9 papers · 1 filter
EvBS: Event-guided Blur Synthesis for Domain-adaptive Motion Deblurring
Junsik Jung, Seokryun Choi, Yoonki Cho +3
Motion deblurring has achieved remarkable progress with deep learning, yet pre-trained deblurring models often suffer from performance degradation in real-world scenarios due to th…
RoME: Robust Mixture of Low-Rank Experts against Multiple Adversarial Perturbations
Woo Jae Kim, Kyle Min, Suhyeon Ha +2
Multi-perturbation adversarial training (MAT) aims to achieve robustness against multiple perturbations but suffers from robustness trade-offs between different threats. T…
Radiometrically Consistent Gaussian Surfels for Inverse Rendering
Kyu Beom Han, Jaeyoon Kim, Woo Jae Kim +2
Inverse rendering with Gaussian Splatting has advanced rapidly, but accurately disentangling material properties from complex global illumination effects, particularly indirect ill…
No Caption, No Problem: Caption-Free Membership Inference via Model-Fitted Embeddings
Joonsung Jeon, Woo Jae Kim, Suhyeon Ha +2
Latent diffusion models have achieved remarkable success in high-fidelity text-to-image generation, but their tendency to memorize training data raises critical privacy and intelle…
AegisRF: Adversarial Perturbations Guided with Sensitivity for Protecting Intellectual Property of Neural Radiance Fields
Woo Jae Kim, Kyu Beom Han, Yoonki Cho +4
As Neural Radiance Fields (NeRFs) have emerged as a powerful tool for 3D scene representation and novel view synthesis, protecting their intellectual property (IP) from unauthorize…
Pose-free 3D Gaussian splatting via shape-ray estimation
Youngju Na, Taeyeon Kim, Jumin Lee +3
While generalizable 3D Gaussian splatting enables efficient, high-quality rendering of unseen scenes, it heavily depends on precise camera poses for accurate geometry. In real-worl…