Chuanxia Zheng

Chuanxia Zheng is a postdoctoral researcher in VGG at the University of Oxford, working with Prof. Andrea Vedaldi and Prof. Andrew Zisserman.

Before that, he spent one year at Monash University, where he worked as a Research Fellow with Prof. Jianfei Cai and Prof. Dinh Phung. He received his PhD degree from the SCSE at Nanyang Technological University, supervised by Prof. Tat-Jen Cham and Prof. Jianfei Cai. His thesis Synthesizing Photorealistic Images was awarded the NTU Outstanding PhD Thesis Award 2022.

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His research interests are broadly in artificial intelligence, with emphasis on computer vision and machine learning. Much of his research is about 2D 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 via generative AI.

Cocktail🍸: Mixing Multi-Modality Controls for Text-Conditional Image Generation
Minghui Hu, Jianbin Zheng, Daqing Liu, Chuanxia Zheng, Chaoyue Wang, Dacheng Tao, Tat-Jen Cham
arXiv, 2023
project page / PDF / arXiv / video / code

We develop a generalized framework for multi-modality control based on text-to-image generation.

Explicit Correspondence Matching for Generalizable Neural Radiance Fields
Yuedong Chen, Haofei Xu, Qianyi Wu, Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai
arXiv, 2023
project page / arXiv / code

Employing explicit correspondence matching as a geometry prior enables NeRF to generalize across scenes.

Vector Quantized Wasserstein Auto-Encoder
Long Tung Vuong, Trung Le, He zhao, Chuanxia Zheng, Mehrtash Harandi, Jianfei Cai, Dinh Phung
ICML, 2023
arXiv / code (coming soon)

Minimize the codebook-data distortion as the Wasserstein distance.

UniD3: Unified Discrete Diffusion for Simultaneous Vision-Language Generation
Minghui Hu, Chuanxia Zheng, Heliang Zheng, Tat-Jen Cham, Chaoyue Wang, Zuopeng Yang, Dacheng Tao, P.N.Suganthan,
ICLR, 2023
project page / arXiv / code /

A unified discrete diffusion model for simultaneous vision-language generation.

MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation
Chuanxia Zheng, Long Tung Vuong, Jianfei Cai, Dinh Phung
NeurIPS (Spotlight), 2022
project page / PDF / arXiv / video / code(Kandinsky2) / poster /

A spatially conditional normalization is introduced to address the repeated artifacts in vector quantized methods.

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, 2023
ICCV    2019, 2021, 2023
ECCV    2020, 2022
NeurIPS    2022, 2023
ICLR    2021, 2022, 2023
ICML    2023
SIGGRAPH&Asia    2022
ICRA    2022
IROS    2022
IJCAI    2022
ACM MM    2021, 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 June. 2023.