6 papers · 1 filter
Hand-4DGS: Feed-Forward 3D Gaussian Splatting for 4D Hand Reconstruction from Egocentric Videos
Jeongmin Bae, Seoha Kim, Marc Pollefeys +3
Dynamic 3D hand reconstruction from egocentric videos is essential for next-generation computing platforms such as AR/VR and AI glasses. Despite its importance, most prior works fo…
4D Scaffold Gaussian Splatting with Dynamic-Aware Anchor Growing for Efficient and High-Fidelity Dynamic Scene Reconstruction
Woong Oh Cho, In Cho, Seoha Kim +3
Modeling dynamic scenes through 4D Gaussians offers high visual fidelity and fast rendering speeds, but comes with significant storage overhead. Recent approaches mitigate this cos…
Compensating Spatiotemporally Inconsistent Observations for Online Dynamic 3D Gaussian Splatting
Youngsik Yun, Jeongmin Bae, Hyunseung Son +4
Online reconstruction of dynamic scenes is significant as it enables learning scenes from live-streaming video inputs, while existing offline dynamic reconstruction methods rely on…
Rethinking Open-Vocabulary Segmentation of Radiance Fields in 3D Space
Hyunjee Lee, Youngsik Yun, Jeongmin Bae +2
Understanding the 3D semantics of a scene is a fundamental problem for various scenarios such as embodied agents. While NeRFs and 3DGS excel at novel-view synthesis, previous metho…
Sync-NeRF: Generalizing Dynamic NeRFs to Unsynchronized Videos
Seoha Kim, Jeongmin Bae, Youngsik Yun +3
Recent advancements in 4D scene reconstruction using neural radiance fields (NeRF) have demonstrated the ability to represent dynamic scenes from multi-view videos. However, they f…
Per-Gaussian Embedding-Based Deformation for Deformable 3D Gaussian Splatting
Jeongmin Bae, Seoha Kim, Youngsik Yun +3
As 3D Gaussian Splatting (3DGS) provides fast and high-quality novel view synthesis, it is a natural extension to deform a canonical 3DGS to multiple frames for representing a dyna…