KGS-GCN: Kinematics-Driven Gaussian Splatting and Probabilistic Topology for Skeleton-Based Action Recognition
arXiv:2603.16943v2 Announce Type: replace-cross Abstract: Skeleton-based action recognition is widely applied in sensor-based systems, including human-computer interaction and intelligent surveillance. However, typical sensors produce sparse and discrete joint coordinates, often leading to the loss of fine-grained spatiotemporal information during dynamic movements. Furthermore, predefined physical topologies restrict modeling potential long-range dependencies. To address these challenges, we propose KGS-GCN, which integrates kinematics-driven Gaussian splatting and probabilistic topology with
Ongoing advancements in artificial intelligence and computer vision research are continuously pushing the boundaries of action recognition capabilities.
Improved action recognition is crucial for enhancing the effectiveness and reliability of sensor-based systems in diverse applications such as security, human-computer interaction, and robotics.
This research introduces a novel method to overcome limitations of sparse sensor data and fixed topologies, potentially leading to more robust and accurate skeleton-based action recognition.
- · AI researchers
- · Surveillance technology providers
- · Human-computer interaction developers
- · Robotics industry
- · Developers relying on traditional, less robust action recognition methods
More accurate and reliable understanding of human movements from sensor data will become possible.
This could lead to more sophisticated and responsive AI systems in security, health monitoring, and automation.
Enhanced action recognition might enable new forms of human-robot collaboration and intelligent autonomous systems in complex environments.
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Read at arXiv cs.AI