Easi3R: Estimating Disentangled Motion from DUSt3R Without Training
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/
67eb72a2291b56e50b66a063
Xingyu Chen
claimed_verified
2025-04-01T07:46:33.467Z
66606a13fc6c0816442bd161
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Xingyu Chen
user
rover-xingyu
67eb72a2291b56e50b66a064
Yue Chen
claimed_verified
2025-04-01T07:46:31.158Z
66f80281d88dc2ad510663e9
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/5HV3mTu-cxPCnWlCi_2wB.jpeg
Yue Chen
user
faneggg
67eb72a2291b56e50b66a065
Yuliang Xiu
claimed_verified
2025-05-12T12:56:26.718Z
6183d0b249ef1d984699e4a3
https://cdn-avatars.huggingface.co/v1/production/uploads/6183d0b249ef1d984699e4a3/M6B9b73v8su9H8jfdcbgu.jpeg
Yuliang Xiu
user
Yuliang
67eb72a2291b56e50b66a066
Andreas Geiger
67eb72a2291b56e50b66a067
Anpei Chen
Easi3R: Estimating Disentangled Motion from DUSt3R Without Training
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/
Easi3R, a training-free method leveraging attention adaptation in DUSt3R, achieves accurate 4D reconstruction by disentangling attention maps for dynamic region segmentation, camera pose estimation, and dense point map reconstruction.
Transformer network architectures direct supervision large-scale 3D datasets 4D datasets geometric priors optical flow depths attention adaptation dynamic region segmentation camera pose estimation 4D dense point map reconstruction