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I am a PhD student at ETH Zurich and University of Tübingen under ELLIS, supervised by Marc Pollefeys and Andreas Geiger. I have broad interests in computer vision and deep learning, particularly in motion, dense correspondences and 3D scene representation learning. Before starting my PhD, I spent two wonderful years working remotely with Jianfei Cai and Hamid Rezatofighi at Monash University, Australia. I obtained a master degree at University of Science and Technology of China (USTC) in 2021, where I was supervised by Juyong Zhang. During my master study, I interned at Nanyang Technological University (NTU), Singapore and Microsoft Research Asia (MSRA). I received the Outstanding Reviewer award in CVPR 2022. |
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Yuedong Chen, Haofei Xu, Qianyi Wu, Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai arXiv, 2023 project page / code Employing explicit correspondence matching as a geometry prior enables NeRF to generalize across scenes. |
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Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, Dacheng Tao, Andreas Geiger IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023 project page / slides / video(cn) / code / colab / demo A unified dense correspondence matching formulation enables three motion and 3D perception tasks to be solved with a unified model. |
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Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Dacheng Tao Computer Vision and Pattern Recognition (CVPR), 2022 (Oral) slides / video(cn) / poster / code Learning strong features with a Transformer enables optical flow to be obtained by directly comparing feature similarities. |
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Haofei Xu, Jiaolong Yang, Jianfei Cai, Juyong Zhang, Xin Tong International Conference on Computer Vision (ICCV), 2021 (Oral) code Factorizing 2D optical flow with 1D attention and 1D correlation enables 4K resolution optical flow estimation on ordinary GPUs. |
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Wanquan Feng, Juyong Zhang, Hongrui Cai, Haofei Xu, Junhui Hou, Hujun Bao Computer Vision and Pattern Recognition (CVPR), 2021 project page / code A new non-rigid representation and a differentiable loss function enable end-to-end learning of non-rigid registration. |
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Haofei Xu, Juyong Zhang Computer Vision and Pattern Recognition (CVPR), 2020 code A sparse points-based cost aggregation method leads to an efficient and accurate stereo matching architecture without any 3D convolutions. |
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Haofei Xu, Jianmin Zheng, Jianfei Cai, Juyong Zhang International Joint Conference on Artificial Intelligence (IJCAI), 2019 code A bicubic motion representation enables unsupervised depth estimation from monocular videos in dynamic scenes. |
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