World's Best Scientists 2026 revealed!
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Rising Stars
2025

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Rising Stars

D-Index
56
Citations
16181
World Ranking
201
National Ranking
28

Computer Science

D-Index
61
Citations
15689
World Ranking
3058
National Ranking
1495

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Jiajun Wu is affiliated with Stanford University in the United States and has a substantial body of research primarily focused on computer science and engineering. Their work spans various subfields, including computer vision and pattern recognition, control and systems engineering, artificial intelligence, computational mechanics, and computer graphics and computer-aided design.

Their recent papers highlight contributions to multiple aspects of computer vision, 3D shape modeling, and multimodal machine learning. Notable publications include:

  • 3D Shape Generation and Completion through Point-Voxel Diffusion (2021, 2021 IEEE/CVF International Conference on Computer Vision - ICCV)
  • SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations (2021, arXiv - Cornell University)
  • Neural Radiance Flow for 4D View Synthesis and Video Processing (2021, 2021 IEEE/CVF International Conference on Computer Vision - ICCV)
  • Revisiting the "Video" in Video-Language Understanding (2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition - CVPR)
  • VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models (2023, arXiv - Cornell University)

Their frequent co-authors include Joshua B. Tenenbaum, Li Fei-Fei, Hong-Xing Yu, Jiayuan Mao, and Ruohan Gao. Collaborations with these researchers have contributed to 20, 19, 18, 16, and 11 publications respectively.

Jiajun Wu's research has been featured extensively in renowned venues, particularly in:

  • arXiv (Cornell University)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • ACM Transactions on Graphics
  • DSpace@MIT (Massachusetts Institute of Technology)

The main topics addressed in their work cover a diverse range of themes:

  • Human Pose and Action Recognition
  • Multimodal Machine Learning Applications
  • Advanced Vision and Imaging
  • Computer Graphics and Visualization Techniques
  • 3D Shape Modeling and Analysis
  • Robot Manipulation and Learning
  • Human Motion and Animation

Within the broader disciplines of computer science and engineering, Jiajun Wu focuses on problems related to vision, graphics, learning, and robotics. This research portfolio positions them at the interface of multiple technical domains, emphasizing both foundational and applied aspects of advanced machine learning systems in visual computing and robotic manipulation.

Best Publications

  • Video Enhancement with Task-Oriented Flow

    Tianfan Xue;Baian Chen;Jiajun Wu;Donglai Wei

  • On the Opportunities and Risks of Foundation Models.

    Rishi Bommasani;Drew A. Hudson;Ehsan Adeli;Russ Altman

  • Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

    Jiajun Wu;Chengkai Zhang;Tianfan Xue;William T. Freeman

  • pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis

    Eric R. Chan;Marco Monteiro;Petr Kellnhofer;Jiajun Wu

  • Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

    Xingyuan Sun;Jiajun Wu;Xiuming Zhang;Zhoutong Zhang

  • Deep multiple instance learning for image classification and auto-annotation

    Jiajun Wu;Yinan Yu;Chang Huang;Kai Yu

  • The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

    Jiayuan Mao;Chuang Gan;Pushmeet Kohli;Joshua B. Tenenbaum

  • Ambient Sound Provides Supervision for Visual Learning

    Andrew Hale Owens;Jiajun Wu;Joshua H. McDermott;William T. Freeman;William T. Freeman

  • Visual dynamics: probabilistic future frame synthesis via cross convolutional networks

    Tianfan Xue;Jiajun Wu;Katherine L. Bouman;William T. Freeman

  • Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding

    Kexin Yi;Jiajun Wu;Chuang Gan;Antonio Torralba

  • Single Image 3D Interpreter Network

    Jiajun Wu;Tianfan Xue;Joseph J. Lim;Joseph J. Lim;Yuandong Tian

  • 3D Shape Generation and Completion Through Point-Voxel Diffusion

    Linqi Zhou;Yilun Du;Jiajun Wu

  • 3D Shape Generation and Completion through Point-Voxel Diffusion

    Linqi Zhou;Yilun Du;Jiajun Wu

  • Galileo: perceiving physical object properties by integrating a physics engine with deep learning

    Jiajun Wu;Ilker Yildirim;Joseph J. Lim;William T. Freeman

  • MarrNet: 3D Shape Reconstruction via 2.5D Sketches

    Jiajun Wu;Yifan Wang;Tianfan Xue;Xingyuan Sun

  • The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

    Jiayuan Mao;Chuang Gan;Pushmeet Kohli;Joshua B. Tenenbaum

  • SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

    Unknown

  • ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

    Yuanming Hu;Jiancheng Liu;Andrew Spielberg;Joshua B. Tenenbaum

  • Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification

    Xiang Long;Chuang Gan;Gerard de Melo;Jiajun Wu

  • Raster-to-Vector: Revisiting Floorplan Transformation

    Chen Liu;Jiajun Wu;Pushmeet Kohli;Yasutaka Furukawa

  • CLEVRER: Collision Events for Video Representation and Reasoning

    Kexin Yi;Chuang Gan;Yunzhu Li;Pushmeet Kohli

  • Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks

    Amir Arsalan Soltani;Haibin Huang;Jiajun Wu;Tejas D. Kulkarni

  • MarrNet: 3D Shape Reconstruction via 2.5D Sketches

    Jiajun Wu;Yifan Wang;Tianfan Xue;Xingyuan Sun

  • ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding

    Unknown

  • Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids

    Yunzhu Li;Jiajun Wu;Russ Tedrake;Joshua B. Tenenbaum

  • Visual Object Networks: Image Generation with Disentangled 3D Representations

    Jun-Yan Zhu;Zhoutong Zhang;Chengkai Zhang;Jiajun Wu

Frequent Co-Authors

Chuang Gan
Chuang Gan University of Massachusetts Amherst
Pushmeet Kohli
Pushmeet Kohli DeepMind (United Kingdom)
Jun-Yan Zhu
Jun-Yan Zhu Carnegie Mellon University
Zhuowen Tu
Zhuowen Tu University of California, San Diego
Chelsea Finn
Chelsea Finn Stanford University

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