81 citations · 113 across the 13 of their papers we have counts for
6 papers · 1 filter
Generative Spatiotemporal Data Augmentation
Jinfan Zhou, Lixin Luo, Sungmin Eum +2
We explore spatiotemporal data augmentation using video foundation models to diversify both camera viewpoints and scene dynamics. Unlike existing approaches based on simple geometr…
Coarse-to-Fine Hierarchical Alignment for UAV-based Human Detection using Diffusion Models
Wenda Li, Meng Wu, Liangzhao Chen +3
Training object detectors demands extensive, task-specific annotations, yet this requirement becomes impractical in UAV-based human detection due to constantly shifting target dist…
MoRe: Monocular Geometry Refinement via Graph Optimization for Cross-View Consistency
Dongki Jung, Jaehoon Choi, Yonghan Lee +3
Monocular 3D foundation models offer an extensible solution for perception tasks, making them attractive for broader 3D vision applications. In this paper, we propose MoRe, a train…
UAV4D: Dynamic Neural Rendering of Human-Centric UAV Imagery using Gaussian Splatting
Jaehoon Choi, Dongki Jung, Christopher Maxey +4
Despite significant advancements in dynamic neural rendering, existing methods fail to address the unique challenges posed by UAV-captured scenarios, particularly those involving m…
UAVTwin: Neural Digital Twins for UAVs using Gaussian Splatting
Jaehoon Choi, Dongki Jung, Yonghan Lee +3
We present UAVTwin, a method for creating digital twins from real-world environments and facilitating data augmentation for training downstream models embedded in unmanned aerial v…
AutoComPose: Automatic Generation of Pose Transition Descriptions for Composed Pose Retrieval Using Multimodal LLMs
Yi-Ting Shen, Sungmin Eum, Doheon Lee +4
Composed pose retrieval (CPR) enables users to search for human poses by specifying a reference pose and a transition description, but progress in this field is hindered by the sca…