Chuanxia Zheng

I am a research fellow in the Department of Data Science & AI at Monash University, where I work on computer vision and machine learning with Jianfei Cai and Dinh Phung.

I received my PhD degree from the School of Computer Science and Engineering at Nanyang Technological University, where I was advised by Tat-Jen Cham and Jianfei Cai. My thesis Synthesizing Photorealistic Images was awarded the SCSE Outstanding PhD Thesis Award 2022. Before that, I received the Master’s degree in the IR&MCT Lab at Beihang University, advised by Jianhua Wang and Weihai Chen.

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My research interests are broadly in artificial intelligence, with emphasis on computer vision and machine learning. Much of my research is about image generation, completion and translation, 3D scene reconstruction, generation and completion with the goal of building intelligent machines, capable of rebuilding a photorealistic virtual world.

Object-Compositional Neural Implicit Surfaces
Qianyi Wu, Xian Liu, Yuedong Chen, Kejie Li, Chuanxia Zheng, Jianfei Cai, Jianmin Zheng
ECCV, 2022
project page / arXiv / video / code

Automatically decompose a scene into 3D instance, trained using only 2D semantic lables and images.

Sem2NeRF: Converting Single-View Semantic Masks to Neural Radiance Fields
Yuedong Chen, Qianyi Wu, Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai,
ECCV, 2022
project page / arXiv / video / code

We train a 3D inversion model to transfer the 2D semantic map into 3D NeRF, and lets users edit 3D model through 2D semantic input.

Bridging global context interactions for high-fidelity image completion
Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai, Dinh Phung
CVPR, 2022
project page / PDF / arXiv / video / code / poster

TFill fills in reasonable contents for both foreground object removal and content completion.

Visiting the Invisible: Layer-by-Layer Completed Scene Decomposition
Chuanxia Zheng, Duy-Son Dao, Guoxian Song, Tat-Jen Cham, Jianfei Cai,
IJCV, 2021
project page / PDF / arXiv / video / code

We build a high-level scene understanding system that simultaneously models the completed shape and appearance for all instances.

AgileGAN: Stylizing Portraits by Inversion-Consistent Transfer Learning
Guoxian Song, Linjie Luo, Jing Liu, Wan-Chun Ma, Chuanxia Zheng, Tat-Jen Cham,
project page / PDF / video / code / Online Demo

A GAN inversion model is trained for Stylizing Portraits.

The Spatially-Correlative Loss for Various Image Translation Tasks
Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai
CVPR, 2021
project page / PDF / arXiv / video / code / poster

We propose a novel spatially-correlative loss that is simple, efficient and yet effective for preserving scene structure consistency while supporting large appearance changes during unpaired I2I translation.

Pluralistic (Free-Form) Image Completion
Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai
IJCV, 2021
CVPR, 2019
project page / PDF / arXiv / video / code / poster

Given a single masked image, the proposed model is able to generate multiple and diverse plausible results.

T2Net: Synthetic-to-Realistic Translation for Depth Estimation Tasks
Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai
ECCV, 2018
project page / PDF / arXiv / video / code / poster

Without any real depth map, the proposed model evaluates depth maps on real scenes using only synthetic datasets.

Academic Services

Conference Reviewer

CVPR    2020, 2021, 2022
ICCV    2019, 2021
ECCV    2020, 2022
ICLR    2021, 2022, 2023
NeurIPS    2022
SIGGRAPH&Asia    2022
IJCAI    2022
ACM MM    2021, 2022
IROS    2022

Journal Reviewer

TPAMI, IJCV, TIP, JAS, TMM(Outstanding Reviewer Award, 2021), TCSVT, CVIU, TVCJ, NCAA

  • Teaching Assistant, Advanced Digital Image Processing, Graduate, NTU, 2018-2020
  • Teaching Assistant, Human-Computer Interaction, Undergraduate, NTU, 2018-2020
  • Teaching Assistant, Engineering Mathematics, Undergraduate, NTU, 2018-2020

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Last updated Sept. 2022.