Yushi Lan

I am currently a 4th-year 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

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.

	GaussianAnything: Interactive Point Cloud Latent Diffusion for 3D Generation Yushi Lan, Shangchen Zhou, Zhaoyang Lyu, Fangzhou Hong, Shuai Yang, Bo Dai, Xingang Pan, Chen Change Loy preprint, 2024 project page / arXiv / Code GaussianAnything generates high-quality and editable surfel Gaussians through a cascaded 3D diffusion pipeline, given single-view images or texts as the conditions.
	MVDrag3D: Drag-based Creative 3D Editing via Multi-view Generation-Reconstruction Priors Honghua Chen, Yushi Lan, Yongwei Chen, Yifan Zhou, Xingang Pan, preprint, 2024 project page / arXiv / Code MvDrag3D provide a precise, generative, and flexible solution for 3D drag-based editing.
	3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion Zhaoxi Chen, Jiaxiang Tang, Yuhao Dong, Ziang Cao, Fangzhou Hong, Yushi Lan, Tengfei Wang, Haozhe Xie, Tong Wu, Shunsuke Saito, Liang Pan, Dahua Lin, Ziwei Liu, preprint, 2024 project page / arXiv / Code 3DTopia-XL scales high-quality 3D asset generation using Diffusion Transformer (DiT) built upon an expressive and efficient 3D representation, PrimX.
	LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation Yushi Lan, Fangzhou Hong, Shuai Yang, Shangchen Zhou, Xuyi Meng, Bo Dai, Xingang Pan, Chen Change Loy ECCV, 2024 project page / arXiv / Code LN3Diff is a native 3D diffusion model that creates high-quality 3D object mesh from image or text within 8 seconds.
	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 ECCV, 2024 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