Geometry-Editable and Appearance-Preserving Object Compositon

Chunmei Qing; Haojie Li; Jianman Lin; Liang Lin; Tianshui Chen; Zhijing Yang

arxiv: 2505.20914 · v2 · pith:IY3PNYEXnew · submitted 2025-05-27 · 💻 cs.CV

Geometry-Editable and Appearance-Preserving Object Compositon

Jianman Lin , Haojie Li , Chunmei Qing , Zhijing Yang , Liang Lin , Tianshui Chen This is my paper

Pith reviewed 2026-05-22 02:09 UTC · model grok-4.3

classification 💻 cs.CV

keywords object compositiondiffusion modelgeometry editingappearance preservationcross attentionsemantic embeddingimage synthesis

0 comments

The pith

A disentangled diffusion model uses semantic embeddings for geometry edits and cross-attention to preserve appearance details in object composition.

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

This paper develops a method for inserting objects into images with desired shapes and positions while keeping their original look intact. It addresses the problem that compact semantic descriptions lose important visual details. The solution separates geometry handling, which uses embeddings to guide transformations in a diffusion model, from appearance handling, which retrieves and aligns detailed features using cross-attention. If successful, this would allow more accurate and flexible image editing tools for creating composite scenes without artifacts.

Core claim

The paper claims that by first integrating semantic embeddings into pre-trained diffusion models to implicitly capture geometric transformations and then applying a dense cross-attention retrieval mechanism to align fine-grained appearance features with the edited geometry, the DGAD model achieves both precise geometry editing and faithful appearance preservation in general object composition tasks.

What carries the argument

The Disentangled Geometry-editable and Appearance-preserving Diffusion (DGAD) model, which builds semantic embeddings and appearance representations from CLIP/DINO and reference networks, then integrates them disentangled into diffusion encoding and decoding with cross-attention for alignment.

Load-bearing premise

Semantic embeddings from models like CLIP and DINO can sufficiently encode object geometry for manipulation in diffusion processes, and cross-attention can then perfectly match appearance features to those edited regions.

What would settle it

Generate composites where an object is rotated or resized significantly, then compare the output's fine details such as specific patterns or edges against the input reference; any systematic loss or distortion in those details would indicate failure of the preservation mechanism.

Figures

Figures reproduced from arXiv: 2505.20914 by Chunmei Qing, Haojie Li, Jianman Lin, Liang Lin, Tianshui Chen, Zhijing Yang.

**Figure 2.** Figure 2: The training process of the proposed Disentangled Geometry-editable and Appearance [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Qualitative comparison with recent advanced methods. The results show that the proposed [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Left half: Visualization of the implicitly captured geometric properties of the object. Right half: Qualitative comparisons of “Ours w/o dense CA” and “Ours”, Using dense cross-attention effectively retrieves accurate appearance features, thereby promoting appearance preservation. 4.5 Ablation Study 4.5.1 Ablation of the Geometry-Editable Encoder Ablation of Geometric Prior . In Section 3.1, we introduce a… view at source ↗

**Figure 5.** Figure 5: Qualitative comparison with recent advanced methods. The images shown are sampled [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗

**Figure 6.** Figure 6: Qualitative comparison with recent advanced methods. The images shown are sampled [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗

**Figure 7.** Figure 7: Qualitative comparison with recent advanced methods. The images shown are sourced from [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗

read the original abstract

General object composition (GOC) aims to seamlessly integrate a target object into a background scene with desired geometric properties, while simultaneously preserving its fine-grained appearance details. Recent approaches derive semantic embeddings and integrate them into advanced diffusion models to enable geometry-editable generation. However, these highly compact embeddings encode only high-level semantic cues and inevitably discard fine-grained appearance details. We introduce a Disentangled Geometry-editable and Appearance-preserving Diffusion (DGAD) model that first leverages semantic embeddings to implicitly capture the desired geometric transformations and then employs a cross-attention retrieval mechanism to align fine-grained appearance features with the geometry-edited representation, facilitating both precise geometry editing and faithful appearance preservation in object composition. Specifically, DGAD builds on CLIP/DINO-derived and reference networks to extract semantic embeddings and appearance-preserving representations, which are then seamlessly integrated into the encoding and decoding pipelines in a disentangled manner. We first integrate the semantic embeddings into pre-trained diffusion models that exhibit strong spatial reasoning capabilities to implicitly capture object geometry, thereby facilitating flexible object manipulation and ensuring effective editability. Then, we design a dense cross-attention mechanism that leverages the implicitly learned object geometry to retrieve and spatially align appearance features with their corresponding regions, ensuring faithful appearance consistency. Extensive experiments on public benchmarks demonstrate the effectiveness of the proposed DGAD framework.

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.

Desk Editor's Note private letter to a colleague

DGAD adds a disentangled semantic-geometry plus cross-attention path on top of existing diffusion models for object composition, but the key claim rests on an untested assumption about implicit geometry capture.

read the letter

The paper introduces DGAD, which first feeds CLIP or DINO semantic embeddings into a pre-trained diffusion backbone to handle geometry edits implicitly, then uses dense cross-attention to retrieve and align fine-grained appearance features from a reference. This is the concrete new piece: a two-stage disentanglement that tries to avoid the detail loss that comes with compact embeddings alone. The approach reuses strong pre-trained components and claims better results on standard benchmarks for controllable object placement in generated scenes. That reuse is a practical strength and keeps the method straightforward to implement on top of current diffusion pipelines. The logic of separating the geometry path from the appearance retrieval step is clear and directly targets a known limitation in prior semantic-embedding work. The abstract and method sketch make a reasonable case that the spatial reasoning already present in frozen U-Nets can be leveraged this way. The soft spot is exactly the one the stress-test flags. The method depends on those high-level semantic tokens being sufficient to drive precise geometric transformations without explicit pose, keypoint, or deformation signals, and on the subsequent cross-attention map being able to register appearance tokens accurately. The provided description gives no isolated ablation of the implicit-geometry step, no direct measure of how well the latent geometry matches ground-truth edits, and no failure-case analysis for out-of-distribution manipulations. If that assumption is only partially true, both editability and appearance fidelity will suffer. This is aimed at computer-vision researchers building generative editing tools rather than at theorists. Readers who need incremental improvements for controllable composition will find the framework and claimed experiments useful. It deserves a serious referee because the problem is well-motivated, the proposed fix is concrete, and the experiments are said to be extensive; the review process can check whether the quantitative results and ablations actually support the disentanglement claim. I would send it to peer review rather than desk-reject it.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces the Disentangled Geometry-editable and Appearance-preserving Diffusion (DGAD) model for general object composition. It first integrates CLIP/DINO-derived semantic embeddings into pre-trained diffusion models to implicitly capture desired geometric transformations for flexible editing, then applies a dense cross-attention retrieval mechanism to align and preserve fine-grained appearance features from reference images, with the components integrated in a disentangled manner into the diffusion encoding and decoding pipelines. Extensive experiments on public benchmarks are claimed to demonstrate effectiveness.

Significance. If the implicit geometry capture and cross-attention alignment prove reliable, the DGAD approach could meaningfully advance object composition methods by addressing the loss of fine-grained details in compact semantic embeddings while leveraging the spatial reasoning of frozen diffusion backbones, potentially enabling more controllable and faithful edits without explicit geometric supervision.

major comments (2)

[§3.2 and Fig. 2] §3.2 and Fig. 2: The headline claim of disentangled geometry editing rests on the assumption that injecting compact CLIP/DINO semantic embeddings into the pre-trained U-Net implicitly captures precise object geometry sufficiently for controllable manipulation. No ablation isolating this implicit-geometry step is described, nor is there a quantitative evaluation (e.g., geometric accuracy metrics such as keypoint error or transformation fidelity against ground-truth edits) to verify that the latent representation matches desired transformations. This is load-bearing for both the editability and disentanglement arguments.
[Method and Experiments sections] Method and Experiments sections: The dense cross-attention retrieval is presented as ensuring faithful appearance consistency by spatially aligning features conditioned on the implicit geometry. However, the manuscript provides no failure-case analysis or robustness metrics for out-of-distribution geometric edits, leaving open the possibility of misalignment or loss that would undermine the appearance-preservation claim.

minor comments (1)

The abstract and method overview could more explicitly contrast the proposed disentangled integration against prior semantic-embedding approaches to highlight the precise novelty in the cross-attention retrieval step.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our DGAD framework for object composition. We address each major comment below with clarifications from the manuscript and indicate planned revisions to strengthen the presentation of the implicit geometry capture and robustness aspects.

read point-by-point responses

Referee: [§3.2 and Fig. 2] §3.2 and Fig. 2: The headline claim of disentangled geometry editing rests on the assumption that injecting compact CLIP/DINO semantic embeddings into the pre-trained U-Net implicitly captures precise object geometry sufficiently for controllable manipulation. No ablation isolating this implicit-geometry step is described, nor is there a quantitative evaluation (e.g., geometric accuracy metrics such as keypoint error or transformation fidelity against ground-truth edits) to verify that the latent representation matches desired transformations. This is load-bearing for both the editability and disentanglement arguments.

Authors: We agree that an explicit ablation isolating the contribution of the semantic embeddings to geometry capture, along with quantitative metrics such as keypoint error or transformation fidelity, would provide stronger validation for the implicit mechanism described in §3.2. While the manuscript relies on the spatial reasoning capabilities of the frozen pre-trained diffusion backbone to enable the observed editability (as shown through the overall framework results and Fig. 2), we did not include a dedicated isolation study or those specific geometric accuracy metrics. We will add such an ablation and quantitative evaluation in the revised manuscript to better support the disentanglement claims. revision: yes
Referee: [Method and Experiments sections] Method and Experiments sections: The dense cross-attention retrieval is presented as ensuring faithful appearance consistency by spatially aligning features conditioned on the implicit geometry. However, the manuscript provides no failure-case analysis or robustness metrics for out-of-distribution geometric edits, leaving open the possibility of misalignment or loss that would undermine the appearance-preservation claim.

Authors: We acknowledge that including failure-case analysis and robustness metrics specifically for out-of-distribution geometric edits would better substantiate the reliability of the dense cross-attention retrieval mechanism. The current experiments on public benchmarks demonstrate appearance preservation through spatial alignment conditioned on the learned geometry, but we did not provide dedicated OOD robustness evaluation or failure cases. We will expand the Experiments section to include such analysis and discuss potential limitations for extreme edits in the revision. revision: yes

Circularity Check

0 steps flagged

No significant circularity; new integration steps on pre-trained models remain independent of inputs

full rationale

The paper proposes DGAD as a constructive architecture: semantic embeddings (from CLIP/DINO) are integrated into a pre-trained diffusion backbone to implicitly capture geometry, followed by a dense cross-attention retrieval step for appearance features. This is presented as a novel disentangled pipeline rather than a derivation that reduces by construction to fitted parameters, self-citations, or renamed inputs. No equations, uniqueness theorems, or load-bearing self-citations are exhibited in the abstract or method outline that would force the claimed editability and preservation to be tautological. The central claims rest on the proposed mechanisms having independent content beyond the pre-trained components, consistent with standard model-building papers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No explicit free parameters, axioms, or invented entities are described in the abstract; the approach relies on pre-trained CLIP/DINO networks and diffusion models from prior literature.

pith-pipeline@v0.9.0 · 5774 in / 1175 out tokens · 69061 ms · 2026-05-22T02:09:38.739058+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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