Newswise — NEW YORK, 09 June 2026 – 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