KHMP: Frequency-Domain Kalman Refinement for High-Fidelity Human Motion Prediction

• URL: http://arxiv.org/abs/2603.21327v1
• Date: Sun, 22 Mar 2026 17:10:43 GMT
• Title: KHMP: Frequency-Domain Kalman Refinement for High-Fidelity Human Motion Prediction
• Abstract summary: We introduce KHMP, a novel framework featuring an adaptiveKalman filter applied in the DCT domain to generate high-fidelity human motion predictions.<n>We show that KHMP achieves state-of-the-art accuracy, effectively mitigating jitter artifacts to produce smooth and physically plausible motions.
• Score: 30.827921105686244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stochastic human motion prediction aims to generate diverse, plausible futures from observed sequences. Despite advances in generative modeling, existing methods often produce predictions corrupted by high-frequency jitter and temporal discontinuities. To address these challenges, we introduce KHMP, a novel framework featuring an adaptiveKalman filter applied in the DCT domain to generate high-fidelity human motion predictions. By treating high-frequency DCT coefficients as a frequency-indexed noisy signal, the Kalman filter recursively suppresses noise while preserving motion details. Notably, its noise parameters are dynamically adjusted based on estimated Signal-to-Noise Ratio (SNR), enabling aggressive denoising for jittery predictions and conservative filtering for clean motions. This refinement is complemented by training-time physical constraints (temporal smoothness and joint angle limits) that encode biomechanical principles into the generative model. Together, these innovations establish a new paradigm integrating adaptive signal processing with physics-informed learning. Experiments on the Human3.6M and HumanEva-I datasets demonstrate that KHMP achieves state-of-the-art accuracy, effectively mitigating jitter artifacts to produce smooth and physically plausible motions.

Related papers

• PedestrianDiffusion: Multimodal Generative Denoising and Dense State Estimation for Inertial Navigation [1.8620637029128544]
  PedestrianDiffusion is a generative framework that reformulates dense 6D state estimation as a continuous conditional denoising process.<n>It achieves state-of-the-art performance, exhibiting unprecedented robustness to impulse perturbations and coupled 6D kinematic drift.<n>This work provides a rigorous algorithmic blueprint for next-generation Neural Inertial Measurement Units (N-IMUs)
  arXiv  Detail & Related papers  (2026-07-03T14:04:30Z)
• Hierarchical Policy Learning via Spectral Decomposition [58.65543693788772]
  We identify a semantic decomposition in robot action sequences, separating task-level motion intent from execution-level refinements.<n>Motivated by this structure, we propose Causal Spectral Policy (CSP), which models action generation as a causal coarse-to-fine process.<n>CSP consistently outperforms strong baselines on precision-sensitive manipulation tasks.
  arXiv  Detail & Related papers  (2026-06-28T19:22:40Z)
• Noise-Adaptive Predictive Dynamical Decoupling [0.0]
  We develop a noise-adaptive dynamical decoupling framework that combines analytical open-quantum-system modeling with machine-learning-based forecasting.<n>We investigate stationary and non-stationary environments spanning both Markovian and non-Markovian regimes.
  arXiv  Detail & Related papers  (2026-06-14T12:09:18Z)
• Real-Time Learning of Predictive Dynamic Obstacle Models for Robotic Motion Planning [7.819710421921815]
  We develop an online framework to learn, in real-time, a nonlinear predictive model of another agent's motions.<n>A Cadzow projection enforces structured low-rank consistency, yielding a denoised trajectory and local noise variance estimates.<n>We validate the approach in simulation under Gaussian and heavy-tailed noise, and experimentally on a dynamic crane testbed.
  arXiv  Detail & Related papers  (2025-11-02T05:54:30Z)
• Mitigating the Noise Shift for Denoising Generative Models via Noise Awareness Guidance [54.88271057438763]
  Noise Awareness Guidance (NAG) is a correction method that explicitly steers sampling trajectories to remain consistent with the pre-defined noise schedule.<n>NAG consistently mitigates noise shift and substantially improves the generation quality of mainstream diffusion models.
  arXiv  Detail & Related papers  (2025-10-14T13:31:34Z)
• A Noise Resilient Approach for Robust Hurst Exponent Estimation [0.0]
  A key property is self-similarity, quantified by the Hurst (H)<n>Wavelet-based methods are effective for estimating H due to their multi-scale analysis capability, but additive noise in real-world measurements often degrades accuracy.<n>We propose Noise-Controlled ALPHEE, incorporating noise mitigation and generating multiple level-pairwise estimates from signal energy pairs.
  arXiv  Detail & Related papers  (2025-10-06T13:54:23Z)
• PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement [63.007237197267834]
  Existing deep learning methods are mostly physiological monitoring and lack theoretical robustness.<n>We propose a physics-informed r paradigm derived from the Navier-Stokes equations of hemodynamics, showing that the pulse signal follows a second-order system.<n>This provides a theoretical justification for using a Temporal Conal Network (TCN)<n>Phase-Net achieves state-of-the-art performance with strong efficiency, offering a theoretically grounded and deployment-ready r solution.
  arXiv  Detail & Related papers  (2025-09-29T14:36:45Z)
• RAPID Quantum Detection and Demodulation of Covert Communications: Breaking the Noise Limit with Solid-State Spin Sensors [5.246986428523558]
  We introduce a comprehensive framework for the detection and demodulation of covert electromagnetic signals using solid-state spin sensors.<n>Our approach, named RAPID, is a two-stage hybrid strategy that leverages nitrogen-vacancy centers to operate below the classical noise floor.<n>This work establishes a theoretically rigorous and practically viable pathway for deploying quantum sensors in security-critical applications such as electronic warfare and covert surveillance.
  arXiv  Detail & Related papers  (2025-09-09T22:12:28Z)
• Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
  We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
  arXiv  Detail & Related papers  (2025-07-15T17:57:48Z)
• Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing [0.0]
  This paper presents a novel Reservoir Computing (RC) method for noise filtering and reconstructing unobserved nonlinear dynamics.<n>The performance of the RC in terms of noise intensity, noise frequency content, and drastic shifts in dynamical parameters is studied.<n>The framework yields competitive accuracy at low signal-to-noise ratios and high-frequency ranges.
  arXiv  Detail & Related papers  (2025-04-17T21:47:13Z)
• KFD-NeRF: Rethinking Dynamic NeRF with Kalman Filter [49.85369344101118]
  We introduce KFD-NeRF, a novel dynamic neural radiance field integrated with an efficient and high-quality motion reconstruction framework based on Kalman filtering. Our key idea is to model the dynamic radiance field as a dynamic system whose temporally varying states are estimated based on two sources of knowledge: observations and predictions. Our KFD-NeRF demonstrates similar or even superior performance within comparable computational time and state-of-the-art view synthesis performance with thorough training.
  arXiv  Detail & Related papers  (2024-07-18T05:48:24Z)
• A Noise-robust Multi-head Attention Mechanism for Formation Resistivity Prediction: Frequency Aware LSTM [7.466740264582114]
  The prediction of formation resistivity plays a crucial role in the evaluation of oil and gas reservoirs. Traditional well logging techniques fail to measure accurate resistivity in cased boreholes. A frequency-aware framework and temporal anti-noise block are proposed to build frequency aware LSTM.
  arXiv  Detail & Related papers  (2024-06-06T08:31:52Z)
• Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization [58.720142291102135]
  Recent has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of chaotic dynamical systems. In the absence of mitigating techniques, this technique can result in artificially rapid error growth, leading to inaccurate predictions and/or climate instability. We introduce Linearized Multi-Noise Training (LMNT), a regularization technique that deterministically approximates the effect of many small, independent noise realizations added to the model input during training.
  arXiv  Detail & Related papers  (2022-11-09T23:40:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.