activity
20182022
most citedRecurrent Embedding Aggregation Network for Video Face Recognition

16 citations · 54 across the 6 of their papers we have counts for

collaborators

12 papers

cs.CV2022

AvatarGen: A 3D Generative Model for Animatable Human Avatars

Jianfeng Zhang, Zihang Jiang, Dingdong Yang +6

Unsupervised generation of 3D-aware clothed humans with various appearances and controllable geometries is important for creating virtual human avatars and other AR/VR applications…

cs.CV202213 cited

IDE-3D: Interactive Disentangled Editing for High-Resolution 3D-aware Portrait Synthesis

Jingxiang Sun, Xuan Wang, Yichun Shi +3

Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution or high-quality ones with no editi…

cs.CV20201 cited

Lifting 2D StyleGAN for 3D-Aware Face Generation

Yichun Shi, Divyansh Aggarwal, Anil K. Jain

We propose a framework, called LiftedGAN, that disentangles and lifts a pre-trained StyleGAN2 for 3D-aware face generation. Our model is "3D-aware" in the sense that it is able to…

cs.CV2020

Boosting Unconstrained Face Recognition with Auxiliary Unlabeled Data

Yichun Shi, Anil K. Jain

In recent years, significant progress has been made in face recognition, which can be partially attributed to the availability of large-scale labeled face datasets. However, since…

cs.CV202014 cited

Towards Universal Representation Learning for Deep Face Recognition

Yichun Shi, Xiang Yu, Kihyuk Sohn +2

Recognizing wild faces is extremely hard as they appear with all kinds of variations. Traditional methods either train with specifically annotated variation data from target domain…

cs.CV201916 cited

Recurrent Embedding Aggregation Network for Video Face Recognition

Sixue Gong, Yichun Shi, Anil K. Jain

Recurrent networks have been successful in analyzing temporal data and have been widely used for video analysis. However, for video face recognition, where the base CNNs trained on…