AI model predicts diverse human movement goals using CVAE

Researchers have developed a new method for predicting diverse human movement goals using a conditional variational autoencoder (CVAE). This approach leverages environmental context and human pose to generate multiple potential future movement goals by sampling from the CVAE's latent space. The method demonstrates generalization capabilities across scenarios in the GTA-IM and PROX datasets, with code made publicly available. AI

IMPACT This research could improve proactive planning for autonomous systems by enabling more accurate prediction of human trajectories and intentions.

RANK_REASON Academic paper detailing a new AI model for predicting human movement. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• conditional variational autoencoder
• CORE Recommender
• CVAE
• DagsHub
• Gotit.pub
• GTA-IM dataset
• Hugging Face
• PROX dataset
• ScienceCast

• paper
• model release

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