This could be an interesting paper by Raia Hadsell, Zoubin Ghahramani, Andrew Zisserman and their team!
From the 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. ..."
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