With the advancements in methods for capturing 3D object motion, motion capture (MoCap) data are starting to be used beyond their traditional realm of animation and gaming in areas such as the arts, rehabilitation, automotive industry, remote interactions, and so on. As the amount of MoCap data increase, compression becomes crucial for further expansion and adoption of these technologies. In this paper, we extend our previous work on low-delay MoCap data compression by introducing two improvements. The first improvement is the bit allocation to long-term and short-term reference MoCap frames, which provides a 10–15% reduction in coded bit rate at the same quality. The second improvement is the post-processing in the form of motion-adaptive temporal low-pass filtering, which is able to provide another 9–13% savings in the bit rate. The experimental results also indicate that the proposed online MoCap codec is competitive with several state-of-the-art offline codecs. Overall, the proposed techniques integrate into a highly effective online MoCap codec that is suitable for low-delay applications, whose implementation is provided alongside this paper to aid further research in the field.

Geometric representation of objects and surfaces in terms of 3D meshes is becoming increasingly important in a variety of applications. As the amount of data in this format increases, the problem of compression becomes vital for the further development of the field. In this paper we present a codec for dynamic 3D mesh data that utilizes the “hybrid” framework from video coding, built upon temporal prediction and spatial transform. We discuss various features of the codec, including unrestricted quantization and two-stage entropy coding, and investigate its compression efficiency on a variety of test material. A discussion of various prediction structures and their impact on error resilience is also provided.

Motion Capture (MoCap) technology is becoming increasingly popular in gaming, entertainment, and multimedia industries. Interactive systems usingMoCap technology require low-delay MoCap data compression. In this paper, we extend previous work on low-delay MoCap compression by introducing several useful features, such as unrestricted quantization, more efficient entropy coding, as well as encoder rate control. Experimental results show that the proposed rate control provides better than 99% accuracy in controlling encoder's output bitrate. At the same time, improvements in quantization and entropy coding provide over 20% reduction in bit rate for the same reconstruction quality, compared to the current state of the art.

Motion Capture (MoCap) is becoming important in many areas of technology, science, and art, including graphics, visualization, gaming, and medical applications. In parallel with its increased use and abundance, compression of this kind of data is becoming more important. In this paper, we propose a hybrid low-delay compression scheme for MoCap data that is particularly suitable for interactive applications such as online gaming or telemedicine. Experimental results confirm the superiority of the proposed approach against state-of-the-art methods for MoCap compression in both compression efficiency and delay, making it suitable for interactive applications.

Motion Capture (MoCap) is becoming important in many areas of technology, science, and art, including graphics, visualization, gaming, and medical applications. MoCap data streaming faces many challenges found in other kinds of media communications, an important one being the degradation of decoded data quality due to packet loss. In this paper, we propose several strategies for error concealment of MoCap data based on the principles of mechanics. In particular, forward, backward and bi-directional error concealment strategies are presented. Experimental results verify the effectiveness of the proposed methods and demonstrate the advantage of bi-directional error concealment over forward and backward approaches.