I am Yiqian Wu (吴奕谦), a fourth-year CS Ph.D. candidate at the State Key Laboratory of CAD&CG, Zhejiang University, where I have been conducting research under the supervision of Prof. Xiaogang Jin since 2021. From May 2024 to April 2025, I was an academic guest at ETH Zurich, collaborating with Prof. Siyu Tang. I received my B.S. degree in Computer Science and Technology from the Chu Kochen College at Zhejiang University in 2021.
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Zhejiang University
State Key Lab of CAD&CG
Ph.D. StudentSep. 2021 - present -
Zhejiang University
B.S. in Computer ScienceSep. 2017 - Jun. 2021
Experience
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ETH Zürich
Computer Vision and Learning Group (VLG)
Academic GuestMay. 2024 - April. 2025
News
Research Overview
My current research focuses on avatar editing and generation. I have explored 2D GAN-based portrait editing, developed datasets and novel architectures for 3D-aware face GANs, and worked on text-guided 3D and 4D avatar generation using diffusion models.
Below is an overview that showcases all of my papers through a single identity!
Selected Recent Publications
(view all )
Text-based Animatable 3D Avatars with Morphable Model Alignment
Yiqian Wu, Malte Prinzler, Xiaogang Jin, Siyu Tang
ACM SIGGRAPH (Conference Track) 2025
A novel framework, AnimPortrait3D, for text-based realistic animatable 3DGS avatar generation with morphable model alignment.
Text-based Animatable 3D Avatars with Morphable Model Alignment
Yiqian Wu, Malte Prinzler, Xiaogang Jin, Siyu Tang
ACM SIGGRAPH (Conference Track) 2025
A novel framework, AnimPortrait3D, for text-based realistic animatable 3DGS avatar generation with morphable model alignment.
Portrait3D: Text-Guided High-Quality 3D Portrait Generation Using Pyramid Representation and GANs Prior
Yiqian Wu, Hao Xu, Xiangjun Tang, Xien Chen, Siyu Tang, Zhebin Zhang, Chen Li, Xiaogang Jin
ACM Transactions on Graphics (Proceedings of SIGGRAPH) 2024
A novel neural rendering-based framework with a novel joint geometry-appearance prior to achieve text-to-3D-portrait generation.
Portrait3D: Text-Guided High-Quality 3D Portrait Generation Using Pyramid Representation and GANs Prior
Yiqian Wu, Hao Xu, Xiangjun Tang, Xien Chen, Siyu Tang, Zhebin Zhang, Chen Li, Xiaogang Jin
ACM Transactions on Graphics (Proceedings of SIGGRAPH) 2024
A novel neural rendering-based framework with a novel joint geometry-appearance prior to achieve text-to-3D-portrait generation.
LPFF: A Portrait Dataset for Face Generators Across Large Poses
Yiqian Wu, Jing Zhang, Hongbo Fu, Xiaogang Jin
IEEE/CVF International Conference on Computer Vision (ICCV) 2023
A large-pose Flickr face dataset comprised of 19,590 high-quality real large-pose portrait images.
LPFF: A Portrait Dataset for Face Generators Across Large Poses
Yiqian Wu, Jing Zhang, Hongbo Fu, Xiaogang Jin
IEEE/CVF International Conference on Computer Vision (ICCV) 2023
A large-pose Flickr face dataset comprised of 19,590 high-quality real large-pose portrait images.
3DPortraitGAN: Learning Canonical Full-Head 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses
Yiqian Wu, Hao Xu, Xiangjun Tang, Hongbo Fu, Xiaogang Jin
Under review 2023
The first 3D-aware one-quarter headshot portrait generator that learns a canonical 3D avatar distribution from the 360°PHQ dataset with body pose self-learning.
3DPortraitGAN: Learning Canonical Full-Head 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses
Yiqian Wu, Hao Xu, Xiangjun Tang, Hongbo Fu, Xiaogang Jin
Under review 2023
The first 3D-aware one-quarter headshot portrait generator that learns a canonical 3D avatar distribution from the 360°PHQ dataset with body pose self-learning.
HairMapper: Removing Hair from Portraits Using GANs
Yiqian Wu, Yongliang Yang, Xiaogang Jin
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022
A dataset and a baseline method for removing hair from portrait images using generative adversarial networks (GANs).
HairMapper: Removing Hair from Portraits Using GANs
Yiqian Wu, Yongliang Yang, Xiaogang Jin
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022
A dataset and a baseline method for removing hair from portrait images using generative adversarial networks (GANs).
Coarse-to-Fine: Facial Structure Editing of Portrait Images via Latent Space Classifications
Yiqian Wu, Yongliang Yang, Qinjie Xiao, Xiaogang Jin
ACM Transactions on Graphics (Proceedings of SIGGRAPH) 2021
A novel method that can automatically remove the double chin effect in portrait images.
Coarse-to-Fine: Facial Structure Editing of Portrait Images via Latent Space Classifications
Yiqian Wu, Yongliang Yang, Qinjie Xiao, Xiaogang Jin
ACM Transactions on Graphics (Proceedings of SIGGRAPH) 2021
A novel method that can automatically remove the double chin effect in portrait images.