3D Body Twin: Improving Human Gait Visualizations Using Personalized Avatars - Zieger, Daniel; Güthlein, Florian; Henningson, Jann-Ole; Jakob, Verena; Gaßner, Heiko; Shanbhag, Julian; Fleischmann, Sophie; Miehling, Jörg; Wartzack, Sandro; Eskofier, Björn; Winkler, Jürgen; Egger, Bernhard; et al. (ShapeMI 2024, 2024)
We propose an interactive visualization and reconstruction system for gait pattern analysis that creates a more realistic visualization of the patient than contemporary stick figure-like representations. It supports thorough gait pattern analysis, and enables unbiased inter- and intra-patient comparisons as well as longitudinal studies. Our system takes as input 3D motion capture data, but can be further constrained using readily available metadata or layman-accessible measurements. A statistical shape model is then fitted to the motion capture and personal metadata. The result is an immediate and interactive visualization of an animated 3D twin. Our system handles different marker setups and can thus be applied to already existing data. We further show that we can infer realistic body models with only a few of markers. A survey with medical experts confirms the clinical applicability of our method. <br

END-TO-END LEARNED LOSSY DYNAMIC POINT CLOUD ATTRIBUTE COMPRESSION - Nguyen, Dat Thanh; Zieger, Daniel; Stamminger, Marc; Kaup, André (Proceedings - International Conference on Image Processing, ICIP, 2024)
Recent advancements in point cloud compression have primarily emphasized geometry compression while comparatively fewer efforts have been dedicated to attribute compression. This study introduces an end-to-end learned dynamic lossy attribute coding approach, utilizing an efficient high-dimensional convolution to capture extensive inter-point dependencies. This enables the efficient projection of attribute features into latent variables. Subsequently, we employ a context model that leverage previous latent space in conjunction with an auto-regressive context model for encoding the latent tensor into a bitstream. Evaluation of our method on widely utilized point cloud datasets from the MPEG and Microsoft demonstrates its superior performance compared to the core attribute compression module Region-Adaptive Hierarchical Transform method from MPEG Geometry Point Cloud Compression with 38.1% Bjontegaard Delta-rate saving in average while ensuring a low-complexity encoding/decoding.
CRIS-Team Scopus Importer:2025-04-01
Keywords: Deep Learning; Dynamic Point Cloud; G-PCC; RAHT

Multiresolution point cloud compression for real-time visualization and streaming of large 3D datasets - Nguyen, Dat Thanh; Zieger, Daniel; Gambietz, Markus; Koelewijn, Anne; Stamminger, Marc; Kaup, André (Asilomar Conferense on Signals, Systems, and Computers, 2024)