activity
20192024
most cited3D Dense Face Alignment via Graph Convolution Networks

25 citations · 34 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024

End to End Face Reconstruction via Differentiable PnP

Yiren Lu, Huawei Wei

This is a challenge report of the ECCV 2022 WCPA Challenge, Face Reconstruction Track. Inside this report is a brief explanation of how we accomplish this challenge. We design a tw…

cs.CV2023

ExpCLIP: Bridging Text and Facial Expressions via Semantic Alignment

Yicheng Zhong, Huawei Wei, Peiji Yang +1

The objective of stylized speech-driven facial animation is to create animations that encapsulate specific emotional expressions. Existing methods often depend on pre-established e…

cs.CV2023

XFormer: Fast and Accurate Monocular 3D Body Capture

Lihui Qian, Xintong Han, Faqiang Wang +6

We present XFormer, a novel human mesh and motion capture method that achieves real-time performance on consumer CPUs given only monocular images as input. The proposed network arc…

cs.CV2023★ 5 cited

Human MotionFormer: Transferring Human Motions with Vision Transformers

Hongyu Liu, Xintong Han, Chengbin Jin +8

Human motion transfer aims to transfer motions from a target dynamic person to a source static one for motion synthesis. An accurate matching between the source person and the targ…

cs.CV2020★ 4 cited

Balanced Alignment for Face Recognition: A Joint Learning Approach

Huawei Wei, Peng Lu, Yichen Wei

Face alignment is crucial for face recognition and has been widely adopted. However, current practice is too simple and under-explored. There lacks an understanding of how importan…

cs.CV2019★ 25 cited

3D Dense Face Alignment via Graph Convolution Networks

Huawei Wei, Shuang Liang, Yichen Wei

Recently, 3D face reconstruction and face alignment tasks are gradually combined into one task: 3D dense face alignment. Its goal is to reconstruct the 3D geometric structure of fa…