Jinlu Zhang

Jinlu Zhang (张金禄)

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Jinlu Zhang is a Ph.D. student in CFCS, School of Computer Science, Peking University, supervised by Prof. Yizhou Wang.
Before that, he obtained a Master's degree in 2023 from State Key Laboratory at Wuhan University, supervised by Prof. Zhigang Tu and Prof. Junsong Yuan. And he received a B.Eng from Shandong University in 2020, with an honor degree and outstanding student award.

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Research

My current research interests include computer vision and machine learning.
I am focusing on Human-centric 3D vision, and Intelligent agent interacting with human in 3D world.

News

[2025.02] One paper accepted to CVPR 2025 as Highlight, focusing on 3D human object intraction.

[2024.02] One paper accepted to CVPR 2024 as Highlight, focusing on 3D human motion generation.

[2023.11] One paper accepted to TPAMI, a comprehensive survey on 3D human motion generation.

[2023.07] One paper accepted to ICCV 2023.

Preprints

	EMO-X: Efficient Multi-Person Pose and Shape Estimation in One-Stage Haohang Jian, Jinlu Zhang, Junyi Wu, Zhigang Tu Arxiv, 2025 Paper Abstract Our EMO-X leverages the superior global modeling capability of Mamba and designs a local bidirectional scan mechanism for skeleton-aware local refinement. Notably, it achieves a significant reduction in computational complexity, requiring 69.8% less inference time compared to state-of-the-art (SOTA) methods, while outperforming most of them in accuracy.
	OwlSight: A Robust Illumination Adaptation Framework for Dark Video Human Action Recognition Shihao Cheng, Jinlu Zhang, Yue Liu, Zhigang Tu Arxiv, 2025 Paper Abstract We propose OwlSight, a biomimetic-inspired framework with whole-stage illumination enhancement to interact with action classification for accurate dark video human action recognition.
	Expressive Keypoints for Skeleton-based Action Recognition via Skeleton Transformation Yijie Yang, Jinlu Zhang, Jiaxu Zhang, Zhigang Tu Arxiv, 2024 Paper/ Code Abstract In this work, we propose Expressive Keypoints that incorporates hand and foot details to form a fine-grained skeletal representation, to improve the discriminative ability for existing models in discerning intricate human actions.

Publications

	InteractAnything: Zero-shot Human Object Interaction Synthesis via LLM Feedback and Object Affordance Parsing Jinlu Zhang , Yixin Chen, Zan Wang, Jie Yang, Yizhou Wang, Siyuan Huang IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025 Highlight Project page/ Paper Abstract In this work, we propose a novel zero-shot 3D HOI generation framework without training on specific datasets, leveraging the knowledge from large-scale pre-trained models.
	Move as You Say Interact as You Can: Language-guided Human Motion Generation with Scene Affordance Zan Wang, Yixin Chen, Baoxiong Jia, Puhao Li, Jinlu Zhang , Jingze Zhang, Tengyu Liu, Yixin Zhu, Wei Liang, Siyuan Huang IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024 Highlight Project page/ Paper/ Code Abstract In this work, we introduce a novel two-stage framework that employs scene affordance as an intermediate representation, effectively linking 3D scene grounding and conditional motion generation. Our framework comprises an Affordance Diffusion Model (ADM) for predicting explicit affordance map and an Affordance-to-Motion Diffusion Model (AMDM) for generating plausible human motions.
	Human Motion Generation: A Survey Wentao Zhu, Xiaoxuan Ma, Dongwoo Ro, Hai Ci, Jinlu Zhang, Jiaxin Shi, Feng Gao, Qi Tian, Yizhou Wang IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024 Paper Abstract A comprehensive survey of recent advances in human motion generation, covering the technical evolution, key challenges, and future directions in this rapidly developing field.
	PHRIT: Parametric Hand Representation with Implicit Template Zhisheng Huang, Yujin Chen, Di Kang, Jinlu Zhang , Zhigang Tu IEEE/CVF International Conference on Computer Vision (ICCV), 2023 Paper Abstract We propose PHRIT, a novel approach for parametric hand mesh modeling with an implicit template that combines the advantages of both parametric meshes and implicit representations. Our method represents deformable hand shapes using signed distance fields (SDFs) with part-based shape priors, utilizing a deformation field to execute the deformation.
	MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video Jinlu Zhang , Zhigang Tu , Jianyu Yang , Yujin Chen , Junsong Yuan IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Paper/ Video/ Code/ Bibtex Abstract We propose MixSTE (Mixed Spatio-Temporal Encoder), which has a temporal transformer to separately model the temporal motion of each joint and a spatial transformer to learn inter-joint spatial correlation.
	Uncertainty-Aware 3D Human Pose Estimation from Monocular Video Jinlu Zhang , Yujin Chen , Zhigang Tu ACM International Conference on Multimedia (ACM MM), 2022 Paper Abstract We propose an uncertainty-aware method to quantify and optimize the depth and 2D detection input respectively.

Experience
Research Intern at BigAI, working with Yixin Chen and Siyuan Huang.	Sep, 2023 - Feb, 2025
Research Intern at Tencent, working with Wei Zhuo.	Jan, 2022 - Jan, 2023
Cooperation Project of WHU & Tencent in real-time super-resolution.	Dec, 2021 - May, 2022
Research Intern at Huawei (Suzhou).	Jul, 2019 - Oct, 2019

Services & Activities

Conference Reviewer: CVPR'22/23/25, ICCV'23/25, ECCV'24, ICML'24, NeurIPS'24.

Journal Reviewer of: TPAMI, TIP, TSCVT.

Awards & Scholarships
National Scholarship of Wuhan University	Oct, 2022
1st Runner-up in ICCV2021 MMVRAC Challenge Track 2&3. [Link]	Sep, 2021
Top 10 first-year graduate students scholarship of WHU LIESMARS.	Nov, 2020

Education

	Doctor of Computer Applied Technology CFCS, School of Computer Science, Peking University, China. Advisor: Prof. Yizhou Wang 2023 - Now
	Master of Computer Applied Technology State Key Laboratory at Wuhan University, China. Advisor: Prof. Zhigang Tu and Prof. Junsong Yuan 2020 - 2023
	Bachelor Degree of Computer Science and Technology Computer Science, Shandong University, China. 2016 - 2020, with Honours degree

Thanks for the Jon Barron template.