MoViD: View-Invariant 3D Human Pose Estimation via Motion-View Disentanglement
3D human pose estimation is a key enabling technology for applications such as healthcare monitoring, human-robot collaboration, and immersive gaming, but real-world deployment remains challenged by viewpoint variations. Existing methods struggle to generalize to unseen camera viewpoints, require large amounts of training data, and suffer from high inference latency. We propose MoViD, a viewpoint-invariant 3D human pose estimation framework that disentangles viewpoint information from motion fea
Record details
Published: 29 March 2026
Source: arXiv
Category: Research
Topics: Healthcare · Agents & autonomy
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses
arXiv · 28 March 2026
FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocation
arXiv · 30 March 2026
Navigating the Mirage: A Dual-Path Agentic Framework for Robust Misleading Chart Question Answering
arXiv · 30 March 2026
Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments
arXiv · 31 March 2026
AD-CARE: A Guideline-grounded, Modality-agnostic LLM Agent for Real-world Alzheimer's Disease Diagnosis with Multi-cohort Assessment, Fairness Analysis, and Reader Study
arXiv · 26 March 2026
MolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimization
arXiv · 2 April 2026
How to cite this record
ethics.ai (29 March 2026), “MoViD: View-Invariant 3D Human Pose Estimation via Motion-View Disentanglement,” evidence record 6623, https://ethics.ai/record/6623 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.