title: Easi3R: Estimating Disentangled Motion from DUSt3R Without Training
summary: 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/
paper_authors._id: 67eb72a2291b56e50b66a063
paper_authors.name: Xingyu Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-01T07:46:33.467Z
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66606a13fc6c0816442bd161/tS8pBDXEb3QkIjvZao55l.jpeg
paper_authors.fullname: Xingyu Chen
paper_authors.type: user
paper_authors.user: rover-xingyu
paper_authors._id: 67eb72a2291b56e50b66a064
paper_authors.name: Yue Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-01T07:46:31.158Z
paper_authors._id: 66f80281d88dc2ad510663e9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/5HV3mTu-cxPCnWlCi_2wB.jpeg
paper_authors.fullname: Yue Chen
paper_authors.type: user
paper_authors.user: faneggg
paper_authors._id: 67eb72a2291b56e50b66a065
paper_authors.name: Yuliang Xiu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-12T12:56:26.718Z
paper_authors._id: 6183d0b249ef1d984699e4a3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6183d0b249ef1d984699e4a3/M6B9b73v8su9H8jfdcbgu.jpeg
paper_authors.fullname: Yuliang Xiu
paper_authors.type: user
paper_authors.user: Yuliang
paper_authors._id: 67eb72a2291b56e50b66a066
paper_authors.name: Andreas Geiger
paper_authors._id: 67eb72a2291b56e50b66a067
paper_authors.name: Anpei Chen
paper_title: Easi3R: Estimating Disentangled Motion from DUSt3R Without Training
paper_summary: 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/
paper_ai_summary: 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.
paper_ai_keywords: 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