{"ID":2831355,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.08924","arxiv_id":"2512.08924","title":"Efficiently Reconstructing Dynamic Scenes One D4RT at a Time","abstract":"Understanding and reconstructing the complex geometry and motion of dynamic scenes from video remains a formidable challenge in computer vision. This paper introduces D4RT, a simple yet powerful feedforward model designed to efficiently solve this task. D4RT utilizes a unified transformer architecture to jointly infer depth, spatio-temporal correspondence, and full camera parameters from a single video. Its core innovation is a novel querying mechanism that sidesteps the heavy computation of dense, per-frame decoding and the complexity of managing multiple, task-specific decoders. Our decoding interface allows the model to independently and flexibly probe the 3D position of any point in space and time. The result is a lightweight and highly scalable method that enables remarkably efficient training and inference. We demonstrate that our approach sets a new state of the art, outperforming previous methods across a wide spectrum of 4D reconstruction tasks. We refer to the project webpage for animated results: https://d4rt-paper.github.io/.","short_abstract":"Understanding and reconstructing the complex geometry and motion of dynamic scenes from video remains a formidable challenge in computer vision. This paper introduces D4RT, a simple yet powerful feedforward model designed to efficiently solve this task. D4RT utilizes a unified transformer architecture to jointly infer...","url_abs":"https://arxiv.org/abs/2512.08924","url_pdf":"https://arxiv.org/pdf/2512.08924v2","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\"]","published":"2025-12-09T18:57:21Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\"]","has_code":false}