Portrait de Zhen Liu n'est pas disponible

Zhen Liu

liuzhen@mila.quebec
Doctorat - Université de Montréal
Superviseur⋅e principal⋅e
Liam Paull
Co-superviseur⋅e
Yoshua Bengio

Billets de blogue

Visuel de l'Article sur la représentation du maillage non étanche de t-shirts
15 mai 2024
Comment représenter efficacement le maillage non étanche de t-shirts?
par
Zhen Liu
Yao Feng
Yuliang Xiu
Weiyang Liu
Liam Paull
Michael J. Black
Bernhard Scholkopf
Lire l'article

Publications

Ghost on the Shell: An Expressive Representation of General 3D Shapes
Zhen Liu
Yao Feng
Yuliang Xiu
Weiyang Liu
Liam Paull
Michael J. Black
Bernhard Schölkopf
2024-01-16
ICLR.cc/2024/Conference (présentation orale)
doi.org
openreview.net
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods
Yuchen Lu
Zhen Liu
Aristide Baratin
Romain Laroche
Aaron Courville
Alessandro Sordoni
2023-11-14
TMLR (accepté)
openreview.net
openreview.net
Ghost on the Shell: An Expressive Representation of General 3D Shapes
Zhen Liu
Yao Feng
Yuliang Xiu
Weiyang Liu
Liam Paull
Michael J. Black
Bernhard Schölkopf
2023-10-23
ArXiv (prépublication)
doi.org
arxiv.org
MeshDiffusion: Score-based Generative 3D Mesh Modeling
Zhen Liu
Yao Feng
Michael J. Black
Derek Nowrouzezahrai
Liam Paull
Weiyang Liu
We consider the task of generating realistic 3D shapes, which is useful for a variety of applications such as automatic scene generation and… (voir plus) physical simulation. Compared to other 3D representations like voxels and point clouds, meshes are more desirable in practice, because (1) they enable easy and arbitrary manipulation of shapes for relighting and simulation, and (2) they can fully leverage the power of modern graphics pipelines which are mostly optimized for meshes. Previous scalable methods for generating meshes typically rely on sub-optimal post-processing, and they tend to produce overly-smooth or noisy surfaces without fine-grained geometric details. To overcome these shortcomings, we take advantage of the graph structure of meshes and use a simple yet very effective generative modeling method to generate 3D meshes. Specifically, we represent meshes with deformable tetrahedral grids, and then train a diffusion model on this direct parameterization. We demonstrate the effectiveness of our model on multiple generative tasks.
2023-02-01
ICLR.cc/2023/Conference (notable)
doi.org
openreview.net