New Trends in Computer Vision for Activity Recognition and 3D Human Pose Understanding

Dear Colleagues,

Activity recognition and 3D human pose estimation are two rapidly evolving fields in computer vision and artificial intelligence. This Special Issue aims to showcase recent advances and emerging trends in these areas, with an emphasis on technical innovation, diverse application scenarios, and pressing academic challenges. We invite the submission of original research that bridges cutting-edge deep learning algorithms with real-world use cases, security, virtual reality, human–computer interaction, healthcare, and more. This Special Issue aims to collate emerging strategies such as efficient lightweight models and privacy-aware solutions, managing data quality issues across sensing modalities, ensuring model generalizability across diverse contexts, and accurately interpreting subtle human gestures and movements, with the goal of fostering interdisciplinary collaboration and driving progress in human-centric vision research.

In recent years, we have seen significant advancements in the use of deep learning techniques for activity recognition and 3D pose estimation. Modern approaches leverage sophisticated network architectures and training strategies to improve accuracy, efficiency, and robustness.

Topics of interest for this Special Issue include, but are not limited to, the following:

• Deep Learning and Network Architectures;
• Multimodal and Multisensor Fusion;
• Lightweight and Efficient Models;
• Privacy-Preserving and Ethical AI;
• Self-Supervised and Generative Learning;
• Physics-Based and Hybrid Models;
• Practical Applications in Health, Surveillance, and Ambient Intelligence.

Dr. Ghazal Rouhafzay
Prof. Dr. Pierre Payeur
Dr. Ana-Maria Cretu
Dr. Éric Hervet
Guest Editors

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