Search Results for author: Tiancheng Zhi

Found 7 papers, 2 papers with code

FG-Net: Facial Action Unit Detection with Generalizable Pyramidal Features

1 code implementation23 Aug 2023 Yufeng Yin, Di Chang, Guoxian Song, Shen Sang, Tiancheng Zhi, Jing Liu, Linjie Luo, Mohammad Soleymani

The proposed FG-Net achieves a strong generalization ability for heatmap-based AU detection thanks to the generalizable and semantic-rich features extracted from the pre-trained generative model.

Action Unit Detection Cross-corpus +1

AgileGAN3D: Few-Shot 3D Portrait Stylization by Augmented Transfer Learning

no code implementations24 Mar 2023 Guoxian Song, Hongyi Xu, Jing Liu, Tiancheng Zhi, Yichun Shi, Jianfeng Zhang, Zihang Jiang, Jiashi Feng, Shen Sang, Linjie Luo

Capitalizing on the recent advancement of 3D-aware GAN models, we perform \emph{guided transfer learning} on a pretrained 3D GAN generator to produce multi-view-consistent stylized renderings.

Transfer Learning

AgileAvatar: Stylized 3D Avatar Creation via Cascaded Domain Bridging

no code implementations15 Nov 2022 Shen Sang, Tiancheng Zhi, Guoxian Song, Minghao Liu, Chunpong Lai, Jing Liu, Xiang Wen, James Davis, Linjie Luo

We propose a novel self-supervised learning framework to create high-quality stylized 3D avatars with a mix of continuous and discrete parameters.

Self-Supervised Learning

Learning Continuous Implicit Representation for Near-Periodic Patterns

1 code implementation25 Aug 2022 Bowei Chen, Tiancheng Zhi, Martial Hebert, Srinivasa G. Narasimhan

To address these challenges, we learn a neural implicit representation using a coordinate-based MLP with single image optimization.

TexMesh: Reconstructing Detailed Human Texture and Geometry from RGB-D Video

no code implementations ECCV 2020 Tiancheng Zhi, Christoph Lassner, Tony Tung, Carsten Stoll, Srinivasa G. Narasimhan, Minh Vo

We present TexMesh, a novel approach to reconstruct detailed human meshes with high-resolution full-body texture from RGB-D video.

Deep Material-Aware Cross-Spectral Stereo Matching

no code implementations CVPR 2018 Tiancheng Zhi, Bernardo R. Pires, Martial Hebert, Srinivasa G. Narasimhan

Often, multiple cameras are used for cross-spectral imaging, thus requiring image alignment, or disparity estimation in a stereo setting.

Disparity Estimation Stereo Matching +1

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