Yushi Lan

I am currently a Ph.D. candidate at MMLab@NTU, Nanyang Technological University, supervised by Prof. Chen Change Loy and working closely with Dr. Daibo. I got my bachelor degree in software engineering from Yepeida Honors College, Beijing Univ of Posts and Tele (BUPT) in 2020.

Email  /  CV  /  Github

profile photo
Research

My interests lie in the intersection of computer vision, computer graphics, and machine learning, particularly in inverse graphics powered by neural rendering, including 3D generative models, shape analysis and 3D avatar, etc.

Gaussian3Diff: 3D Gaussian Diffusion for 3D Full Head Synthesis and Editing
Yushi Lan, Feitong Tan, Di Qiu, Qiangeng Xu Kyle Genova Zeng Huang, Sean Fanello, Rohit Pandey, Thomas Funkhouser, Chen Change Loy, Yinda Zhang
arXiv preprint
project page / arXiv

Gaussian3Diff adopts 3D Gaussians defined in UV space as the underlying 3D representation, which intrinsically support high-quality novel view synthesis, 3DMM-based animation and 3D diffusion for unconditional generation.

Learning Dense Correspondence for NeRF-Based Face Reenactment
Songlin Yang, Wei Wang, Yushi Lan, Xiangyu Fan, Bo Peng, Lei Yang, Jing Dong
AAAI, 2024
project page / arXiv

We propose a novel face reenactment framework, which adopts tri-planes as fundamental NeRF representation and decomposes face tri-planes into three components: canonical tri-planes, identity deformations, and motion.

DeformToon3D: Deformable 3D Toonification from Neural Radiance Fields
Junzhe Zhang*, Yushi Lan*, Shuai Yang, Fangzhou Hong, Quan Wang, Chai Kiat Yeo, Ziwei Liu, Chen Change Loy
ICCV, 2023
project page / arXiv / Code

We propose DeformToon3D, an 3D toonification methods that achieves high-quality geometry and texture stylization under given styles.

E3DGE: Self-Supervised Geometry-Aware Encoder for Style-Based 3D GAN Inversion
Yushi Lan, Xuyi Meng, Shuai Yang, Chen Change Loy, Bo Dai
CVPR, 2023
project page / arXiv / video / Code

We propose E3DGE, an encoder-based 3D GAN inversion framework that yields high-quality shape and texture reconstruction.

EVA3D: Compositional 3D Human Generation from 2D Image Collections
Fangzhou Hong, Zhaoxi Chen, Yushi Lan, Liang Pan, Ziwei Liu
ICLR, 2023, Spotlight
project page / arXiv / video / Code

EVA3D is a high-quality unconditional 3D human generative model that only requires 2D image collections for training.

DDF: Correspondence Distillation from NeRF-Based GAN
Yushi Lan, Chen Change Loy, Bo Dai
IJCV, 2022
project page / arXiv / Springer

We study dense correspondence, which plays a key role in 3D scene understanding but has been ignored in NeRF research. DDF presents a novel way to distill dense NeRF correspondence from pre-trained NeRF GAN unsupervisedly.

Magnifier: Towards Semantic Adversary and Fusion for Person Re-identification
Yushi Lan*, Yuan Liu*, Xinchi Zhou, Maoqing Tian, Xuesen Zhang, Shuai Yi, Hongsheng Li,
BMVC, 2020
arXiv / Code

We propose MagnifierNet, a triple-branch network which accurately mines details from whole to parts in person re-identification (ReID).


Design and source code from Jon Barron's website