FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction

1 code implementation • CVPR 2020 • Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, Xun Cao

In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and propose a novel algorithm that is able to predict elaborate riggable 3D face models from a single image input.

FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction

1 code implementation • 1 Nov 2021 • Hao Zhu, Haotian Yang, Longwei Guo, Yidi Zhang, Yanru Wang, Mingkai Huang, Menghua Wu, Qiu Shen, Ruigang Yang, Xun Cao

By training on FaceScape data, a novel algorithm is proposed to predict elaborate riggable 3D face models from a single image input.

3D Face Reconstruction 3D Reconstruction

A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations

no code implementations • 25 Aug 2020 • Mingkai Huang, Hao Li, Bing Bai, Chang Wang, Kun Bai, Fei Wang

Federated Learning(FL) is a newly developed privacy-preserving machine learning paradigm to bridge data repositories without compromising data security and privacy.

Collaborative Filtering Federated Learning +1

Hybrid Differentially Private Federated Learning on Vertically Partitioned Data

no code implementations • 6 Sep 2020 • Chang Wang, Jian Liang, Mingkai Huang, Bing Bai, Kun Bai, Hao Li

We present HDP-VFL, the first hybrid differentially private (DP) framework for vertical federated learning (VFL) to demonstrate that it is possible to jointly learn a generalized linear model (GLM) from vertically partitioned data with only a negligible cost, w. r. t.

Privacy Preserving Vertical Federated Learning