ArticleExperimental neurobiology2026
AVATAR: AI Vision Analysis for Three-dimensional Action in Real-time.
Article in Experimental neurobiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Signaling Connectomics: A Brain-wide Framework for Intercellular Communication.Experimental neurobiology · 2026Article
- Cerebellar tonic inhibition orchestrates the maturation of information processing and motor coordination.Experimental & molecular medicine · 2026Article
- AI-driven decoding of naturalistic behaviors enables tailored detection of depressive-like behavior in mice.Nature communications · 2025Article
- Identifying Neurobehavioral Biomarkers of Anxiety and Treatment Response Using Virtual Reality, Electroencephalography, Magnetic Resonance Imaging, and Related Multimodal Assessments: A Longitudinal Study Protocol.Journal of clinical medicine · 2025Article
- Integrating artificial intelligence and optogenetics for Parkinson's disease diagnosis and therapeutics in male mice.Nature communications · 2025Article
- Egocentric 3D Skeleton Learning in a Deep Neural Network Encodes Obese-like Motion Representations.Experimental neurobiology · 2024Article
- A quantitative analysis of spontaneous alternation behaviors on a Y-maze reveals adverse effects of acute social isolation on spatial working memory.Scientific reports · 2023Article
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8 authors.
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Abstract
Artificial intelligence (AI) provides new opportunities for high-resolution behavioral analysis and automated, human-free experiments. Here we present AVATAR (AI Vision Analysis for Three-dimensional Action in Real-time). This AI-driven system reconstructs 3D mouse motions by detecting key body parts from synchronized multi-view videos and converting into action skeletons. AVATAR achieves near-human accuracy in pose estimation, enables robust extraction of kinematic and postural features, and supports scalable analysis of model animal behaviors. Using these features represented by 3D action skeleton, LSTM-based model reliably classifies freely moving mouse behaviors during various experimental paradigms with low-latency processing (100 ms) enables real-time closed-loop optogenetic stimulation. As a demonstration of generalizability, we applied AVATAR framework to bottom-view predatory hunting paradigm. AVATARnet accurately detected mouse poses and extracted dynamic behavioral features of the mouse. Using AVATARnet-driven dynamic features, an XGBoost-based classifier automated action segmentation annotation during complex predatory chasing behavior. Together, AVATAR provides 3D pose estimation, dynamic quantification, classification, and closed-loop manipulation in real-time.
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