5 papers
Towards Trustworthy Hypergraph Neural Networks under Label Noise
Mengyao Zhou, Zhiheng Zhou, Xiao Han +1
Hypergraph neural networks (HGNNs) have demonstrated remarkable capabilities in processing complex higher-order relationships. However, their performance is highly dependent on lab…
Tackling Over-smoothing on Hypergraphs: A Ricci Flow-guided Neural Diffusion Approach
Mengyao Zhou, Zhiheng Zhou, Xiao Han +3
Hypergraph neural networks (HGNNs) have demonstrated strong capabilities in modeling complex higher-order relationships. However, existing HGNNs often suffer from over-smoothing as…
CAGE-GS: High-fidelity Cage Based 3D Gaussian Splatting Deformation
Yifei Tong, Runze Tian, Xiao Han +3
As 3D Gaussian Splatting (3DGS) gains popularity as a 3D representation of real scenes, enabling user-friendly deformation to create novel scenes while preserving fine details from…
ARAP-GS: Drag-driven As-Rigid-As-Possible 3D Gaussian Splatting Editing with Diffusion Prior
Xiao Han, Runze Tian, Yifei Tong +3
Drag-driven editing has become popular among designers for its ability to modify complex geometric structures through simple and intuitive manipulation, allowing users to adjust an…
GSEditPro: 3D Gaussian Splatting Editing with Attention-based Progressive Localization
Yanhao Sun, RunZe Tian, Xiao Han +3
With the emergence of large-scale Text-to-Image(T2I) models and implicit 3D representations like Neural Radiance Fields (NeRF), many text-driven generative editing methods based on…