Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking

In this paper, we propose a generative framework that unifies depth-based 3D facial pose tracking and face model adaptation on-the-fly, in the unconstrained scenarios with heavy occlusions and arbitrary facial expression variations. Specifically, we introduce a statistical 3D morphable model that flexibly describes the distribution of points on the surface of the face model, with an efficient switchable online adaptation that gradually captures the identity of the tracked subject and rapidly constructs a suitable face model when the subject changes. Moreover, unlike prior art that employed ICP-based facial pose estimation, to improve robustness to occlusions, we propose a ray visibility constraint that regularizes the pose based on the face model's visibility with respect to the input point cloud. Ablation studies and experimental results on Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective and outperforms completing state-of-the-art depth-based methods

Network streaming and compression for mixed reality tele-immersion

Bulterman, D.C.A. [Promotor]Cesar, P.S. [Copromotor

Reliable and timely event notification for publish/subscribe services over the internet

The publish/subscribe paradigm is gaining attention for the development of several applications in wide area networks (WANs) due to its intrinsic time, space, and synchronization decoupling properties that meet the scalability and asynchrony requirements of those applications. However, while the communication in a WAN may be affected by the unpredictable behavior of the network, with messages that can be dropped or delayed, existing publish/subscribe solutions pay just a little attention to addressing these issues. On the contrary, applications such as business intelligence, critical infrastructures, and financial services require delivery guarantees with strict temporal deadlines. In this paper, we propose a framework that enforces both reliability and timeliness for publish/subscribe services over WAN. Specifically, we combine two different approaches: gossiping, to retrieve missing packets in case of incomplete information, and network coding, to reduce the number of retransmissions and, consequently, the latency. We provide an analytical model that describes the information recovery capabilities of our algorithm and a simulation-based study, taking into account a real workload from the Air Traffic Control domain, which evidences how the proposed solution is able to ensure reliable event notification over a WAN within a reasonable bounded time window. © 2013 IEEE

Geometry Compression of 3D Static Point Clouds based on TSPLVQ

International audienceIn this paper, we address the challenging problem of the 3D point cloud compression required to ensure efficient transmission and storage. We introduce a new hierarchical geometry representation based on adaptive Tree-Structured Point-Lattice Vector Quantization (TSPLVQ). This representation enables hierarchically structured 3D content that improves the compression performance for static point cloud. The novelty of the proposed scheme lies in adaptive selection of the optimal quantization scheme of the geometric information, that better leverage the intrinsic correlations in point cloud. Based on its adaptive and multiscale structure, two quantization schemes are dedicated to project recursively the 3D point clouds into a series of embedded truncated cubic lattices. At each step of the process, the optimal quantization scheme is selected according to a rate-distortion cost in order to achieve the best trade-off between coding rate and geometry distortion, such that the compression flexibility and performance can be greatly improved. Experimental results show the interest of the proposed multi-scale method for lossy compression of geometry

Random Linear Network Coding for 5G Mobile Video Delivery

An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.Comment: Invited paper for Special Issue "Network and Rateless Coding for Video Streaming" - MDPI Informatio

Unconstrained Free-Viewpoint Video Coding

In this paper, we present a coding framework addressing image-space compression for free-viewpoint video. Our framework is based on time-varying 3D point samples which represent real-world objects. The 3D point samples are obtained after a geometrical reconstruction from multiple pre-recorded video sequences and thus allow for arbitrary viewpoints during playback. The encoding of the data is performed as an off-line process and is not time-critical. The decoding however, must support for real-time rendering of the dynamic 3D data. We introduce a compression framework which encodes multiple point attributes like depth and color into progressive streams. The reference data structure is aligned on the original camera input images and thus enables for easy view-dependent decoding. A novel differential coding approach permits random access in constant time throughout the entire data set and thus enables arbitrary viewpoint trajectories in both time and space.Engineering and Applied Science