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Chuanxia Zheng is a postdoctoral researcher in VGG at the University of Oxford, working with Prof. Andrea Vedaldi on 3D reconstruction and generation and Prof. Andrew Zisserman on generative model for understanding. Before that, he spent one year at Monash University, where he worked as a Research Fellow with Prof. Jianfei Cai and Prof. Dinh Phung on codebook learning for generation. He received his PhD degree from the SCSE at Nanyang Technological University, supervised by Prof. Tat-Jen Cham and Prof. Jianfei Cai on 2D generation, translation and completion. His thesis Synthesizing Photorealistic Images was awarded the NTU Outstanding PhD Thesis Award 2022. Email / CV / Google Scholar / Github / Twitter |
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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. In particular, he is working on topics: |
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Chuanxia Zheng, Andrea Vedaldi arXiv, 2023 project page / PDF / arXiv / video / code / Free3D synthesizes consistent novel views on open-set categories without the need of explicit 3D representations. |
 
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Minghui Hu, Jianbin Zheng, Chuanxia Zheng, Chaoyue Wang, Dacheng Tao, Tat-Jen Cham arXiv, 2023 project page / PDF / arXiv / code / HugeFace A versatile plug-and-play module to fix the scheduler flaws for diffusion models. |
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Tianhao Wu, Chuanxia Zheng, Tat-Jen Cham arXiv, 2023 project page / PDF / arXiv / code An indoor panorama outpainting model using latent diffusion models with view-consistent. |
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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. |
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Minghui Hu, Jianbin Zheng, Daqing Liu, Chuanxia Zheng, Chaoyue Wang, Dacheng Tao, Tat-Jen Cham NeurIPS, 2023 project page / PDF / arXiv / video / code We develop a generalized framework for multi-modality control based on text-to-image generation. |
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Chuanxia Zheng, Andrea Vedaldi ICCV, 2023 project page / PDF / arXiv / video / code / poster A simple approach to avoid codebook collapse and achive 100% codebook utilisation. |
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Long Tung Vuong, Trung Le, He zhao, Chuanxia Zheng, Mehrtash Harandi, Jianfei Cai, Dinh Phung ICML, 2023 arXiv / poster / code (coming soon) Minimize the codebook-data distortion as the Wasserstein distance. |
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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. |
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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. |
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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. |
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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. |
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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. |
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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. |
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Guoxian Song, Linjie Luo, Jing Liu, Wan-Chun Ma, Chuanxia Zheng, Tat-Jen Cham, SIGGRAPH, 2021 project page / PDF / video / code / Online Demo A GAN inversion model is trained for Stylizing Portraits. |
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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. |
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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. |
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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. |
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Conference Reviewer
Journal Reviewer TPAMI, IJCV, TIP, JAS, TMM(Outstanding Reviewer Award, 2021), TCSVT, CVIU, TVCJ, NCAA |
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