activity
20162022
most citedMotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model

110 citations · 233 across the 20 of their papers we have counts for

collaborators

20 papers

cs.CV20221 cited

On-Device Domain Generalization

Kaiyang Zhou, Yuanhan Zhang, Yuhang Zang +3

We present a systematic study of domain generalization (DG) for tiny neural networks. This problem is critical to on-device machine learning applications but has been overlooked in…

cs.CV2022110 cited

MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model

Mingyuan Zhang, Zhongang Cai, Liang Pan +4

Human motion modeling is important for many modern graphics applications, which typically require professional skills. In order to remove the skill barriers for laymen, recent moti…

cs.CV2022

Mind the Gap in Distilling StyleGANs

Guodong Xu, Yuenan Hou, Ziwei Liu +1

StyleGAN family is one of the most popular Generative Adversarial Networks (GANs) for unconditional generation. Despite its impressive performance, its high demand on storage and c…

cs.CV2022

Open Long-Tailed Recognition in a Dynamic World

Ziwei Liu, Zhongqi Miao, Xiaohang Zhan +3

Real world data often exhibits a long-tailed and open-ended (with unseen classes) distribution. A practical recognition system must balance between majority (head) and minority (ta…

cs.CV202213 cited

StyleFaceV: Face Video Generation via Decomposing and Recomposing Pretrained StyleGAN3

Haonan Qiu, Yuming Jiang, Hang Zhou +2

Realistic generative face video synthesis has long been a pursuit in both computer vision and graphics community. However, existing face video generation methods tend to produce lo…

cs.CV20223 cited

Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with Transformer

Zhipeng Luo, Changqing Zhou, Liang Pan +6

With the prevalence of LiDAR sensors in autonomous driving, 3D object tracking has received increasing attention. In a point cloud sequence, 3D object tracking aims to predict the…