Easi3R: Estimating Disentangled Motion from DUSt3R Without Training
This work addresses the bottleneck of limited 4D datasets for researchers in computer vision by providing an efficient, training-free alternative to conventional methods that require extensive training or fine-tuning.
The paper tackles the problem of 4D reconstruction from dynamic videos by introducing Easi3R, a training-free method that uses attention adaptation in DUSt3R to disentangle motion, achieving state-of-the-art performance in dynamic region segmentation, camera pose estimation, and 4D dense point map reconstruction.
Recent advances in DUSt3R have enabled robust estimation of dense point clouds and camera parameters of static scenes, leveraging Transformer network architectures and direct supervision on large-scale 3D datasets. In contrast, the limited scale and diversity of available 4D datasets present a major bottleneck for training a highly generalizable 4D model. This constraint has driven conventional 4D methods to fine-tune 3D models on scalable dynamic video data with additional geometric priors such as optical flow and depths. In this work, we take an opposite path and introduce Easi3R, a simple yet efficient training-free method for 4D reconstruction. Our approach applies attention adaptation during inference, eliminating the need for from-scratch pre-training or network fine-tuning. We find that the attention layers in DUSt3R inherently encode rich information about camera and object motion. By carefully disentangling these attention maps, we achieve accurate dynamic region segmentation, camera pose estimation, and 4D dense point map reconstruction. Extensive experiments on real-world dynamic videos demonstrate that our lightweight attention adaptation significantly outperforms previous state-of-the-art methods that are trained or finetuned on extensive dynamic datasets. Our code is publicly available for research purpose at https://easi3r.github.io/