fine details, while improving the reconstruction fidelity via instance-specific optimization is time-consuming. We introduce a novel detail manifolds reconstructor to learn 3D-consistent fine details on the radiance manifolds from monocular images, and combine them with the coarse radiance manifolds for high-fidelity reconstruction. The 3D priors derived from the coarse radiance manifolds are used to regulate the learned details to ensure reasonable synthesized results at novel views. Trained on in-the-wild 2D images, our method achieves high-fidelity and 3D-consistent portrait synthesis largely outperforming the prior art.Comment: Project page: https://yudeng.github.io/GRAMInverter

AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image Collections

Previous animatable 3D-aware GANs for human generation have primarily focused on either the human head or full body. However, head-only videos are relatively uncommon in real life, and full body generation typically does not deal with facial expression control and still has challenges in generating high-quality results. Towards applicable video avatars, we present an animatable 3D-aware GAN that generates portrait images with controllable facial expression, head pose, and shoulder movements. It is a generative model trained on unstructured 2D image collections without using 3D or video data. For the new task, we base our method on the generative radiance manifold representation and equip it with learnable facial and head-shoulder deformations. A dual-camera rendering and adversarial learning scheme is proposed to improve the quality of the generated faces, which is critical for portrait images. A pose deformation processing network is developed to generate plausible deformations for challenging regions such as long hair. Experiments show that our method, trained on unstructured 2D images, can generate diverse and high-quality 3D portraits with desired control over different properties.Comment: SIGGRAPH Asia 2023. Project Page: https://yuewuhkust.github.io/AniPortraitGAN