mlsanityData to 14 Oct 2026

GeoCo-SAVi: Geometry-Consistent Slot Attention for Explicitly Editable Object Representations

arXiv 2609.06628 ↗First posted 6 Sep 2026cs.CVcited by 0

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Abstract (arXiv)

Object-centric video models represent scenes with slots, yet exposed geometry can vary in meaning with appearance. In Invariant Slot Attention (ISA), explicit position and scale can disagree with the decoded center and extent; edits can yield unexpected motion or resizing, and replacing appearance can shift geometry. GeoCo-SAVi promotes geometric authority and semantic alignment. Its spatially equivariant, object-wise decoder makes position and scale effective commands: changing them moves or resizes the rendered support. Factual position alignment ties position to the decoded center, and normalized attention overlap discourages duplicate allocation. Appearance transplantation aligns geometry semantics across objects, so recipient geometry governs layout while donor appearance supplies shape. On Obj3D, GeoCo-SAVi matches ISA reconstruction, reduces latent-position-to-decoded-centroid error by nearly 90%, and reduces appearance-induced size variation while producing the expected translation and scale responses. On 250 MOVi-C videos, it improves reconstruction and instance grouping over both same-protocol references, and video editing over STAITUS. GeoCo-SAVi transforms explicit geometry into compositional control, making both position and scale more readable and editable. Project page: https://GeoCo-SAVi.github.io/.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 34 references that are in the map. First posted on arXiv 6 Sep 2026.

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References · 7

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019MONet: Unsupervised Scene Decomposition and Representationcited by 392
  3. 2019Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEscited by 117
  4. 2023Leveraging Image Augmentation for Object Manipulation: Towards Interpretable Controllability in Object-Centric Learningcited by 2
  5. 2026Dual-State Slot Attention: Decoupling Appearance and Identity for Video Object-Centric Learningcited by 2
  6. 2026Selective Synergistic Learning for Video Object-Centric Learningcited by 2
  7. 2026Rethinking Object-Centric Representations for Video Dynamics Modelingcited by 2

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