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I am a first-year Ph.D student in the Department of Electronic Engineering at Tsinghua University , advised by Prof. Yueqi Duan. In 2023, I obtained my B.Eng. in the Department of Electronic Engineering, Tsinghua University. My research interest lies in the causality, machine learning and 3D computer vision. I aim to build reliable models with a focus on their generalization ability (to unseen data or different domains), robustness (to data noise and bias) and explainability. Email / CV / Google Scholar / Github |
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* indicates equal contribution |
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Junliang Ye* , Fangfu Liu*, Qixiu Li, Zhengyi Wang , Yikai Wang , Xinzhou Wang, Yueqi Duan , Jun Zhu Arxiv, 2024 [arXiv] [Code] [Project Page] In this work, We propose the first general-purpose human preference reward model for text-to-3D generation, named Reward3D. Then we further introduce a novel text-to-3D framework, coined DreamReward, which greatly boosts high-text alignment and high-quality 3D generation through human preference feedback. |
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Fangfu Liu, Hanyang Wang, Weiliang Chen, Haowen Sun, Yueqi Duan Arxiv, 2024 [arXiv] [Code] [Project Page] We introduce a novel 3D customization method, dubbed Make-Your-3D that can personalize high-fidelity and consistent 3D content from only a single image of a subject with text description within 5 minutes. |
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Fangfu Liu, Diankun Wu, Yi Wei , Yongming Rao , Yueqi Duan IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 [arXiv] [Code] [Project Page] We propose Sherpa3D, a new text-to-3D framework that achieves high-fidelity, generalizability, and geometric consistency simultaneously. Extensive experiments show the superiority of our Sherpa3D over the state-of-the-art text-to-3D methods in terms of quality and 3D consistency. |
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Fangfu Liu, Wenchang Ma, An Zhang , Xiang Wang , Yueqi Duan , Tat-Seng Chua ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023 Oral Presentation [arXiv] [Code] [Project Page] we propose a dynamic causal space for DAG structure learning, coined CASPER, that integrates the graph structure into the score function as a new measure in the causal space to faithfully reflect the causal distance between estimated and groundtruth DAG. |
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Fangfu Liu, Chubin Zhang, Yu Zheng, Yueqi Duan IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 [arXiv] [Code] [Project Page] We propose a neural semantic representation called Semantic-Ray (S-Ray) to build a generalizable semantic field, which is able to learn from multiple scenes and directly infer semantics on novel viewpoints across novel scenes. |
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An Zhang, Fangfu Liu, Wenchang Ma, Zhibo Cai, Xiang Wang , Tat-Seng Chua International Conference on Learning Representations (ICLR), 2023 [arXiv] [Code] [Project Page] We propose ReScore, a simple-yet-effective model-agnostic optimzation framework that simultaneously eliminates spurious edge learning and generalizes to heterogeneous data by utilizing learnable adaptive weights. |
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Yuhao Lu, Yixuan Fan, Beixing Deng, Fangfu Liu, Yali Li, Shengjin Wang International Conference on Intelligent Robots and Systems (IROS), 2023 [arXiv] [Code] [Project Page] The VL-Grasp is an interactive grasp policy combined with visual grounding and 6-dof grasp pose detection tasks. The robot can adapt to various observation views and more diverse indoor scenes to grasp the target according to a human's language command by applying the VL-Grasp. Meanwhile, we build a new visual grounding dataset specially designed for the robot interaction grasp task, called RoboRefIt. |
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