Stereoscopic video has long been the subject of research due to its ability to deliver immersive three-dimensional content to a wide range of applications. The dual-view format inherently provides binocular disparity cues that enhance depth perception and realism, making it indispensable for fields such as telepresence, 3D mapping, and robotic vision. Until recently, however, end-to-end pipelines for capturing, encoding, and viewing high-quality stereoscopic video were neither widely accessible nor optimized for consumer-grade devices. Today's smartphones, such as the iPhone Pro, and modern Head-Mounted Displays (HMDs) like the Apple Vision Pro, offer built-in support for stereoscopic video capture, hardware-accelerated encoding, and seamless playback on devices like the Apple Vision Pro and Meta Quest 3, which require minimal user intervention. Apple refers to this streamlined workflow as spatial Video. Making the full stereoscopic video process available to everyone has made new applications possible. Despite these advances, there remains a notable absence of publicly available datasets that include the complete spatial video pipeline on consumer platforms, hindering reproducibility and comparative evaluation of emerging algorithms.

Time-varying meshes (TVMs), characterized by their varying connectivity and number of vertices, hold significant potential in AR/VR applications. However, their practical use is challenging due to their large file sizes and the complexity of time-varying topology. Many time-varying mesh compression methods attempted to exploit redundancy between consecutive meshes to compress TVMs more efficiently, however, most face difficulties in establishing stable vertex and surface correspondence between the frames of a TVM. We propose TVMC, a novel TVM compression method that leverages volume tracking and extracts high-quality reference meshes for inter-frame prediction. Specifically, we use as-rigid-as-possible volume tracking to align consecutive TVMs and track volume centers, followed by multidimensional scaling to refine reference centers. This allows us to precisely deform a group of frames to the reference space and extract the reference mesh which is then deformed to approximate each mesh in the group to get displacement fields for TVM compression. Extensive experiments show that TVMC outperforms state-of-the-art methods (e.g., Google Draco, V-DMC 4.0, etc.), with bitrates of 4-6 Mbps compared to 9--12 Mbps for Draco and 10-15 Mbps for V-DMC 4.0. It reduces the decoding time by 66.1% compared to Draco and enables an increased group of frames (up to 15) without significant distortion.

The proliferation of immersive applications such as telepresence, AR/VR streaming, and 3D digital twins demands efficient capture, compression, and delivery of dynamic 3D scenes. Time-varying meshes (TVMs), sequences of meshes with evolving geometry and topology, are a compact and expressive format for volumetric video, but their high data rates and irregular structure make real-time streaming challenging. This paper presents an overview of the compression of TVMs. The talk also includes two complementary contributions from our group: TVMC, a time-varying mesh compression framework for deformable objects utilizing volume-tracked references, and an extension designed for full, unbounded scene meshes with both static and dynamic parts. Together, these systems demonstrate high compression ratios, low decoding latencies, and robustness to real-world scene complexity, paving the way for scalable, low-bitrate 3D video streaming.