17 citations · 38 across the 8 of their papers we have counts for
8 papers
NeRF-MIR: Towards High-Quality Restoration of Masked Images with Neural Radiance Fields
Xianliang Huang, Zhizhou Zhong, Shuhang Chen +3
Neural Radiance Fields (NeRF) have demonstrated remarkable performance in novel view synthesis. However, there is much improvement room on restoring 3D scenes based on NeRF from co…
IDDR-NGP: Incorporating Detectors for Distractor Removal with Instant Neural Radiance Field
Xianliang Huang, Jiajie Gou, Shuhang Chen +3
This paper presents the first unified distractor removal method, named IDDR-NGP, which directly operates on Instant-NPG. The method is able to remove a wide range of distractors in…
ImmerIris: A Large-Scale Dataset and Benchmark for Off-Axis and Unconstrained Iris Recognition in Immersive Applications
Yuxi Mi, Qiuyang Yuan, Zhizhou Zhong +5
Recently, iris recognition is regaining prominence in immersive applications such as extended reality as a means of seamless user identification. This application scenario introduc…
Data Synthesis with Diverse Styles for Face Recognition via 3DMM-Guided Diffusion
Yuxi Mi, Zhizhou Zhong, Yuge Huang +7
Identity-preserving face synthesis aims to generate synthetic face images of virtual subjects that can substitute real-world data for training face recognition models. While prior…
Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +56
Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including…
SlerpFace: Face Template Protection via Spherical Linear Interpolation
Zhizhou Zhong, Yuxi Mi, Yuge Huang +7
Contemporary face recognition systems use feature templates extracted from face images to identify persons. To enhance privacy, face template protection techniques are widely emplo…
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +55
Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class v…
Privacy-Preserving Face Recognition Using Trainable Feature Subtraction
Yuxi Mi, Zhizhou Zhong, Yuge Huang +6
The widespread adoption of face recognition has led to increasing privacy concerns, as unauthorized access to face images can expose sensitive personal information. This paper expl…