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arxiv: 2606.27332 · v1 · pith:ROA6W6PCnew · submitted 2026-06-25 · 💻 cs.CV

RoPEMover: Depth-Aware Object Relocation via Positional Embeddings

Ipek Oztas , Duygu Ceylan , Aybars Bugra Aksoy , Aysegul Dundar This is my paper

Pith reviewed 2026-06-26 05:17 UTC · model grok-4.3

classification 💻 cs.CV
keywords object relocationdepth-aware RoPEdiffusion transformerspositional embeddingsimage editinggeometry consistencysynthetic data
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The pith

Extending rotary positional embeddings with depth awareness enables geometry-consistent object relocation in single images.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes a method for relocating objects in a single image that maintains overall scene geometry, including proper handling of occlusions, new content generation, and updates to lighting effects. It achieves this by directly editing the positional representations inside diffusion transformers instead of relying on conventional image-editing pipelines. The central move is to take standard 2D rotary positional embeddings and extend them into a depth-aware form that captures three-dimensional spatial relations, allowing controlled and consistent object displacement. Training uses mostly synthetic data plus limited real images through efficient fine-tuning, yet the resulting model keeps object identity intact even under large moves.

Core claim

Rotary positional embeddings define a structured spatial field that can be explicitly manipulated to induce controlled motion. We extend 2D RoPE into a depth-aware formulation that encodes 3D spatial structure, enabling consistent object displacement and scene-aware updates.

What carries the argument

depth-aware rotary positional embeddings that encode 3D spatial structure inside diffusion transformers for direct manipulation of object position

If this is right

  • Large spatial displacements preserve object identity without retraining the underlying diffusion model.
  • Newly revealed image regions receive plausible content that respects the updated scene geometry.
  • Scene-dependent effects such as shadows and reflections update automatically to match the new object placement.
  • State-of-the-art scores are obtained across all reported evaluation metrics on existing object motion benchmarks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same depth-aware RoPE manipulation could be applied to other spatial edits such as viewpoint changes or object insertion.
  • Because the method relies on positional structure rather than pixel-level synthesis, it may transfer to video or 3D-aware generation tasks with only modest additional supervision.
  • The success with mostly synthetic training data suggests that explicit geometric control inside transformers can reduce the need for large real-world paired datasets in editing tasks.

Load-bearing premise

Rotary positional embeddings define a structured spatial field that can be explicitly manipulated via a depth-aware extension to produce controlled, geometry-consistent object motion.

What would settle it

A controlled test on the standard object motion benchmarks in which the depth-aware RoPE manipulation produces inconsistent displacements, broken shadows, or mismatched revealed regions would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.27332 by Aybars Bugra Aksoy, Aysegul Dundar, Duygu Ceylan, Ipek Oztas.

Figure 1
Figure 1. Figure 1: Our method enables realistic object displacement in a single image while correctly handling [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the method: Given an input image, object mask, and a user-specified drag [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: An example source-target pair from our synthetic CLEVR split. One object moves, with all [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparisons with competing methods. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Ablation on training setup Input + Edit Stage 1 Ours Input + Edit Stage 1 Ours [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Identity improvement with second stage training on real captured dataset. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Examples from our captured real-photo dataset. Each row shows the source image, the [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Effect of the warping schedule. Warping is applied for the first [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Additional qualitative comparisons on Object Mover Benchmark A. [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Additional qualitative comparisons on Object Mover Benchmark B. [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Qualitative comparisons against an extended set of methods on Object Mover Benchmarks [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Qualitative comparisons against an extended set of methods on Object Mover Benchmarks [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Qualitative comparisons against an extended set of methods on Object Mover Benchmarks [PITH_FULL_IMAGE:figures/full_fig_p021_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Additional results on two-stage training on Object Mover Benchmark A. [PITH_FULL_IMAGE:figures/full_fig_p022_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Additional results on two-stage training on Object Mover Benchmark B. [PITH_FULL_IMAGE:figures/full_fig_p023_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Component ablation across ObjMove-A and ObjMove-B. Removing depth or the second [PITH_FULL_IMAGE:figures/full_fig_p024_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Target depth synthesis pipeline. Input + Edit Ours (FLUX.1 Kontext) Input + Edit Ours (FLUX.1 Kontext) Input + Edit Ours (FLUX.1 Kontext) [PITH_FULL_IMAGE:figures/full_fig_p025_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Our method generalizes to other DiT-based editing models such as FLUX.1 Kontext Labs [PITH_FULL_IMAGE:figures/full_fig_p025_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Given an image with desired objects copy-pasted on top, our method performs object [PITH_FULL_IMAGE:figures/full_fig_p025_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Instructions shown to participants for completing the user study. [PITH_FULL_IMAGE:figures/full_fig_p026_20.png] view at source ↗
read the original abstract

Moving an object in a single image requires geometry-consistent spatial rearrangement, including handling occlusions, revealing previously unseen regions, and maintaining coherent shadows and reflections. Existing approaches are not well suited to this setting and often fail to preserve such scene-level consistency. We address this problem by introducing a geometry-aware object motion method that operates directly on the positional representations of diffusion transformers. Our key insight is that rotary positional embeddings (RoPE) define a structured spatial field that can be explicitly manipulated to induce controlled motion. We extend 2D RoPE into a depth-aware formulation that encodes 3D spatial structure, enabling consistent object displacement and scene-aware updates. Our model is trained using synthetic data combined with a small set of real images via parameter-efficient fine-tuning. Despite minimal real supervision, it preserves object identity under large spatial displacements, generates plausible content in newly revealed regions, and consistently updates scene-dependent effects such as shadows and illumination. Experimental results on standard object motion benchmarks demonstrate state-of-the-art performance across all evaluation metrics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper introduces RoPEMover, a method for depth-aware object relocation in single images that extends 2D rotary positional embeddings (RoPE) into a 3D-aware formulation. It operates directly on the positional representations inside diffusion transformers to enable geometry-consistent object displacement, occlusion handling, and scene-aware updates to effects such as shadows and illumination. The model is trained primarily on synthetic data with parameter-efficient fine-tuning on a small set of real images and is reported to preserve object identity under large displacements while achieving state-of-the-art results on standard object motion benchmarks.

Significance. If the depth-aware RoPE manipulation produces the claimed geometry-consistent motion without introducing new free parameters or circular definitions, the approach could offer an efficient, embedding-centric alternative to explicit 3D reconstruction or heavy supervision for image editing tasks in diffusion models.

major comments (2)
  1. [Abstract / method description] The abstract asserts that the depth-aware RoPE extension 'encodes 3D spatial structure' and enables 'consistent object displacement,' yet no equations, pseudocode, or derivation of the extension (e.g., how depth is injected into the rotary angles or how the embedding field is updated for relocation) are supplied, leaving the central mechanistic claim untestable.
  2. [Abstract / experimental results] The claim of 'state-of-the-art performance across all evaluation metrics' on 'standard object motion benchmarks' is presented without any reported numbers, baselines, ablation tables, or metric definitions, which is load-bearing for the experimental validation of the method.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed comments. The full manuscript contains the method derivation and quantitative results; the abstract serves as a concise overview. We respond point-by-point to the major comments below.

read point-by-point responses

  1. Referee: [Abstract / method description] The abstract asserts that the depth-aware RoPE extension 'encodes 3D spatial structure' and enables 'consistent object displacement,' yet no equations, pseudocode, or derivation of the extension (e.g., how depth is injected into the rotary angles or how the embedding field is updated for relocation) are supplied, leaving the central mechanistic claim untestable.

    Authors: The full manuscript supplies the equations, pseudocode, and derivation in Section 3, including the precise formulation for injecting depth into the rotary angles and the update rule for the embedding field under relocation. The abstract is length-constrained and therefore omits equations, but the mechanistic claim is fully specified and testable from the paper. We will revise the abstract to add a brief clause referencing the depth-injection mechanism. revision: yes

  2. Referee: [Abstract / experimental results] The claim of 'state-of-the-art performance across all evaluation metrics' on 'standard object motion benchmarks' is presented without any reported numbers, baselines, ablation tables, or metric definitions, which is load-bearing for the experimental validation of the method.

    Authors: The abstract states the SOTA claim at a summary level, as is conventional. Section 4 of the manuscript provides the supporting tables with concrete metric values, baseline comparisons, ablation results, and explicit metric definitions on the standard benchmarks. We will revise the abstract to include a short parenthetical note on the magnitude of the reported gains. revision: partial

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The provided abstract and description present the core contribution as an extension of pre-existing 2D rotary positional embeddings (RoPE) to a depth-aware 3D formulation, with the model trained on synthetic data plus limited real images via parameter-efficient fine-tuning. No equations, fitted parameters, or self-citations are shown that would make any claimed prediction or result equivalent to its inputs by construction. The method is described as operating on and manipulating existing embeddings to achieve geometry-consistent motion, with evaluation on external benchmarks. This leaves the derivation self-contained without reduction to self-definition, fitted-input renaming, or load-bearing self-citation chains.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no information on free parameters, axioms, or invented entities; full paper text was not available for inspection.

pith-pipeline@v0.9.1-grok · 5719 in / 1047 out tokens · 26460 ms · 2026-06-26T05:17:10.231483+00:00 · methodology

discussion (0)

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