Autonomous Tracking For Volumetric Video Sequences

3rd December 2020

We present a robust, autonomous method for tracking volumetric sequences which can detect missing geometry and propagate user edits. Pictured left to right are step-by-step visualizations of the process. The input to our system is a temporally incoherent and noisy sequence of meshes. We perform pairwise registration using abstraction layers, volumetric segmentation and a keyframing system which allows for user edits, e.g. the hand recovered in red. We establish correspondences which maintain edits and propagate geometry throughout a graph-based deformation process.


As a rapidly growing medium, volumetric video is gaining attention beyond academia, reaching industry and creative communities alike. This brings new challenges to reduce the barrier to entry from a technical and economical point of view. We present a system for robustly and autonomously performing temporally coherent tracking for volumetric sequences, specifically targeting those from sparse setups or with noisy output. Our system will detect and recover missing pertinent geometry across highly incoherent sequences as well as provide users the option of propagating drastic topology edits. In this way, affordable multi-view setups can leverage temporal consistency to reduce processing and compression overheads while also generating more aesthetically pleasing volumetric sequences.


Autonomous Tracking For Volumetric Video Sequences, Matthew Moynihan, Susana Ruano, Rafael Pagés and Aljosa Smolic, WACV 2021


Available on GitHub