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

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.

LN3Diff proposes a 3D VAE that supports both high-quality 3D reconstruction and latent diffusion training. The text-to-3D diffusion model trained on its latent space supports high-quality 3D mesh synthesis within 8 V100-SECONDS.

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.

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.

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

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

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

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.

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