Considering that human has specific characteristics, including the structural semantics of human body and the dynamics of human motions, we propose a unified framework to make full use of the prior knowledge and explore the inherent features in the data itself for generalized human-centric point cloud video understanding.

We present a large-scale multi-modal dataset for human-centric scene understanding, dubbed HuCenLife, which is collected in diverse daily-life scenarios with rich and fine-grained annotations, with benchmarks in 3D perception tasks, such as segmentation, detection, action recognition, etc.

We propose a monocular camera and single LiDAR-based method for 3D multi-person pose estimation in large-scale scenes, with an effective fusion strategy to take advantage of multi-modal input data and make full use of temporal information to guide the network to learn natural and coherent human motions in a weak supervised paradigm.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

第三届中国三维视觉大会(China3DV 2024)由中国图象图形学学会(CSIG)主办，CSIG三维视觉专委会与哈尔滨工业大学（深圳）承办，是国内三维视觉领域最高规格的学术研讨盛会。每年仅有100篇工作被China3DV接收。

A low-cost and highly accurate product developed based on copy number features for predicting the presence of homologous recombination defect features in pan cancer patients.