1 code implementation • 26 Sep 2024 • Huan Wang, Feitong Tan, Ziqian Bai, yinda zhang, Shichen Liu, Qiangeng Xu, Menglei Chai, Anish Prabhu, Rohit Pandey, Sean Fanello, Zeng Huang, Yun Fu
Recent works have shown that neural radiance fields (NeRFs) on top of parametric models have reached SOTA quality to build photorealistic head avatars from a monocular video.
no code implementations • CVPR 2024 • Ziqian Bai, Feitong Tan, Sean Fanello, Rohit Pandey, Mingsong Dou, Shichen Liu, Ping Tan, yinda zhang
To address these challenges, we propose a novel fast 3D neural implicit head avatar model that achieves real-time rendering while maintaining fine-grained controllability and high rendering quality.
no code implementations • 19 Feb 2024 • Zhixuan Yu, Ziqian Bai, Abhimitra Meka, Feitong Tan, Qiangeng Xu, Rohit Pandey, Sean Fanello, Hyun Soo Park, yinda zhang
Traditional methods for constructing high-quality, personalized head avatars from monocular videos demand extensive face captures and training time, posing a significant challenge for scalability.
no code implementations • CVPR 2023 • Ziqian Bai, Feitong Tan, Zeng Huang, Kripasindhu Sarkar, Danhang Tang, Di Qiu, Abhimitra Meka, Ruofei Du, Mingsong Dou, Sergio Orts-Escolano, Rohit Pandey, Ping Tan, Thabo Beeler, Sean Fanello, yinda zhang
The learnt avatar is driven by a parametric face model to achieve user-controlled facial expressions and head poses.
1 code implementation • 25 Mar 2022 • Ziqian Bai, Timur Bagautdinov, Javier Romero, Michael Zollhöfer, Ping Tan, Shunsuke Saito
In this work, for the first time, we enable autoregressive modeling of implicit avatars.
1 code implementation • CVPR 2021 • Ziqian Bai, Zhaopeng Cui, Xiaoming Liu, Ping Tan
This paper presents a method for riggable 3D face reconstruction from monocular images, which jointly estimates a personalized face rig and per-image parameters including expressions, poses, and illuminations.
1 code implementation • CVPR 2020 • Ziqian Bai, Zhaopeng Cui, Jamal Ahmed Rahim, Xiaoming Liu, Ping Tan
We facilitate it with a CNN network that learns to regularize the non-rigid 3D face according to the input image and preliminary optimization results.
no code implementations • 4 Mar 2019 • Sanghamitra Dutta, Ziqian Bai, Tze Meng Low, Pulkit Grover
This work proposes the first strategy to make distributed training of neural networks resilient to computing errors, a problem that has remained unsolved despite being first posed in 1956 by von Neumann.
no code implementations • 27 Nov 2018 • Sanghamitra Dutta, Ziqian Bai, Haewon Jeong, Tze Meng Low, Pulkit Grover
First, we propose a novel coded matrix multiplication technique called Generalized PolyDot codes that advances on existing methods for coded matrix multiplication under storage and communication constraints.