Kinematics-Induced Multimodal 3D Human Pose Estimation with Subject-Level Privacy

We introduce HYPERPOSE, a novel 3D human pose estimation framework that performs spatio-temporal reasoning entirely within the Lorentz model of hyperbolic space $\mathbb{H}^d$ to natively preserve the hierarchical tree topology of the human skeleton. Current state-of-the-art pose estimators aim to capture complex joint dynamics by relying on transformers and graph convolutional networks. Since these architectures operate exclusively in Euclidean space which fundamentally mismatches the inherent tree structure of the human body, these methods inevitably suffer from exponential volume distortion and struggle to maintain structural coherence. To this end, we depart from flat spaces and aim to improve geometric fidelity with Hyperbolic Kinematic Phase-Space Attention (HKPSA), natively embedding complex joint relationships without distortion, alongside a multi-scale windowed hyperbolic attention mechanism that efficiently models temporal dynamics in $O(TW)$ complexity. Furthermore, to overcome the well-known instability of training non-Euclidean manifolds, HYPERPOSE introduces a novel Riemannian loss suite and an uncertainty-weighted curriculum, enforcing physical geodesic constraints like bone length and velocity consistency. Extensive evaluations on the Human3.6M and MPI-INF-3DHP datasets demonstrate that HYPERPOSE achieves state-of-the-art structural and temporal coherence, significantly reducing both volume distortion and velocity error, while establishing new state-of-the-art benchmarks in overall positional accuracy.

WiFi Channel State Information (CSI) enables privacy-preserving human pose sensing in camera-denied environments, but existing WiFi-based pose estimators often fail under environment shifts and rely on costly camera-based annotation pipelines that limit scale. We propose WiFi-JEPA, a self-supervised framework that learns CSI-native representations by predicting masked latent embeddings instead of reconstructing raw CSI signals that may contain hardware-specific artifacts. WiFi-JEPA makes three contributions: (i) CSI-specific tokenization and link masking tailored to the CSI tensor over channel, time, and link (C,T,L); masking entire Tx-Rx antenna links forces the model to predict one spatial link view from others, capturing cross-link correlations informative of 3D spatial structure. (ii) A ray-tracing CSI simulation pipeline that generates diverse unlabeled CSI from randomized geometric primitives, providing scalable pre-training data without pose annotations. (iii) State-of-the-art results on Person-in-WiFi-3D: WiFi-JEPA outperforms prior WiFi-CSI baselines on both single- and multi-person 3D pose estimation under the same evaluation protocol. We also show that simulated CSI provides complementary pre-training signal to real CSI, and that four vision-native SSL objectives degrade performance below training from scratch, whereas WiFi-JEPA consistently improves downstream pose estimation.

Device-free 3D human pose estimation using commodity WiFi Channel State Information (CSI) enables privacy-preserving and illumination-robust human sensing, but its deployment is limited by poor cross-environment generalization. Unlike images, CSI measurements do not have a spatially localized correspondence to body parts and are heavily affected by multipath propagation, causing models that regress absolute poses to entangle body structure with environment-specific location cues. Within a single environment this coupling is benign: an end-to-end absolute-pose variant, RePos-D, already achieves state-of-the-art accuracy on Person-in-WiFi-3D (86.9 mm MPJPE, a 3.4% gain over the previous best WiFi method, DT-Pose). Across environments, however, the same model overfits position and suffers significant performance degradation. We therefore propose RePos, a factorized framework that separates root-relative pose estimation from root localization. By preventing absolute-pose supervision from affecting the structure branch, RePos learns environment-invariant pose representations. Specifically, it groups CSI features into body-part-aware latent tokens that skeleton-guided modeling refines into the pose, while a separate amplitude-based network estimates the root position through a differentiable spatial-decomposition module. Under the strict MM-Fi cross-environment protocol, RePos achieves MPJPEs of 254.4-296.1 mm, a 10-21% reduction over existing WiFi-based methods. The improvement remains consistent across activity protocols, leave-one-environment-out splits, and leakage-free few-shot transfer. Analysis of the learned features shows that relative-pose representations remain largely position-agnostic, while root localization retains environment dependence, indicating distinct generalization behavior for structure and localization.