Search Results for author: Zhenfeng Fan

Found 8 papers, 3 papers with code

MyPortrait: Morphable Prior-Guided Personalized Portrait Generation

no code implementations5 Dec 2023 Bo Ding, Zhenfeng Fan, Shuang Yang, Shihong Xia

We incorporate personalized prior in a monocular video and morphable prior in 3D face morphable space for generating personalized details under novel controllable parameters.

RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search

1 code implementation23 May 2023 Yang Bai, Min Cao, Daming Gao, Ziqiang Cao, Chen Chen, Zhenfeng Fan, Liqiang Nie, Min Zhang

RA offsets the overfitting risk by introducing a novel positive relation detection task (i. e., learning to distinguish strong and weak positive pairs).

Person Search Relation +2

Towards Fine-grained 3D Face Dense Registration: An Optimal Dividing and Diffusing Method

1 code implementation23 Sep 2021 Zhenfeng Fan, Silong Peng, Shihong Xia

This method is then extended to 3D surface by formulating a local registration problem for dividing and a linear least-square problem for diffusing, with constraints on fixed features.

3D-TalkEmo: Learning to Synthesize 3D Emotional Talking Head

no code implementations25 Apr 2021 Qianyun Wang, Zhenfeng Fan, Shihong Xia

Impressive progress has been made in audio-driven 3D facial animation recently, but synthesizing 3D talking-head with rich emotion is still unsolved.

3D Face Reconstruction Talking Face Generation +1

A Backbone Replaceable Fine-tuning Framework for Stable Face Alignment

no code implementations19 Oct 2020 Xu sun, Zhenfeng Fan, Zihao Zhang, Yingjie Guo, Shihong Xia

The proposed framework achieves at least 40% improvement on stability evaluation metrics while enhancing detection accuracy versus state-of-the-art methods.

Attribute Face Alignment

Boosting Local Shape Matching for Dense 3D Face Correspondence

no code implementations CVPR 2019 Zhenfeng Fan, Xiyuan Hu, Chen Chen, Silong Peng

The seed points are initialized by a few landmarks, and are then augmented to boost shape matching between the template and the target face step by step, to finally achieve dense correspondence.

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