Zihan Wang

Robotics Institute, Carnegie Mellon University

I am currently a full-time Applied Scientist Intern at Frontier AI & Robotics (FAR), Amazon. My research focuses on 4D vision and its applications to robotics (humaniod locomotion & loco-manipulation).

I earned my M.S. at the Robotics Institute, School of Computer Science, Carnegie Mellon University, I was extremely fortunate to be advised by Prof. Deva Ramanan. I was also fortunate to collaborate with Prof. Shubham Tulsiani. Prior to joining CMU, I previously earned my B.Eng. with Honours in Electrical and Electronics Engineering from The University of Edinburgh. At my undergraduate years, I had the fortune to work with Prof. Sebastian Scherer and Prof. Chen Wang at AirLab.

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We live in a dynamic 4D world, constantly seeing, understanding, and interacting with objects and environments. My goal is to bridge the gap between reconstructed virtual worlds and practical robotic applications. I develop methods that help robots perceive, understand, and interact with objects and environments from sparse observations.

A real-to-sim framework that turns monocular RGB video into whole-body control across diverse, complex terrains.

The first approach that enables free-view synthesis for 4D dynamic scene reconstruction under sparse-view capture.

Class-agnostic relations for few-shot detection without fine-tuning, enabling fast and efficient field deployment.

A general framework that learns whole-body humanoid-object interaction skills directly from monocular RGB videos.

Characterizes noise in large-scale pre-training data and proposes strategies to mitigate its impact on downstream tasks.

Clusters motion data, trains latent variable models, and deploys a style-aware high-level controller for multi-task behaviors.

Contributed physics-aware constrained optimization modules for trajectory planning and motion control.

Developed diffusion autoencoder components for medical imaging, with a focus on MRI generation and reconstruction.

• Edinburgh Award (Research Experience), 2022
• Edinburgh Scholarship (£4,000), 2022
• Edinburgh Scholarship (£4,000), 2021
• QIANJIANG ELECTRIC Scholarship (£3,000), 2020

Academic Reviewer

• International Conference on Learning Representations (ICLR)
• European Conference on Computer Vision (ECCV)
• IEEE International Conference on Robotics and Automation (ICRA)
• IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• IEEE Transactions on Image Processing (TIP)
• IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)

• Email: zihanwa3@cs.cmu.edu
• Phone: +1-412-589-8046
• Address: EDSH, Carnegie Mellon University, Pittsburgh, PA 15213, USA

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Template adapted from Qitao Zhao (credit to Yijia Weng & Jon Barron).
Last updated: February 2025