collaborators

6 papers

cs.CV2025

Dynamic-eDiTor: Training-Free Text-Driven 4D Scene Editing with Multimodal Diffusion Transformer

Dong In Lee, Hyungjun Doh, Seunggeun Chi +3

Recent progress in 4D representations, such as Dynamic NeRF and 4D Gaussian Splatting (4DGS), has enabled dynamic 4D scene reconstruction. However, text-driven 4D scene editing rem…

cs.CV2025

Occlusion-Aware Temporally Consistent Amodal Completion for 3D Human-Object Interaction Reconstruction

Hyungjun Doh, Dong In Lee, Seunggeun Chi +4

We introduce a novel framework for reconstructing dynamic human-object interactions from monocular video that overcomes challenges associated with occlusions and temporal inconsist…

cs.CV2025

Contact-Aware Amodal Completion for Human-Object Interaction via Multi-Regional Inpainting

Seunggeun Chi, Enna Sachdeva, Pin-Hao Huang +1

Amodal completion, which is the process of inferring the full appearance of objects despite partial occlusions, is crucial for understanding complex human-object interactions (HOI)…

cs.CV2025

CATSplat: Context-Aware Transformer with Spatial Guidance for Generalizable 3D Gaussian Splatting from A Single-View Image

Wonseok Roh, Hwanhee Jung, Jong Wook Kim +6

Recently, generalizable feed-forward methods based on 3D Gaussian Splatting have gained significant attention for their potential to reconstruct 3D scenes using finite resources. T…

cs.CV2024

Estimating Ego-Body Pose from Doubly Sparse Egocentric Video Data

Seunggeun Chi, Pin-Hao Huang, Enna Sachdeva +3

We study the problem of estimating the body movements of a camera wearer from egocentric videos. Current methods for ego-body pose estimation rely on temporally dense sensor data,…

cs.CV2024

M2D2M: Multi-Motion Generation from Text with Discrete Diffusion Models

Seunggeun Chi, Hyung-gun Chi, Hengbo Ma +4

We introduce the Multi-Motion Discrete Diffusion Models (M2D2M), a novel approach for human motion generation from textual descriptions of multiple actions, utilizing the strengths…