From dynamic scene reconstruction to 3D generative modeling, CVPR 2026 Best Papers showcase novel solutions poised to shape the next era of intelligent systems

NEW YORK, June 11, 2026 /PRNewswire/ — The IEEE Computer Society (CS) and the Computer Vision Foundation (CVF) announced the award-winning papers from the 2026 Conference on Computer Vision and Pattern Recognition (CVPR), recognizing outstanding achievements in computer vision.

Best Paper Awards

Following a rigorous review process that resulted in 4,089 accepted papers from 16,092 submissions, the CVPR 2026 Awards Selection Committee selected the following two papers for top honors at this year’s conference:

CVPR 2026 Best Paper

• Efficiently Reconstructing Dynamic Scenes One D4RT at a Time, Authors: Chuhan Zhang; Guillaume Le Moing^; Skanda Koppula; Ignacio Rocco; Liliane Momeni; Junyu Xie; Shuyang Sun; Rahul Sukthankar; Joëlle K. Barral; Raia Hadsell; Zoubin Ghahramani; Andrew Zisserman; Junlin Zhang; Mehdi S. M. Sajjadi – A team from Google DeepMind, the University College London, and the University of Oxford developed D4RT, a network that can reconstruct the geometry and motion of dynamic 4D scenes from video. Using a unified transformer-based architecture, the model estimates depth, spatio-temporal correspondence, and full camera parameters, allowing for the independent and efficient probing of a 3D position of any point in space and time. By simplifying what has traditionally been a computationally intensive process, D4RT provides a lightweight and highly scalable method that enables remarkably efficient training and inference.

CVPR 2026 Best Student Paper

• Native and Compact Structured Latents for 3D Generation, Authors: Jianfeng Xiang; Xiaoxue Chen; Sicheng Xu; Ruicheng Wang; Zelong Lv; Yu Deng; Hongyuan Zhu; Yue Dong; Hao Zhao; Nicholas Jing Yuan; Jiaolong Yang – A team from Tsinghua University, Microsoft Research, the University of Science and Technology of China, and Microsoft AI developed a new approach to 3D generative modeling that significantly improves the quality and realism of AI-generated 3D assets. The research is centered around O-Voxel, a novel representation that can accurately capture complex shapes and surface attributes. With O-Voxel, the geometry and quality of generated assets far exceeds those of existing models, providing for significant advancement in 3D generative modeling.

“The award-winning papers this year exemplify the innovation and technical excellence that continue to drive the field forward,” said Alexander G. Schwing, Associate Professor, Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Ill., U.S., CVPR 2026 Program Co-Chair. “From advances in dynamic scene reconstruction to breakthroughs in 3D generative modeling, these works address fundamental challenges in computer vision while opening new possibilities for applications across AI, robotics, and more. We congratulate the authors on this well-deserved recognition and thank the awards committee for their thoughtful evaluation process.”

In addition to these top papers, the Awards Committee identified two best paper honorable mentions and one best student paper honorable mention:

CVPR 2026 Best Paper Honorable Mentions

• NitroGen: An Open Foundation Model for Generalist Gaming Agents, Authors: Loïc Magne, Anas Awadalla, Guanzhi Wang, Yinzhen Xu, Joshua Belofsky, Fengyuan Hu, Joohwan Kim, Ludwig Schmidt, Georgia Gkioxari, Jan Kautz, Yisong Yue, Yejin Choi, Yuke Zhu, Linxi Fan – A team, including authors from NVIDIA, Stanford University, California Institute of Technology, University of Chicago, and the University of Texas at Austin, introduced NitroGen, a vision-action foundation model for generalist gaming agents that is trained on 40,000 hours of gameplay videos across more than 1,000 games, which exhibits strong competence across diverse domains.
• SAM 3D: 3Dfy Anything in Images, Authors: Xingyu Chen, FU-JEN CHU, Pierre Gleize, Kevin J Liang, Alexander Sax, Hao Tang, Weiyao Wang, Michelle Guo, Thibaut Hardin, Xiang Li, Aohan Lin, Jia-Wei Liu, Ziqi Ma, Anushka Sagar, Bowen Song, Xiaodong Wang, Jianing Yang, Bowen Zhang, Piotr Dollár, Georgia Gkioxari, Matt Feiszli, Jitendra Malik – A team from Meta Superintelligence Labs presented SAM 3D, a generative model for visually grounded 3D object reconstruction, predicting geometry, texture, and layout from a single image. The work obtains significant gains over recent work, with at least a 5:1 win rate in human preference tests on real-world objects and scenes.

CVPR 2026 Best Student Paper Honorable Mention

• ChordEdit: One-Step Low-Energy Transport for Image Editing, Authors: Liangsi Lu, Xuhang Chen, Minzhe Guo, Shichu Li, Jingchao Wang, Yang Shi – A team from Guangdong University of Technology, Huizhou University, Shenzhen University, and Peking University introduced ChordEdit, a model agnostic, training-free, and inversion-free method that facilitates high-fidelity one-step image editing. It enables fast, lightweight and precise edits, finally achieving true real-time editing on challenging models.

All award-winning papers demonstrate exceptional results that help to advance computer vision, artificial intelligence (AI), and more. They were announced during the conference Welcome and Awards Ceremony held on 5 June and presented at the IEEE Computer Society Technical Community on Pattern Analysis and Machine Intelligence (TCPAMI) meeting on 6 June.

“These papers represent some of the most impactful and forward-looking research presented at CVPR 2026,” said Chen Change Loy, Tan Lip-Bu Professor in AI at the College of Computing and Data Science, Nanyang Technological University, Singapore, and CVPR 2026 Program Co-Chair. “The selected papers introduce concepts and practices that undoubtedly will help shape the next generation of computer vision systems and applications.”

About CVPR 2026

The Conference on Computer Vision and Pattern Recognition (CVPR) is the preeminent computer vision event for new research in support of artificial intelligence (AI), computer vision, multi-modal AI, wearable AI, novel AI architectures, spatial computing, agentic AI, AI for science, embodied AI and robotics, and much more. Sponsored by the IEEE Computer Society (CS) and the Computer Vision Foundation (CVF), CVPR delivers the important advances in computer vision and pattern recognition that are expanding the frontiers of computer vision and defining the next generation of intelligent, interactive technologies. With a first-in-class technical program, including tutorials and workshops, a leading-edge, immersive expo, and robust networking opportunities, CVPR, which is annually attended by more than 10,000 scientists and engineers from across the globe, creates a one-of-a-kind opportunity for networking, recruiting, inspiration, and motivation.

CVPR 2026 took place 3-7 June at Colorado Convention Center in Denver, Colo., U.S. CVPR 2027 will take place 19-26 June at the Seattle Convention Center in Seattle, Wash., U.S. For more information about CVPR, visit cvpr.thecvf.com.

About the Computer Vision Foundation

The Computer Vision Foundation (CVF) is a non-profit organization whose purpose is to foster and support research on all aspects of computer vision. Together with the IEEE Computer Society, it co-sponsors the two largest computer vision conferences, CVPR and the International Conference on Computer Vision (ICCV). Visit thecvf.com for more information.

About the IEEE Computer Society

Engaging computer engineers, scientists, academia, and industry professionals from all areas of computing, the IEEE Computer Society sets the standard of excellence and champions global advancements to benefit humanity.

As the IEEE Computer Society celebrates its 80th anniversary in 2026, it continues to build on its rich legacy. Through conferences, publications, and programs that bring together computer science and engineering leaders at every stage of their careers, the IEEE Computer Society empowers, shapes, and guides the future of its 375,000+ community members, as well as the greater computing community, enabling new opportunities to better serve our world. Visit computer.org for more information.

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SOURCE Computer Vision Foundation (CVF)