11 papers · 1 filter
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…
MeshGS: Adaptive Mesh-Aligned Gaussian Splatting for High-Quality Rendering
Jaehoon Choi, Yonghan Lee, Hyungtae Lee +2
Recently, 3D Gaussian splatting has gained attention for its capability to generate high-fidelity rendering results. At the same time, most applications such as games, animation, a…
Exploring the Potential of Synthetic Data to Replace Real Data
Hyungtae Lee, Yan Zhang, Heesung Kwon +1
The potential of synthetic data to replace real data creates a huge demand for synthetic data in data-hungry AI. This potential is even greater when synthetic data is used for trai…