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Gradient Spaces Lab

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There was a truck in the image. We removed it with AI. Can you spot where it was?
Photo taken in October 2024, Stanford University. Photo Credits: Geoffrey Tuttle

About Us

Welcome to the Gradient 𝚫 Spaces Research Group. The group belongs to the Civil and Environmental Engineering Department, Stanford University, under the Schools of Engineering and Sustainability. Our research and educational activities focus on developing quantitative and data-driven methods that learn from real-world visual data to design and construct data-driven sustainable and adaptive environments across the physical and digital space. Of particular interest is the creation of spaces that blend from the 100% physical (real reality) to the 100% digital (virtual reality) and anything in between, with the use of mixed reality and multi-level design (i.e., of buildings, processes, UXs, etc.). We believe that by cross-pollinating the two domains, we can achieve higher immersion and view these spaces as a step toward more equitable living conditions. Hence, we aim for developing methods that work in real-world settings on a global scale. To achieve the above, we are building a cross- and inter- disciplinary team that is diverse and well-rounded. Most importantly, we are driven by curiosity and learning, and so does everything we do.

To learn more about this vision, you can read this short story to illustrate this future and the impact on designers: 
A Day in the Life of an Architect in the Gradient World

News

U. V. Helava Award - Best Paper 2025

Tao Sun and Iro Armeni, as well as all co-authors, received the U. V. Helava Award - Best Paper 2025 for their paper Nothing Stands Still.

Research updates

Three papers accepted to CVPR 2026, see you in Denver!

Curious which?

Research updates

Two papers accepted to NeurIPS 2025, see you in San Diego!

Curious which?

Research Highlights

ReScene4D: Temporally Consistent Semantic Instance Segmentation of Evolving Indoor 3D Scenes
Emily Steiner, Jianhao Zheng, Henry Howard-Jenkins, Chris Xie, Iro Armeni
CVPR 2026
[pdf] [website]

WildPose: A Unified Framework for Robust Pose Estimation in the Wild
Jianhao Zheng, Liyuan Zhu, Zihan Zhu, Iro Armeni
CVPR 2026 
[pdf][code][website]

GaussFusion: Improving 3D Reconstruction in the wild with Geometry-Informed Video Generators
Liyuan Zhu, Manjunath Narayana, Michal Stary, Will Hutchcroft, Gordon Wetzstein, Iro Armeni
CVPR 2026
[pdf] [website]