Junliang Ye

Junliang Ye | 叶俊良

I am a second-year master's student in the Department of Computer Science at Tsinghua University , advised by Prof. Jun Zhu. In 2022, I obtained my B.S. in the School of Mathematical Sciences at Peking University.

My research interest lies in the AI4science, machine learning and AIGC.

My email:yejl23@mails.tsinghua.edu.cn

Email / CV / Google Scholar / Github

News

2025-06: One papers on 3D Vision are accepted by ICCV 2025.

2024-07: Two papers on 3D AIGC are accepted by ECCV 2024.

Publications

* indicates equal contribution

	ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understanding Junliang Ye, Zhengyi Wang, Ruowen Zhao, Shenghao Xie, Jun Zhu Arxiv*2025 [arXiv] [Code] [Project Page] We propose ShapeLLM-Omni, a multimodal large model that integrates 3D generation, understanding, and editing capabilities.
	DeepMesh: Auto-Regressive Artist-Mesh Creation With Reinforcement Learning Ruowen Zhao, Junliang Ye, Zhengyi Wang, Gaungce Liu, Yiwen Chen, Yikai Wang, Jun Zhu International Conference on Computer Vision (ICCV)*, 2025 [arXiv] [Code] [Project Page] We propose DeepMesh, which generates meshes with intricate details and precise topology, surpassing state-of-the-art methods in both precision and quality.
	DreamReward-X: Boosting High-Quality 3D Generation with Human Preference Alignment Fangfu Liu, Junliang Ye, Yikai Wang, Hanyang Wang, Zhengyi Wang, Jun Zhu, Yueqi Duan Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence, TPAMI, 2024 [arXiv] [Code] [Project Page] We present a comprehensive framework, coined DreamReward++, where we introduce a reward-aware noise sampling strategy to unleash text-driven diversity during the generation process while ensuring human preference alignment. Grounded by theoretical proof and extensive experiment comparisons, our method successfully generates high-fidelity and diverse 3D results with significant boosts in prompt alignment with human intention. Our results demonstrate the great potential for learning from human feedback to improve 3D generation.
	ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model Fangfu Liu, Wenqiang Sun, Hanyang Wang, Yikai Wang, Sun Haowen, Junliang Ye, Jun Zhang, Yueqi Duan Arxiv*, 2024 [arXiv] [Code] [Project Page] In this paper, we propose ReconX, a novel 3D scene reconstruction paradigm that reframes the ambiguous reconstruction challenge as a temporal generation task. The key insight is to unleash the strong generative prior of large pre-trained video diffusion models for sparse-view reconstruction.
	DreamReward: Aligning Human Preference in Text-to-3D Generation Junliang Ye, Fangfu Liu, Qixiu Li, Zhengyi Wang, Yikai Wang, Xinzhou Wang, Yueqi Duan , Jun Zhu European Conference on Computer Vision (ECCV), 2024 [arXiv] [Code] [Project Page] We present a comprehensive framework, coined DreamReward, to learn and improve text-to-3D models from human preference feedback.
	AnimatableDreamer: Text-Guided Non-rigid 3D Model Generation and Reconstruction with Canonical Score Distillation Xinzhou Wang, Yikai Wang, Junliang Ye, Zhengyi Wang, Fuchun Sun, Pengkun Liu, Ling Wang, Kai Sun, Xintong Wang, Bin He European Conference on Computer Vision (ECCV), 2024 [arXiv] [Code] [Project Page] We propose ANIMATABLEDREAMER, a framework with the capability to generate generic categories of non-rigid 3D models.
	PKU_WICT at TRECVID 2022: Disaster Scene Description and Indexing Task Yanzhe Chen, HsiaoYuan Hsu, Junliang Ye, Zhiwen Yang, Zishuo Wang, Xiangteng He, Yuxin Peng Virtual, Online [arXiv] [Code] [Project Page] We achieved first place in the TRECVID 2022 competition.

Academic Services

Review for CVPR 2023.

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