mlsanityData to 14 Oct 2026

Computer Vision-Driven Gesture Recognition: Toward Natural and Intuitive Human-Computer

arXiv 2412.18321 ↗First posted 24 Dec 2024cs.CVcited by 3

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Abstract (arXiv)

This study mainly explores the application of natural gesture recognition based on computer vision in human-computer interaction, aiming to improve the fluency and naturalness of human-computer interaction through gesture recognition technology. In the fields of virtual reality, augmented reality and smart home, traditional input methods have gradually failed to meet the needs of users for interactive experience. As an intuitive and convenient interaction method, gestures have received more and more attention. This paper proposes a gesture recognition method based on a three-dimensional hand skeleton model. By simulating the three-dimensional spatial distribution of hand joints, a simplified hand skeleton structure is constructed. By connecting the palm and each finger joint, a dynamic and static gesture model of the hand is formed, which further improves the accuracy and efficiency of gesture recognition. Experimental results show that this method can effectively recognize various gestures and maintain high recognition accuracy and real-time response capabilities in different environments. In addition, combined with multimodal technologies such as eye tracking, the intelligence level of the gesture recognition system can be further improved, bringing a richer and more intuitive user experience. In the future, with the continuous development of computer vision, deep learning and multimodal interaction technology, natural interaction based on gestures will play an important role in a wider range of application scenarios and promote revolutionary progress in human-computer interaction.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 28 references that are in the map. First posted on arXiv 24 Dec 2024.

Builds on

17 of 28 references.

References · 17

  1. 2024Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Miningcited by 47
  2. 2024Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networkscited by 33
  3. 2024Harnessing LLMs for API Interactions: A Framework for Classification and Synthetic Data Generationcited by 17
  4. 2024Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learningcited by 13
  5. 2024Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Trainingcited by 13
  6. 2024Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issuescited by 12
  7. 2024Efficient and Aesthetic UI Design with a Deep Learning-Based Interface Generation Tree Algorithmcited by 11
  8. 2024LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuningcited by 10
  9. 2024Comprehensive Evaluation of Multimodal AI Models in Medical Imaging Diagnosis: From Data Augmentation to Preference-Based Comparisoncited by 10
  10. 2024Adaptive Cache Management for Complex Storage Systems Using CNN-LSTM-Based Spatiotemporal Predictioncited by 9
  11. 2024Enhancing Few-Shot Learning with Integrated Data and GAN Model Approachescited by 9
  12. 2024An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reductioncited by 9
5 more
  1. 2024Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Datacited by 7
  2. 2024Accurate Medical Named Entity Recognition Through Specialized NLP Modelscited by 7
  3. 2024Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothingcited by 5
  4. 2024Optimizing Multi-Task Learning for Enhanced Performance in Large Language Modelscited by 5
  5. 2024Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNNcited by 2

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

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  3. 2025Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformercited by 5