PICS: Pairwise Image Compositing with Spatial Interactions
Authors: Hang Zhou
Xinxin Zuo
Sen Wang
Li cheng
Venue: ICLR 2026 Poster (2026)
Keywords: image compositing
diffusion model
spatial relations
Primary area: generative models
TL;DR: A pairwise image compositing model that maintains spatial coherence and realistic object interactions.

Abstract: Despite strong single-turn performance, diffusion-based image compositing often struggles to preserve coherent spatial relations in pairwise or sequential edits, where subsequent insertions may overwrite previously generated content and disrupt physical consistency. We introduce PICS, a self-supervised composition-by-decomposition paradigm that composes objects in parallel while explicitly modeling the compositional interactions among (fully-/partially-)visible objects and background. At its core, an Interaction Transformer employs mask-guided Mixture-of-Experts to route background, exclusive, and overlap regions to dedicated experts, with an adaptive $\alpha$-blending strategy that infers a compatibility-aware fusion of overlapping objects while preserving boundary fidelity. To further enhance robustness to geometric variations, we incorporate geometry-aware augmentations covering both out-of-plane and in-plane pose changes of objects. Our method delivers superior pairwise compositing quality and substantially improved stability, with extensive evaluations across virtual try-on, indoor, and street scene settings showing consistent gains over state-of-the-art baselines. Code and data are available at https://github.com/RyanHangZhou/PICS

Published as a conference paper at ICLR 2026
PICS: PAIRWISE IMAGE COMPOSITING WITH SPATIAL INTERACTIONS
Hang Zhou
Xinxin Zuo
Sen Wang Concordia University
Li Cheng*
University of Alberta
Support
Containment
Occlusion
Deformation
Figure 1: Our method generates spatially plausible and visually realistic pairwise compositions. Each row illustrates two examples, consisting of (from left to right) the objects, the masked back- ground, and two exemplar composite results. Additional comparative results appear in the appendix.
ABSTRACT
Despite strong single-turn performance, diffusion-based image compositing of- ten struggles to preserve coherent spatial relations in pairwise sequential ed- its, where subsequent insertions may overwrite previously generated content and disrupt physical consistency. We introduce PICS, self-supervised composition- by-decomposition paradigm that composes objects in parallel while explicitly modeling the compositional interactions among fullly-pprriaally-vvsbbb objects and background. At its core, an Interaction Transformer employs mask-guided Mixture-of- Expexss to route background, exclusive, and overlap regions ded- icated experts, with an adaptive a-blending strategy that infers compatibility- aware fusion overlapping objects while preserving boundary fidelity. To further enhance robustness to geometric variations, we incorporate geometry-aware aug- mentations covering both out-of-plane and in- in-plane pose changes of objects. Our method delivers superior pairwise compositing quality and substantially improved stability, with extensive evaluations across virtual try-on, indoor, and street scene settings showing consistent gains state-of-the-an thet-rrt baselines. Code and data are available at /attps://githbb com hww.://wwww...aaaaaaaaaaaa/////a
INTRODUCTION
The purpose image compositing is to seamlessly integrate objects or regions, sourced from differ- images, into unified and visually plausible image. This fundamental task has recently gamered

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Inputs
PbE
ControlCom ObjectStitch
AnyDoor
FreeCompose OmniPaint InsertAnything
Ours
Figure 2: Visual comparison of pairwise support relations across Paint-by-Paing ControlCom, Ob- jectStitch, AnyDoor, FreeCompose, OmniPaint and InsertAnything. Left: backgrounds and two ob- jects; right: compositing results. The first row shows composites with the basket, and the second row shows subsequent composites obtained by adding the bread on top. Unlike prior methods that suf- fer from contact artifacts and fidelity loss, our approach performs parallel compositing, effectively handling spatial occlusions and yielding consistent results with preserved fine- tne-grained structure.
considerable attention, particularly film production and photo retouching, where facilitates the seamless blending of diverse visual elements (Mortensen & Barrett 1995). In the film industry, advanced compositing techniques, often coupled with digital manipulation, enable the realistic inte- gration of vintage footage into modern scenes (Brinkmann, 2008; Wright 2017).
Earlier compositing methods, including image blending (Smith & Blinn 1996; Pérez et al. 2003 harmonization (Tsa et al.) 2017, Guerreiro et al.) (202)) and GAN-based models (Chen & Kac 2019 Azadi et al. 2020), refine the appearance of inserted regions but generalize poorly across diverse backgrounds. Recent diffusion models (Ho et al., 2020 Dhariwal & Nicholl 2021 offer stronger generative capability and flexible conditioning, substantially advancing image compositing. Building this, several approaches encode objects as visual prompts, enabling diffusion-based
compositing across varied contexts (Song et al. 2023 Yang et al. 2023 3 Lu al.) 2023 Chen et al,,
2024b Song et al. 2024 Canet Tarrés et al. 2024 Chen et al., 2024c 2025 Tian al.) 2025 Yu
et al. 2025 Song et al. 2026). Despite these gains, such methods remain vulnerable in multi-turr
settings: sequential compositing often disrupt prior content, degrading compositional consistency and visual fidelity, as shown in Figure particularly regarding realistic object interactions.
We posit that instability in multi-ture turlti-turrnnn compositing arises from the lack of explicit modeling of object- object interactions. In real-world scenes, objects rarely occur in isolation; fundamental pairwise re- lations such as support (Jiang et al. 20122), containment (Shamsian al. 2020), occlusion (Lazarow et al. 2020). and deformation (Romero et al.) 2022) structure spatial plausibility. These relations define the basic unit for compositional reasoning (Patel et al., 2024| Mishra et al. (025). enabling systematic evaluation of the limitations of existing diffusion-basee compositing methods.
To address these challenges, we introduce PICS, a parallel image compositing model that performs pairwise compositing in single pass while preserving both object-object and object-backgrone consistency. Built on a latent diffusion backbone with ControlNet conditioning on the masked background, PICS employs Interaction Transformer Blocks with mask- guideed Mixtare-of- Exppers (MoE): background, per-object exclusive regions, and overlaps are deterministically routed to ded- icated experts. The background expert is identity-preseringg attention from scene to individual object; and the overlap expert employs an adaptive attention-get -attd a-blending strategy that dynamically mediates object presence conditioned on background, yielding spatially coherent interactions in the intersection region. Additionally, we incorporate geometry- aware augmentations to handle both in plane and out-of-plan pose variations of objects.
exclusive-region experts apply cross-
Our contributions are summarized as follows:
Parallel Compositing. By modeling pairwise image compositing in parallel, our approach effec- tively avoids the artifacts inherent to step-wise compositing.
Interaction Transformer Block. We propose mask-guided Mfxture-of for region-aware modeling, together with an adaptive a-blending module that achieves boundary-consistent and spatially coherent pairwise composites.
We use "turn" denote composition editing round, avoid confusion with diffusion sampling steps.
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Comprehensive Evaluation. Extensive experiments demonstrate that PICS significantly improves pairwise compositing quality across various scenarios.
2 RELATED WORK
Image compositing. Image compositing is the task of inserting an image-based object into a back- ground image while maintaining visual and contextual consistency. Early approaches fall into three categories: image blending, which focuses smoothing boundaries for seamless transition be- tween the inserted object and the background (Smith & Blinn 1996. Pérez et al., 2003), image harmonization, which adjusts color and illumination achieve visual compatibility (Tsai et al.,
2017
Guerreiro et al., [023 Chen et al., 2024c), and GAN-based models (Chen & Kae 2019: Zhan 2019; Azadi al. 2020), which targets geometry consistency by adversarial training. Re-
al.
cent work represents objects as visual prompts and conditions diffusion models (Ho et al.
2020
Dhariwal & Nichol 2021) to support more general, adaptive compositing (Song al. 2023
Yang
ct al.
2023; Lu ct al. 2023; Chen et al. 2024b; Song et al. 2024 Canet Tarrés et al. 2024 Chen
al.
2024c| 2025 lan et al. 2025] Yu et al. 2025| Song et al., 2026). Despite these advances,
most frameworks remain essentially single-turn: each composite is generated from single prompt,
without support for iterative composition. In complex scenes where multiple objects are added se-
quentially and may overlap or contact (Zhan et al., 2024| Ao et al. 2025 Liu et al. 2025), models pairs often produce artifacts, especially near overlaps and
trained only on seegrrundd-bcckroowe contacts, due to the absence of explicit object-object relation modeling. A related line, multi image customization, personalizes images with multiple objects by jointly generating foreground and background layouts simultaneously (Bao et al., 2024 Chefer al., 2023) Dahary et al., 2024 Gu et al. 2023 Wang et al., 2024). In contrast, we directly target pairwise object compositing, yielding spatially coherent and visually faithful composition.
Multi-turn image editing. Diffusion models have significantly advanced image editing, produc- ing results that are both realistic and diverse. However, most methods operate in a single-turn regime: each edit generated from an isolated prompt without carrying state across rounds (Saharia et al., 2022 Chen et al. 2224a Cai et al. 2025). As result, they preserve local fidelity but struggle to maintain global coherence over sequence of edits. To address this, recent work introduces multi-
turn editing that conditions each round on prior outputs and instructions (Zhou et al. [025 Gupta
ctt al. 2025 Avrahami et al. 2025). which aligns with our setting: each new instruction must respect previously composed content and preserve cross-turn consistency. This dependency on past edits, in turn, makes the task prone to error propagation and semantic drift, an issue analogous to multi-turn dialogue in language models (Wang et al.) 2018: Kwan al., 2024 Duan al., 2024 Laban al., 2025). Notably, sustaining coherence in practice hinges on how compositions handle pairwise ob- ject interactions. Without explicit modeling, methods that perform well in single-turn settings often fail to manage occlusions and preserve boundary consistency, as illustrated in Figure 5
Projected object relations. A 3D scene encodes rich spatial relations among objects that, once rendered to 2D, appear as projected interactions between instances. For example, 2D occlusion arises from 3D depth ordering, while support and containment persist through contact and enclo- sure cues. Prior work has leveraged such projected relations for scene understanding, composi- tional reasoning, and image synthesis. Building on this perspective, study how explicitly mod- eling these relations yields more realistic and spatially consistent 2D object compositing. Rea- soning about occluded objects is long-standing challenge in spatial understanding. Occlusion- annotated datasets (Martin et al., 2001; Zhu et al., 2017: Zhan et al.) 2024) and self-supervised approaches (Zhan et al. 2020) establish the foundations for occlusion handling from perceptual standpoint, and subsequent methods further advance amodal completion/de-ocee
(Ling et al.) 2020 Ke al. 202T Zhou al.) 202T Liu et al., 2024). On the generative side, LaRender (Zhan & Liu 2025) introduces explicit occlusion control in image generation.
3
METHODOLOGY
We begin with the parallel pairwise image compositing pipeline in Subsection 3.1 the interaction transformer in Subsection 3.2 that models interactions among objects and the back- ground. Finally, we introduce two geometry-awan augmentations in Subsection 3.3
followed by
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(a) Interaction Diffusion Network
Shape Encoder
m
Denoising Network
Decoder
Xbg
VAE Encoder
Zug
ControlNnt
Cross
Attn
Self
Router
Expert Block
Cross Attn
Cross
Atin
Attin
Weights
Sim
Sim
Linear
alpha- Upha-blend
Down
sample
Spatial gaate
proj
x blocks
Attn
(b) Interaction Transformer Block
(c) Expert Blocks
Figure 3: Overview of PICS. Input data constructed by decomposing the target image into background and pairwise objects with their designated regions. (a) The interaction diffusion network composites the objects into the background. (b) The interaction transformer block. shared across both branches, models interactions among objects and with the background. (c) Expert blocks focus on distinct spatial regions. All notations are defined in the main text for clarity.
3.1 PAIRWISE IMAGE COMPOSITING
Exploring two-turn compositing. Object-to-object contact is a pervasive phenomenon in the physical world. When 3D scene projected onto 2D image, objects tend (partially-)oceludde each other, leading to what we term interdependent objects. This poses central challenge for pair- wise image compositing: how can synthesized images with occlusions remain visually realistic, and to what extent do existing methods effectively model such occlusion and spatial interactions? To investigate this, we systematically examine the strengths and weaknesses of current single-object compositing approaches when extended to scenarios where inserted objects interact spatially.
straightforward baseline composes objects sequence. For the compositing order, we adopt the classical Painter's Algorithm (Newell et al., 1972): objects are ranked by depth proxy, estimated from the vertical position of their 2D bounding boxes, and composited farther first, nearer last, ensuring that later insertions occlude earlier ones. In Figure 2 interaction boundaries, largely due to bureground-backgrouu ignores cross-one object contacts. While adequate for single-object compositing, this bias makes the first insertion two-turn compositing prone to being interpreted as background, causing partial removal, distortion, over-blending, and inconsistent interactions with the subsequent compositing object.
existing methods often degrade at partitioning in data construction that
Parallel image-prompted compositing. Building on these observations, we adopt a parallel strat- egy that simultaneously composes pairwise objects into the background, thereby preserving realistic interactions among objects and with the background. To explicitly distinguish overlap and exclusive regions, construct the following masks from two object segments (xp)pe(a,)) with binary masks (m,,pe(a.6) representing the bounding boxes of the objects:
m, = m. Vm,.
Mab
m. Am,,
m
m, A( (11-ma) m2),
m m A(, (1-mmm))))) (I)
The masked background is obtained by erasing pixels covered by the union mask,
Xbg (1 m.)
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As illustrated in Figure 3 a), our parallel compositing model Fe takes Xby together with the objects and their masks to produce = Fo(xs {xp}, , (1))(mmp)))) [mp]). Following latent diffusion models, each object segment Xp and the background Xhg encoded into latent codes:
Cp = Eshupe (Xp); Zbg = E P {a.b),
(3)
which are then fused via cross-attention so that Zog conditioned on {cp}; the updates are spa- tially guided by m m compositing, we train Fg with a self-supervised recomposition objective to reconstruct image x.
and Mad, detailed in Subsection 3.2 Following prior single-object
3.2 INTERACTION TRANSFORMER
As illustrated in Figure Bb) each interaction transformer block applies self-attention capture global dependencies, then employs a mask-guided cixture-of-Experts (MoE) to route background, exclusive. and overlap regions to dedicated experts. Their outputs are gated by partition masks, merged through a residual update, and refined with feed-forward network (FFN), ensuring spatially grounded, region- region-consistent updates across the image.
Feature-space routing masks. Object masks are originally defined in image space, while our computations are performed in feature space. We therefore downsample masks to the feature-map resolution using bilinear interpolation,
m DH,w(m)
(4)
where H, W denote the spatial dimensions at each layer. From these, we obtain background masks myy 1 m. exclusive masks m m and overlap masks mab-
Spatially-awaid Mixture-of-Expee of-peersss... Given features z'-1 and masks, the MoE applies region- specific experts to the same input and aggregates their outputs residually to yield z'. Here fq. JK, Jv denote xx1 projections for attention. Figure ((c) illustrates the structure of each expert block.
Background expert. The background left unchanged, i.e., hāg = =2 1-1
Exclusive-region experts. For non-overlgpping regions of object p, inject object-specific appear- ance by cross-attending background queries to object codes:
hp = CrossAttn fz fx(cp), fv(c,)), e (a,
(5)
with the updates upplied under the mask m...
Overlap expert. In overlap regions, directly fusing two object codes with an MLP may cause blurred boundaries or inconsistent dominance. To overcome this, we introduce an attention- -ated expert that adaptively favors either object, or their blend, conditioned on the background context.
We first construct gating query from the background code:
4 9g(zz-1),
where do is 1x1 1 projection analogous fq- This query acts position wise referee, deciding at each spatial location whether object a or should dominate.
Each object code is then aggregated into the background space via attention:
C = CrossAttn(qg. fx fx(c,), fv (c()),, P (a.
yielding čp., a loc-tocation summary of how object p aligns with the background query.
To determine the context-conditioned preference, we first score how well each aggregated object code matches the gating query and then convert the two scores into a mixing weight:
(qg. Cp) Vd
Sp
ca/T e%6/T
(8)
Here d= dim(q,) and 7 > 0 is a temperature controlling the sharpness the selection; the gating query thus favors the object whose aggregated code best explains the local observation.
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The pairwise context is then obtained by adaptive a-blending of the aggregated object codes,
Cab Ca - (1-a) Co.
providing position-we wwise compatibility representation of both objects while preserving boundaries. Finally, we inject this context into the background code through cross-attention:
hab CrossAttulffq22 (z"=>, (Cas), fv((ca)):
(10)
and use has as the overlap expert output, yielding an order-agnostic, attention-based mechanism that adaptively selects between objects while enforcing boundary consistency.
A key property of this design is that the gating query Og carries learned occlusion semantics rather than appearance cues, as it is derived from the deep background representation z'-1, Consequently, the gating in Equation (8) performs context-guided, pairwise arbitration between the two objects: the softmax jointly normalizes their responses, inducing an implicit object-object interaction that determines which object should dominate at each spatial location.
Region-gated updates and aggregation.
Expert outputs are masked by corresponding regions:
Azog
mag
bag
AZp = m @ he: PE ().
Azor Mab @ hab-
(11)
The regional updates are then aggregated and added residually:
Azug
Aza
+
Azb
Azoes
z' =zz''1 z''1 + Az,
(12)
after which an FFN refines z' before it is passed to the next block.
3.3 AUGMENTATIONS
Robust compositing requires handling both out-of-plane viewpoint changes and in-plane rotations. We adopt two geometry-ave aware augmentations during training.
Multi-view shape prior. To capture viewpoint variations beyond standard 2D augmentations, we employ an off-the-shelf single-view reconstruction model to render K auxiliary views. Each view is encoded by frozen shape encoder Eshape into latent codes {ct 1 K ===1 These codes are randomly permuted, concatenated, normalized, and fused with a lightweight MLP V:
D), producing a compact multi-view descriptor that phape-preerriig.
Pp (LN((C :xx D)),,
(13)
In-plane rotation. To improve robustness against in-plane misalignment, we apply random rota- tions 0 ~ U(-T/66 7/6, /6) to object images and their masks, and encode them with Eshape- This enhances alignment with background context and increases robustness to in in-plane transformations.
4 EXPERIMENTS
Datasets. PICS is trained on mixture of image datasets. For validating pairwise recompositing, we use the LVIS benchmark, and for testing, we adopt DreamBooth (Ruiz et al. 2023) together with a set of in-the-wild images. Comprehensive deseriptions of the datasets, as well as implementation details including network architecture, training and inference settings, are provided in the appendix.
Evaluation metrics. We evaluate recompositing quality both on the entire images and on bound-
ing box intersection regions using PSNR, SSIM, and LPIPS. To further assess the realism of the generated images, we employ CLIP-Score (Hessel et al. 2021), DINOV2-Score (Oquab et al. 2024). and DreamSim (Fu et al., 2023). For the image compositing task, we specifically adopt Ccore, DINOv2-Score, and DreamSim for evaluating the compositing quality.

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Table 1: Quantitative comparison of object recompositing against prior methods on the LVIS vali-
dation set. The prefix "m-" indicates evaluation restricted to the intersection regions. Bold numbers denote the best performance, and underlined numbers indicate the second best.
Method
mPSNR
mSSIM
mL.PPP
PSNR
FID
LPIPS
CLIP-score
DINOV2-score
DreamSim J
PbB 23)
0.4220
0.4896
ControlCom (arXiv 23)
11.82
0.3185
0.3988
17.61
25.93
0.3375
85.39
0.5264
0.32:1
ObjectStitch (CVPR 23)
10.84
0.3477
0.4203
16.35
23.68
0.3572
85,01
0.5574
0.3458
AnyDoer (CVPR224)
0.2520
OmniPaint (ICCV225)
12.20
0.3096
0.1618
16.09
20.25
0.3512
53.11
0.5673
0.2771
PICS (ours)
13.88
0.5823
0.3221
18.27
24.99
0.2530
85.25
0.5713
0.2659
Objects Source images
Backgrounds
Paintby-Exampla
ControlCom
ObjectSilich
AnyDoor
OmniPaint
Qurs
Figure 4: Qualitative comparison on the LVIS validation set. Source images, backgrounds, and the two decomposed objects are shown on the left. On the right are the recompositing results from different methods. Our approach is the only that produces composites with realistic spatial interactions between scene objects while maintaining scene consistency and object identity.
4.1 OBJECT RECOMPOSITING
Qualitative comparison. Object recompositing refers to compositing objects and backgrounds from same source image, which serves as our evaluation setting. We compare our method with five prior approaches, namely РbE (Yang al. (023), ControlCom (Zhang et al. (003). Object- Stitch (Song et al.) 2023). AnyDoor (Chen et al. 20246) and OmniPaint (Yu et al. 2025). The baselines adopt two-step compositing protocol, where the red region is placed first and the green region second, whereas our method performs parallel pairwise compositing. As illustrated in Fig- ure 4 existing methods primarily designed for single-object compositing struggle generate clear features in occluded regions. While the objects may appear harmonized with the background, these methods often fail handle occlusion order correctly and may introduce artifacts by improperly lay- ering one object over another. In contrast, PICS consistently generates recompositions that preserve object identity while maintaining coherent and plausible connectivity across interacting regions.
Quantitative comparison. As reported in Table I our method delivers consistent improvements over competing approaches in PSNR, SSIM, FID, and LPIPS, including evaluations on intersection regions, demonstrating its ability to faithfully capture the data distribution. While AnyDoor achieves slightly higher D2 scores, this advantage is partly attributable to its use of additional edge maps as input, which aids semantic preservation but limits flexibility when the structural alignment between the input and background is poor, often resulting in inferior scene consistency.

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Objects
Backgrounds
ObjeciSlitch
ObjeciSlitch
AnyDaor
AnyDoor
Ours
Figure 5: Qualitative comparison of different composition orders on the DreamBooth test set. Left: backgrounds and two objects. Right: results from different methods. Our approach better preserves natural contacts and occlusions, while implicitly learning the correct occlusion order.
Table 2: Quantitative comparison of pairwise object com- positing on the DreamBooth testing set. Bold numbers de- note the best performance, and underlined numbers indi- cate the second best.
Table 3: User study (%). "Quality", "Fidclity", and "Consisteney" evaluate image realism, identity preservation, and object coherence, respectively.
Method
FID
CLIP-score DINOV2-semm
DreamSin
Method
Quality
Fidelity 1 Consistency
PbE (CVPR'22) 23)
202.1
51.96
0.2383
0.4521
PbE
5.13
2.53
8.70
ControlCon (arXiv 23)
273
52.35
0.2414
0.3194
ControCoom
12.2
15.2
13.0
SrjectSriich (CVPPR
11.36
0.3213
ObjeaSüisch
AnyDoor
274.1
0.3401
AnyDoar
FreeComper Compoee (ECCV 224)
200.8
51.71
0.2157
0.3821
Compose
2.56
1.27
4.35
OmniPaint (ICCV'25)
260.4
50.32
0.3741
0,2632
OmniPaint
17.3
19.0
10.9
InsertAnychnng (AAAI220
206.0
50.54
0.3612
0.2934
InserAAy thing
16.0
18.4
12.3
(ours)
(ours)
4.2 OBJECT COMPOSITING
Qualitative comparison. We compare our pairwise object compositing results with ObjectStitch and AnyDoor, using their default settings and pretrained models to sequentially compose objects into backgrounds using the DreamBooth testing set. As shown in Figure 5) both ObjectStitch and AnyDoor cxhibit boundary artifacts when a newly inserted object partially occludes a previously composed one. AnyDoor often causes the current object to either completely cover, entangle with, or shrink parts of the previous composed object, while ObjectStitch struggles to preserve object identity. In comparison, method produces more caundary-consister compositions.
Quantitative comparison. Table reports the quantitative comparisons across various evaluation metries. Our method achieves the best overall performance. On DreamSim, both AnyDoor and OmniPaint attain higher scores, where AnyDoor leverages high-frequency object features addi- tional guidance, which helps preserve object structure but at the cost of consistent compositing with the background. and OmniPaint, on the other hand, is built upon flow- now-matching FLUX backbone whose generative prior is stronger than standard diffusion.
User study. We conducted a user study with 20 participants to evaluate object compositing qual- ity, focusing on realism, object fidelity, and intersection quality. Using the same objects and back- grounds from the challenging DreamBooth dataset, participants were asked to rank and score results from different methods. As shown in Table 3 approach outperforms prior methods in terms
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Table 4: Ablation study on in-the-wild test set to ver- ify key components our method. "ITB" denotes the interaction transformer block. "SV" single-view, "Rot." the rotation augmentation, "MV" the multi- view augmentation, and "Comb." the combined data.
No.
MLP
ITB
SV
Rot.
MV
LVIS
Comb.
FID
CLiP-seoce
173.1
74.6
Early
Mid
166.2
76.3
Figure 6: across denoising stages. Top: sofa, b b- human; bottom: swapped.
158.2
77.3
151.3
79.1
Otjects
Backgrounds
Ours
Otjects
Backgrounds
Figure 7: 3-object compositing.
Figure 8: 4-object compositing.
of realism and consistency, underscoring its effectiveness. On the fidelity criterion, method performs comparably to AnyDoor, as both leverage the DINOv2 model to encode identity features.
4.3 ABLATION STUDIES
summarzzsss ablations on the in-the-wild test set over three factors: model architecture, geometry- augmentation, and training data scale. In the MLP baseline, the two object codes are concatenated and passed through an ML.P to model their interaction, and the re- sulting representation is then fused with the background via cross-attention. Moving from Setting to Setting 2, replacing the MLP with the proposed interaction transformer block for intersection modeling consistently improves all metrics, reflecting stronger reasoning over inter-object cues in overlapping regions. Introducing geometry-awan augmentations further enhances robustness: in- plane rotation (Setting 3) mitigates misalignment within the image plane, while the multi-vier prior (Setting 4) improves robustness to viewpoint variation. Expanding the training set from LVIS-on -nlly the full M-image collection (Setting 5) provides the most significant gain, improving generaliza- tion to unseen object-background pairings.
Module choices.
a-blending. We evaluate whether the overlap expert learns spatially resolved mixing weights in the multi-scale feature spaces. The coefficient is derived from the score difference As Sa 8h. where positive values favor object o, negative values favor object 6, and values close to zero lead balanced blend, with a ((As/T) (Equation (8) ensuring that As and a vary consistently. Figure shows As under two indexing choices: in the top row, corresponds to the sofa and b to the human, while the bottom row the roles are swapped but their compositing regions remain unchanged. The sign of As is consistently aligned with visibility, being positive where the human remains visible and negative elsewhere, and it reverses accordingly when the indices are exchanged. This confirms that the gating mechanism encodes actual visibility relationships rather than relying on the arbitrary order of inputs, thereby realizing the intended logistic blending at each spatial location. Furthermore, across denoising steps during inference (early, mid, late), the maps evolve progres- sively: they begin coarse and noisy, become spatially decisive mid-way, and ultimately sharpen into fine-grained boundaries, reflecting refinement dynamics characteristic of diffusion models.

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Objects& backgrounds
Composite
Objects backgrounds Composites
Objects
Background
Composiles
Sery pift
Figure 9: Applications. Virtual try-on; novel-view compositing.
Figure 10: Failure cases.
4.4 GENERALIZATION TO MULTI-OBIECT COMPOSITING
To assess the scalability of our approach beyond the pairwise setting, we additionally train two models for 3-object and 4-object compositing using samples constructed entirely from the LVIS dataset. Representative results are shown in Figures 7 and In the 3-object setting, the composite results reflect consistent occlusion ordering and contact relations, and object identities remain well preserved even where multiple masks intersect, indicating that our interaction module distributes appearance features without collapsing fine details. The 4-object setting presents more entangled configurations, including multi-level occlusions. The model remains stable: as shown the bottom example of Figure [ the backpack is almost fully occluded and is correctly omitted in the final composite, indicating that the model respects visibility rather than hallucinating hidden content.
4.5 APPLICATIONS
Virtual try-on. PICS supports pairwise try-on of an upper-body garment and a lower-body gar- ment. As shown in Figure (left), maintains a clean, well-aligned seam between the two garments and handles overlap robustly, avoiding color bleeding and double edges even under moderate pose changes. Additional side-by-side comparisons with recent methods are provided in appendix K.2
Novel-view composition. Our approach also supports novel-view composition, which generates previously unseen viewpoint of an object and harmonizing it with the background to ensure visual coherence; see Figure 9 (right). For example, when the bounding the model correctly generates side view of the elephant. This demonstrates the ability of our framework to capture spatial priors and to contextually compose objects.
is horizontally elongated,
5 CONCLUSION AND OUTLOOK
We presented PICS, parallel paradigm for pairwise image compositing that explicitly models spatial interactions among objects and the background. Central to the method is an Interaction Transformer with mask- mask-guided experts and an adaptive a-blending mechanism that enables region- aware composition with boundary fidelity. Robustness to geometric variation is further improved by geometry-aware augmentations that address both out-of-plane and in-plane pose changes.
While PICS yields high-quality pairwise composites, it naturally extends multi-object scenarios via parallel compositing, enabling sophisticated composition in complex scenes. addition, as shown in Figure I0 observe occasional geometry and texture degradation in extremely cluttered environments, attributable to the limited capacity of the shape encoder. Future work will further optimize the routing and fusion mechanisms to enhance the scalability of multi-object compositing while guaranteeing semantic fidelity under diverse lighting conditions.
Ethics statement. This work is conducted on publicly available datasets and is intended solely for scientific research.
Reproducibility statement. We have made our best effort to ensure reproducibility. including but not limited to: 1) dataset description in appendix A and implementation details in appendix B 2) detailed graphic illustrations of model architectures, training and inference in Figures 14 and 3) source code and checkpoints.
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Acknowledgments. This work partly supported by NSERC Discovery, CFI-JELF, NSERC Alliance, Alberta Innovates and PrairiesCan grants.
REFERENCES
Jiayang Ao, Yanbei Jiang, Qiuhong Ke, and Krista A Ehinger. Open-world amodal appearance completion. In CVPR, 2025.
Omri Avrahami, Or Patashnik, Ohad Fried, Egor Nemchinov, Kfir Aberman, Dani Lischinski, and Daniel Cohen-Or. Stable Flow: Vital layers for training- free image editing. In CVPR, 2025.
Samanch Azadi, Deepak Pathak, Sayna Ebrahimi, and Trevor Darrell. Compositional GAN: Learn- ing image-conditional binary composition. International Journal of Computer Vision, 2020.
Zhipeng Bao, Yijun Li, Krishna Kumar Singh, Yu-Xiong Wang, and Martial Hebert. Separate-and- Enhance: Compositional finetuning for text-to-image diffusion models. In SIGGRAPH, 2024.
Ron Brinkmann. The art and science of digital compositing: Téchniques for visual effects, animation and motion graphics. Morgan Kaufmann, 2008
Shengqu Cai, Eric Ryan Chan, Yunzhi Zhang, Leonidas Guibas, Jiajun Wu, and Gordon Wetzstein. Diffusion self-distillation for zero-shot customized image generation. In CVPR, 2025.
Gemma Canet Tarrés, Zhe Lin, Zhifei Zhang. Jianming Zhang, Yizhi Song, Dan Ruta, Andrew Gilbert, John Collomosse, and Soo Ye Kim. Thinking Outside the BBox: Unconstrained genera- tive object compositing. In ECCV, 2024.
Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, and Daniel Cohen-Or. Attend-and-Fxcite Attention-based semantic guidance for text-to-image diffusion models. ACM TOG, 2023.
Bor-Chun Chen and Andrew Kae. Toward realistic image compositing with adversarial learning. In CVPR, 2019.
Junsong Chen, YU Jincheng, GE Chongjian, Lewei Yao, Enze Xie, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li. PixArt-a: Fast training of diffusion transformer for photorealistic text-to-image synthesis. In ICLR, 2024a.
Xi Chen, Lianghua Huang, Yu Liu, Yujun Shen, Deli Zhao, and Hengshuang Zhao. AnyDoor: Zero-shot object-level image customization. In CVPR, 2024b.
Xi Chen, Zhifei Zhang, He Zhang, Yuqian Zhou, Soo Kim, Qing Liu, Yijun Li, Jianming Zhang, Nanxuan Zhao, Yilin Wang, et al. UniReal: Universal image generation and editing via learning real-world dynamics. CVPR, 2025.
Zhekai Chen, Wen Wang, Zhen Yang, Zeqing Yuan, Hao Chen, and Chunhua Shen. FreeCompose: Generic zero-shot image composition with diffusion prior. In ECCV, 2024c.
Seunghwan Choi, Sunghyun Park, Minsoo Lee, and Jaegul Choo. VITON-HD: High-resolution
virtual try-on via misalignment-aware normalization. In CVPR, 2021.
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The Cityscapes dataset for semantic urban scene understanding. In CVPR, 2016.
Omer Dahary, Or Patashnik, Kfir Aberman, and Daniel Cohen-Or. Be Yourself: Bounded attention for multi-subject text-to-image generation. In ECCV, 2024.
Prafulla Dhariwal and Alexander Nichol. Diffusion models beat GANs on image synthesis. In NeurIPS, 2021.
Haodong Duan, Jueqi Wei, Chonghua Wang, Hongwei Liu, Yixiao Fang. Songyang Zhang, Dahua Lin, and Kai Chen. BotChat: Evaluating LLMs' capabilities of having multi-turn dialogues. In NAACI, 2024.
11

---

Published as a conference paper at ICLR 2026
Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. DreamSim: Learning new dimensions human visual similarity using synthetic data. In NeurIPS, 2023.
Yuchao Gu, Xintao Wang, Jay Zhangjie Wu, Yujun Shi, Yunpeng Chen, Zihan Fan, Wuyou Xiao, Rui Zhao, Shuning Chang, Weijia Wu, et al. Mix-of-Show: Decentralized low-rank adaptation for multi-concept customization of diffusion models. NeurIPS, 2023.
Julian Jorge Andrade Guerreiro, Mitsuru Nakazawa, and Björn Stenger. PCT-Net: Full resolution image harmonization using pixel-wise color transformations. CVPR, 2023.
Advait Gupta, NandaKiran Velaga, Dang Nguyen, and Tianyi Zhou. CoSTA>: Cost-sensitive tool- path agent for multi-turn image editing. arXiv preprint arXiv:2503. 10613, 2025.
Agrim Gupta, Piotr Dollar, and Ross Girshick. LVIS: A dataset for large vocabulary instance seg- mentation. In CVPR, 2019.
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. CLIPScore: A reference-free evaluation metric for image captioning. In EMNLP, 2021.
Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NeurlPS Workshop, 2021.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. NeurIPS, 2020.
Yun Jiang, Changxi Zheng, Marcus Lim, and Ashutosh Saxena. Learning to place new objects. In ICRA, 2012.
Lei Ke, Yu-Wing Tai, and Chi-Keung Tang. Deep occlusion-aware instance segmentation with overlapping bilayers. In CVPR, 2021.
Diederik P Kingma. Adam: A method for stochastic optimization. In ICLR, 2014.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yea Lo, et al. Segment anything. In ICCV, 2023.
Wai-Chung Kwan, Xingshan Zeng. Yuxin Jiang, Yufei Wang, Liangyou Li, Lifeng Shang, Xin Jiang, Qun Liu, and Kam-Fai Wong. MT--Evll: A multi-turn capabilities evaluation benchmark for large language models. In EMNLP, 2024.
Philippe Laban, Hiroaki Hayashi, Yingbo Zhou, and Jennifer Neville. LLMs get lost in multi-turn conversation. arXiv preprint arXiv:2505.0612 2025.
Justin Lazarow, Kwonjoon Lee, Kunyu Shi, and Zhuowen Tu. Learning instance occlusion for panoptic segmentation. In CVPR, 2020.
Huan Ling, David Acuna, Karsten Kreis, Seung Wook Kim, and Sanja Fidler. Variational amodal object completion. NeurlPS, 2020.
Yumeng Liu, Xiaoxiao Long, Zemin Yang, Yuan Liu, Mare Habermann, Christian Theobalt, Yuexin Ma. and Wenping Wang. EasyHOl: Unleashing the power of large models for reconstructing hand-object interactions in the wild. In CVPR, 2025.
Zhengzhe Liu, Qing Liu, Chirui Chang, Jianming Zhang, Daniil Pakhomov, Haitian Zheng, Zhe Lin, Daniel Cohen-Or Or, and Chi- thig Fu. Object-level scene deocclusion. In SIGGRAPH, 2024.
Shilin Lu, Yanzhu Liu, and Adams Wai-Kin Kong. TF-ICON: Diffusion-based training-free cross-
domain image composition. In CVPR, 2023.
David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, 2001.
12

---

Published as a conference paper at ICLR 2026
Samarth Mishra, Kate Saenko, and Venkatesh Saligrama. Enhancing compositional reasoning in lision-language models with synthetic preference data. arXiv preprint arXiv:2504.047400 2025.
Eric Mortensen and William A Barrett. Intelligent scissors for image composition. In SIGGRAPH, 1995.
Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulo, and Peter Kontschieder. The Mapillary Vistas dataset for semantic understanding of street scenes. In ICCV, 2017.
ME Newell, RG Newell, and TL Sancha. A solution to the hidden surface problem. In ACM annual conference, 1972.
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research, 2024.
Maitreya Patel, Naga Sai Abhiram Kusumba. Sheng Cheng, Changhoon Kim, Tejas Gokhale, Chitta Baral, et al. TripletCLIP: Improving compositional reasoning of CLIP via synthetic vision- language negatives. NeurIPS, 2024.
Patrick Pérez, Michel Gangnet, and Andrew Blake. Poisson image editing. In SIGGRAPH, 2003.
Cristian Romero, Dan Casas, Maurizio M Chiaramonte, and Miguel A Otaduy. Contact-centric deformation learning. ACM TOG, 2022.
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. DreamBooth: Fine tuning text-to-image diffusion models for subject-driven generation. CVPR, 2023.
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. NeurIPS, 2022.
Aviv Shamsian, Ofri Kleinfeld, Amir Globerson, and Gal Chechik. Learning object permanence from video. In ECCV, 2020.
Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In ICCV, 2019.
Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, and Hao Su. Zerol 12+0122+:: A single image to consistent multi-view diffusion base model. arXiv preprint arXiv2230..5111 2023.
Alvy Ray Smith and James F Blinn. Blue screen matting, In SIGGRAPH, 1996.
Wensong Song. Hong Jiang, Zongxing Yang, Ruijic Quan, and Yi Yang. Insert Anything: Image insertion via in-context editing in DiT. In AAAI, 2026.
Yizhi Song, Zhifei Zhang, Zhe Lin, Scott Cohen, Brian Price, Jianming Zhang, Soo Ye Kim, and Daniel Aliaga. ObjectStitch: Object compositing with diffusion model. In CVPR, 2023.
Yizhi Song, Zhifei Zhang. Zhe Lin, Scott Cohen, Brian Price, Jianming Zhang, Soo Ye Kim, He Zhang, Wei Xiong, and Daniel Aliaga. IMPRINT: Generative object compositing by learning identity-preserving representation. CVPR, 2024.
Xueyun Tian, Wei Li, Binghing Xu, Yige Yuan, Yuanzhuo Wang, and Huawei Shen. MIGE: A unified framework for multimodal instruction-based image generation and editing. ACM MM, 2025.
Yi-Hsuan Tsai, Xiaohui Shen, Zhe Lin, Kalyan Sunkavalli, Xin Lu, and Ming Hsuan Yang. Deep image harmonization. In CVPR, 2017.
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. GLUE: multi-tass benchmark and analysis platform for natural language understanding. In EMNLP Workshop, 2018.
13

---

Published as a conference paper at ICLR 2026
Wang, Daniil Ostashev, Yuwei Fang, Sergey Tulyakov, and Kfir Aberman. MoA: dixture-of-attention for subject-context disentanglement in personalized image generation. In SIGGRAPH Asia, 2024.
Steve Wright. Digital compositing for film and video: Production Workflows and Techniques. Rout- ledge, 2017.
Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, and Fang Wen. Paint by Example: Exemplar-based image editing with diffusion models. In CVPR, 2023.
Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Mad- havan, and Trevor Darrell. BDD100K: diverse driving dataset for heterogeneous multitask learning. In CVPR, 2020.
Yongsheng Yu, Ziyun Zeng, Haitian Zheng, and Jiebo Luo. OmniPaint: Mastering object-oriented editing via disentangled insertion-removal inpainting. In ICCV, 2025.
Fangneng Zhan, Hongyuan Zhu, and Shijian Lu. Spatial fusion GAN for image synthesis. In CVPR, 2019.
Guanqi Zhan, Chuanxia Zheng, Weidi Xie, and Andrew Zisserman. Amodal ground truth and completion in the wild. In CVPR, 2024.
Xiaohang Zhan and Dingming Liu. LaRender: Training-free occlusion control in image generation via latent rendering. In ICCV, 2025.
Xiaohang Zhan, Xingang Pan, Bo Dai, Ziwei Liu, Dahua Lin, and Chen Change Loy. Self- supervised scene de-occlusion. In CVPR, 2020.
Bo Zhang, Yuxuan Duan, Jun Lan, Yan Hong, Huijia Zhu, Weiqiang Wang, and Li Niu. ControlCom: Controllable image composition using diffusion model. arXiv preprint arXiv:2308. 10040, 2023.
Qiang Zhou, Shiyin Wang, Yitong Wang, Zilong Huang, and Xinggang Wang. Human de-occlusion: Invisible perception and recovery for humans. In CVPR. 2021.
Zijun Zhou, Yingying Deng, Xiangyu He, Weiming Dong, and Fan Tang. Multi-turn consistent image editing. ICCV, 2025.
Yan Zhu, Yuandong Tian, Dimitris Metaxas, and Piotr Dollár. Semantic amodal segmentation. In CVPR, 2017.
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Support
Containment
Occlusion
STOP
Deforation
14
Others
Figure 11: Examples pairwise object relations from the LVIS validation set, with object instances
visualized alongside their bounding boxes.
A DATASET
A. PAIRWISE OBJECTS VISUALIZATION
As discussed in the main paper, pairwise object interactions are ubiquitous and pervasive across di- verse real-world datasets. Here, we present several illustrative examples in Figure TT] For each type of interaction, including support, containment, occlusion, deformation, and an additional category that encompasses less canonical atypical cases, we provide few representative examples with object instances visualized alongside their bounding boxes.
Specifically, support relations include cases such as bear standing on ball, a toy placed on chair, and bouquet supported by vase; containment is exemplified by donuts arranged in a box, fruit in a bowl, and flowers inside a vase; occlusion demonstrated through an elephant obscured by another elephant, and vehicle blocked by another vehicle; deformation is shown by wrinkles formed between overlapping clothes and pants, toys compressed against each other, and a soft bag deformed under weight. The others category includes diverse interactions such dog holding a plate in its mouth, objects leaning against each other, or items stacked closely together, which not easily assigned to the primary relation types. These examples highlight that modeling pair-
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Table Statistics and description of our training datasets for pairwise object compositing.
Training
Type
LVIS (Gupta et al. 2019
34,160
General
VITON-TID Lhi
2023
11,647
Try.on
Objocts365 Snao vi 3U
2019
940,764
Gencral
Citysrapes Corals
Street
Mapillary Vislas (Ncalleld c
2017
603
Street
BDD100K
Yu
1,012
Street
wise object interactions is essential for realistic scene compositing, ensuring consistent boundaries, plausible occlusion, and physically coherent interactions.
A.2 PATRWISE SPATTAL. RELATION
To analyze the spatial relationships between pairwise bounding boxes, we construct single aggregated heatmap over their overlapping regions, as visualized in Figure 12 For each pair of intersecting boxes ran- domly sampled from the LVIS training set (10k sam- ples), we normalize one bounding box to the canonical [0, x [0, heatmap coordinate frame, and project the other box accordingly. The overlapping region di- rectly contributes to the heatmap values, and aggre- gating over all samples produces the final distribution of overlap regions. The heatmap peaks near the im- age center (0.5, 0.5). clearly indicating that a major- ity of bounding box pairs exhibit significant overlap in this region (approximately 90%). Even in peripheral regions with minimal overlap. nearly 50% of bound- ing boxes still intersect, further highlighting the preva- lence spatial interactions across the training data.
Spatial Overlap Distriburion
10
5500
7000
0.
02
Figure 12: Heatmap of spatial relations of pairwise bounding boxes.
A.3 TRAINING DATASET PREPARATION
To effectively train model for pairwise object compositing, curated a large-scale dataset con- sisting nearly million diverse samples by combining real-world datasets originally designed for object-centric scene understanding and visual try-on; Table Since not every image nat- urally contains multiple bounding boxes with intersections, we filtered the dataset to retain only samples with least two intersecting boxes. Furthermore, better facilitate effective modeling of overlapping regions, we preferentially select bounding box pairs with the highest IoU, The detailed implementation procedure is elaborated in Algorithm [ For datasets such Objects365 (Shao et al. 2019), which do not provide segmentation masks, we use the existing bounding box annota- tions as prompts for SAM (Kirillov et al.) (203) to obtain sufficiently accurate object masks. For
TF-ICON (Lu et al. 2023). we discard bounding boxes corresponding to background segments.
A.4 TESTING DATASET
We curate a 110-case test set: 80 cases use backgrounds from the LVIS validation set with object exemplars from DreamBooth, and 30 cases use Internet backgrounds with in-the-wil object images. Target insertion regions are manually annotated.
B IMPLEMENTATION DETAILS
Our models are implemented using PyTorch, with details of the model architecture, training, and inference provided below.
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Algorithm Pseudocode of pairwise bounding boxes selection algorithm in a PyTorch-like style.
Solect pair of bounding boxas with the highest Toll
def select_boxes [bbox_xyxy1
san
bbox_xyxy: xyxy: His of bounding boxea [x0, yo, x1, ] format
bbox
return: tuple of two boundiag boxes with highest IoU, or 1 if none exist ean
if cen(bbox_xxxy return
2:
Computa pairwise metrix cou_matrix pyppute_iou_matlaae (bbox_xxx)) # aasume this runction exiats
Find indexx, indexl ap..anaavel_inde azggaaxx matukn)) iou_matrix shape) max_tou tou_masrix (i indexl]
maximum IeU
if mex_ 0:
return -1
return bbox_xyxy (yyxxey
B. MODEL ARCHITECTURE
Our framework builds upon the publicly available implementations of Stable Diffusion v2.1 and ControlNet v1.0. In particular, we adopt the U-Net backbone of Stable Diffusion as the generative model and augment it with ControlNet branch enable spatially guided compositing. The control scale is fixed 1.0 throughout training and inference to ensure a balanced contribution from both the generative and control pathways. To enhance interaction modeling, the original residual blocks with our proposed Interaction Transformer blocks. Specifically, 25 blocks of the U-Net including 12 encoder blocks, middle block, and 12 decoder blocks are substi- tuted with IT blocks. In parallel, the ControlNet branch also undergoes the same replacement, where all 13 blocks (12 encoder and 1 middle block) are re-implemented using our IT design. This consis- tent replacement ensures that both the generative and control pathways benefit from the improved modeling capacity of IT blocks, thereby strengthening cross-object reasoning and compositing fi- delity. Additionally, similar to the original ControlNet, the connections from the control model to the generation model are initialized by coro-convolutions, which prevents the generative capabilities of the controlled U-Net from diminishing at the beginning of training.
systematically replace
As shown in Figure 13 for the multi-view shape encoder, we render six novel views of each ob- ject using the single-view reconstruction model Zero123++ (Shi et al., (023). Each view is encoded into a latent representation by a pretrained DINOv2 image encoder (Oquab et al. (2024), which provides strong semantic and structural features. The result- ing per-view codes aggregated our fusion module to form a compact multi-view descriptor, en- riching the object representation with both global shape priors and fine-grained texture details. The MLP used for the multi-view shape prior is imple- mented as two-layer feedforward networks.
Objects
Single evvien Reconstruction
Shape codes
Shape Encoder
Multiview objects
Figure 13: Details of multi-view shape prior.
B.2 TRAINING DETAILS
For the objective loss function, we adopt the standard denoising diffusion objective, defined as the mean squared error between the predicted noise and the ground-truth Gaussian noise:
[())= Exap, Pxxp:(xy.mm m e~.1(
[|| [| (Xba (xp, my hppptaao) () - - -s1
(14)
The temperature parameter is set to r = 0.5 for all experiments. Our model is implemented in PyTorch Lightning and trained with mixed-precision (fp16) NVIDIA H100 GPUs with 80GB
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X
Xa
X
Xcomp
Shape Encoder
UNet
m,
ma
VAE Encoder
WE
Zog
ControlNet
Decader
Figure 14: Inference process of PICS. The background and object embeddings, together with their
masks, are fused in latent space and decoded by the VAE to produce the final composite Xcomp-
memory. We train with batch size of 8, using Adam (Kingma 2014) with learning rate of 1x10-5 and gradient accumulation of 1.
B.3 INFERENCE DETAILS
As illustrated in Figure 14, during inference our model takes the background embedding Zég. ob- tained by encoding the background image Xbg with the VAE encoder, and the object codes ca and Cb extracted from the object images X, and X₁, using shape encoders, together with their corre- sponding compositing masks m, and m,. These components are fused form latent composite, which is subsequently decoded by the VAE decoder to generate the final composite image Xcomp- Specifically, the DDIM sampler generates the composite image after 50 denoising steps, with classifier- free guidance scale of 5.0 (Ho & Salimans 2021
C MULTI-OBJECT COMPOSITING
C.1 MATHEMATICAL FORMULATION
We extend the pairwise overlap expert to the case of M composed objects.
Multi-object overlap expert. Given object codes {cI,..., and the background feature z the gating query is computed as
(0(2z=1)
(15)
Fach object code is aligned to the background query via cross-altention:
č Cross qas Jx (cp), fv(c,)),
p= M.
(16)
A compatibility score is computed for every aggregated object:
Sp (9.. Cp)
p I,...,,, M,
(17)
Vd
and normalized via a softmax:
exp($p/T)
Op E exp(s,/T))
p= 1,..., M.
(18)
The fused multi-object context is obtained by attention- combination:
CL:M
ap Ep-
(19)
p=1
We then calculate unified overlap response using background-guidee cross-attention:
hi:M CrossAttn[[q(22= [K(C1)))) fv(CuM)))
(20)
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Objects
Source images
Backgrounds
CFG=1
CFG=5
CFG-9
Figure 15: Effect of different classifier-free guidance (CFG) scales on image recompositing, com- paring CFG values of 1, 5, and 9.
This produces an order-agnostic, attention-based overlap expert that synthesizes multi-way interac- tion patterns among all objects.
Region-gated updates and aggregation. For M objects, each expert output is masked by its corre- sponding spatial region:
Bbg
mbg
hōg
AZp
mp hp: p ..... M,
AZI.M m1.M h (21)
All regional updates are aggregated via residual update:
M
Az = AZbg
Azp + AZI:M.
z' ='=2" z 1-1 + Az.
(22)
after which feed-forward network refines 1 before passing it to the next block. This formulation reduces to the two-object case in the main text when M 2.
C.2 DATASET PREPARATION
For each image, we first discard very small instances and keep only objects above an area threshold. We then convert all remaining bounding boxes into consistent format and compute the pairwise IoU among them. To select set of overlapping objects, we identify the anchor, defined as the object that overlaps the most with the others. We then take the anchor's ' top overlapping neighbors (those with positive ToU) and choose as many needed for the target setting. For example, the top two neighbors for a 3-object sample, or the top three neighbors for a 4-object sample. This ensures that all selected objects overlap with the anchor, although they are not required to overlap with one another. For each selected object, we extract its binary mask and the corresponding cropped RGB patch. These masks and patches saved together with the original image to form structured multi-object sample.
D EFFECT OF CLASSIFIER-FREE GUIDANCE
In our experiments, we systematically evaluate the effect the classifier-free guidance (CFG) seale image compositing quality by comparing three representative values: 1, 5, and 9. As shown in Figure 13] when the CFG is set to 1, the model behaves almost unconditionally. leading to noisy and blurry synthesis in the compositing regions. Interestingly, although the fidelity of the inserted objects
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Figure 16:
Four exemplars for (non-)ppartal 3D reconstruction. From left to right: source image, image with object bounding boxes, 6-view reconstructed object images.
poor, the color distribution tends to match the background more naturally, resulting better chromatic consistency. In contrast, large CFG value such 9 enforces strong adherence to the object condition, thereby producing composites that better preserve the identity and fine details of the reference objects. However, this often comes at the cost of visual harmony, as the object colors may deviate noticeably from the background, leading to less coherent composition. A mid-range CFG of provides favorable balance between these two extremes, ensuring that object identity is retained while maintaining reasonable consistency with the background. This observation is consistent with prior findings guided diffusion, where overly low scales reduce conditional fidelity and overly high scales overfit the conditioning signal, thereby compromising overall realism. Hence, adopt 5 as the default setting in all our experiments.
E 3D RECONSTRUCTION
As shown in Figure 16 we evaluate a pretrained 3D reconstruction model, Zero on occluded 2D segments from LVIS and find that, despite occlusion, it reliably reconstructs coherent partial 3D shapes. The partial 3D shape formed from such multi-view images provides compact features of the objects that guide the modeling of intersection regions, leading geometrically consistent interactions and improving compositing quality.
F CHOICES OF OBJECT MASK
We further assess the robustness of our method to segmentation masks varying quality, as il- lustrated in Figure 17 Specifically, compare pairwise compositing results using coarse masks
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Inputs
Bounding box
Manually segmented
SAM
Figure 17: Four exemplars for (non-)partial 3D reconstruction. From left right: source image, image with object bounding boxes, 6-view reconstructed object images.
Objects
Backgrounds
CLIP encoder
DINOv2 encoder
Figure 18: CLIP encoder vs DINOv2 encoder. Models are separately trained on LVIS dataset.
including bounding boxes and manually annotated masks? against results obtained with high gigh-quality SAM masks. Our findings show a clear trend: better segmentation masks lead to better compositing. In contrast, coarse or inaccurate masks tend introduce undesired background cues from the object image, ultimately degrading the compositing quality.
G CHOICES OF OBJЕCT ENCODER
We conduct an ablation study to evaluate how the choice of object encoder influences the fidelity of composed objects, as exemplified in Figure [8] Specifically, we replace our default object encoder with CLIP, while keeping all other components unchanged, and both of the two models are trained
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Objects
Backgrounds
Ours
Figure 19: Additional visualization of our pairwise compositing with unoccluded inputs.
only on the LVIS dataset. The comparison reveals clear degradation in appearance preservation when using CLIP: the composed objects exhibit diminished fine-grained details, such the subtle skin-tone differences between the two elephants, the striped patterns on the T-shirt, and the features of the motorcyclist. These observations highlight the importance of using a stronger encoder for capturing tigh-freeuency object textures that are crucial for identity-peeserving compositing.
H VISUALIZATION OF UNOCCLUDED INPUTS
Our model is trained exclusively on occluded object instances, without ever seeing intact objects during training. To evaluate robustness beyond this training regime, conduct additional evalua- tion experiments using fully visible (unoccluded) objects as inputs, as demonstrated in Figure [9] Our method demonstrates strong generalization to this setting, where it able accurately generate pairwise spatial relations while preserving the identity of composed objects.
I VISUALIZATION OF 3D AUGMENTATION
In Figure 20 we compare results generated with and without the proposed 3D augmentation strat- egy. Incorporating 3D augmentation enables the model synthesize broader range of viewpoint variations for the composed objects. For instance, in the first row, the inserted donut exhibits clear geometric changes; the second row, the flower rendered from a novel orientation relative to its input view; and in the last row, the doll also appears under a noticeably different facing direction. These examples illustrate that coupling our model with 3D augmentation substantially improves viewpoint diversity, leading richer and more flexible compositional generations.
COMPUTATION AND TIME COST
Our model exhibits competitive computational efficiency relative to existing methods, requiring ap- proximately 20 seconds per sample for pairwise image compositing, as shown in Table 6 While integrating the alug-and-plar 3D reconstruction module introduces additional computational over- head, the overall inference- sitrennee--imeeeee cost remains practical.
K MORE QUALITATIVE RESULTS
K.1 IN-THE-WILD PAIRWISE COMPOSITING
We provide comparison results on the in-the-wild images in Figure 21 for each sample listed in Table 7
Typical artifacts observed
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Objects
Source image
Backgrounds
w/o 3D
w 3D
Figure 20: Additional visualization of w us w/o 3D augmentation of our method.
Table 6: Comparison of inference-time computation cost for different methods.
Methods
Params
Time (min)
GFLOPs
GPU Memory
PbE (ann al.
2023
1.31
0.722
1048
8,70
ControtCom Zhang clal.
2023
1.37 G
0.684
1123
21.5G
OhjectStitch Song et
7.04
AnyDoor (Chon ét
20/40
2,45
0.342
2450
15.8 G
FreeCompose (Cavi 0l
2024c
1.07
1,912
678
5.79 G
OmniPain Yu
2172
12.1
1.516
33035
23.1 G
Insert InsertAnything Song
2026
0.52
1.379
20955
9.62 G
Ours
2.74
0,316
2685
18.1 G
reconstmacton)
K.2
VIRTUAL TRY-ON
We provide comparison results on the VITON-HD testing set in Figure 22 Zoom-in insets of waistline highlight that our method maintains boundary fidelity under occlusion and nonrigid defor- mations, whereas competing methods exhibit seam breakage, color bleeding and ghosting artifacts.
K.3 IMAGE RECOMPOSITING
We provide additional comparison results the LVIS validation in Figure 23
L LLM USAGE
We used ChatGPT 5 solely general-purposs writing assistant for minor phrasing well as grammar and spelling corrections. The LLM did not contribute to research ideation, dataset design, model architecture, experiments, analyses, conclusions, and it was not used to generate code,
23

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Published as a conference paper at ICLR 2026
Objects Background
PuE
ControlCom ObjectSlichh
AnyDoor
FreeCompose
OmniPaint
InsertAnythingg
Ours
13
Figure 21: Qualitative comparisons for in-the-wild compositing, highlighting spatial relations: sup- port, containment, occlusion, and deformation, corresponding to the teaser figure.
Table 7: Typical artifacts observed in the pairwise compositing results of Figure 21
Sample
Paint-by-Example
ControlCom
ObjectStich
AnyDoor
distorted, fused with backpack
physical placement supported
Seat surface missing
Seat surface disuppeared
Holder sbortened. pen style changed
attuched side of holder
Pen attached side of holder
Pen attached side of holder
Artifacts between backpacks
Back occludes front one
Right backpack shows left backpack' is color
Back one occludes front one
Toy style changed, backpack deformed
Contact region distorted
Toy style changed, contact region unrealistic
Toy not physically touching blanket
Vase not placed upright
Vase not placed upright
Vase and flowers both lilled
Vase shape altered
apple.
fused busket front
placement coused busket gup
partly fused with basket
no busket shudow
Features of both human and sofa not preserved
Strange artifacts occluded sofa region
Both buman and sofa shapes unrcalistic
leg splits sofa
Distorted leg
Fair
Distorted leg
Fair
figures, or results. All technical content, equations, and claims were written and verified by the authors, who accept full responsibility for the paper. No confidential data were shared with the LLM, and any suggested text was reviewed and revised by the authors. The LLM is not an author.
24

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Published as a conference paper at ICLR 2026
Otjects Backgrounds
Dantt-y--Examppt
ControlCom
ObjectSitch
AryDoor
OrniiPaint
InserAnyyhing
1500
LUSE
KOUD
Figure 22: Qualitative comparison on the VITON-HD.
25

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Published as a conference paper at ICLR 2026
Objects Source images
Backgrounds
Paint-by-Exampie
ControlCom
ObjectSiich
AnyDoor
OmniPaint
Ours
LORT
Figure 23: Additional qualitative comparison the LVIS validation set.
26


Reviews:
[{"review_id": "HF3PjxqHQt", "reviewer": "Reviewer_4pr4", "summary": "The authors developed a parallel image compositing framework, PICS, to resolve spatial inconsistencies in pairwise edits by explicitly modeling object interactions using a mask-guided Mixture-of-Experts. The paper is well-organized.", "questions": "See previous.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper is well-written, and the proposed methodology is sound.\n2. The approach is well-supported by sufficient experimental results, including both quantitative and qualitative comparisons.\n3. The core idea of the Mask-guided Mixture-of-Experts (MoE) is interesting.", "weaknesses": "1. Dependency on Mask Quality: The method's performance relies on precise input masks. The paper lacks a sensitivity analysis on mask quality (e.g., comparing results from high-quality vs. coarse masks or different segmentation sources), making it difficult to assess its robustness to imperfect inputs.\n2. Background-based Gating: The gating mechanism for occlusion (Eqs. 6-9)  is primarily based on background-object similarity. This may lead to incorrect judgments when objects share similar textures or colors with the background. Incorporating direct object-object interactions might be necessary for more reliable occlusion reasoning.\n3. Limited Physical Consistency: The method occasionally degrades the original object's geometry or texture , as seen in the failure cases (Figure 8).\n4. High Computational Cost: Compared to existing methods, the model has a significant parameter count (2.74G – 4.66G), which could hinder practical adoption.\n5. Scalability Limitation: The approach is restricted to pairwise compositing, making it less flexible for multi-object scenes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0qqrNWupDO", "reviewer": "Reviewer_r6im", "summary": "This paper proposes PICS, a diffusion-based method for pairwise image compositing that shifts from sequential to parallel composition of two objects into a background, explicitly modeling spatial interactions like support, containment, occlusion, and deformation.\nThe main technical contribution includes an Interaction Transformer with mask-guided Mixture-of-Experts for region-specific processing (background/exclusive/overlap) and an adaptive alpha-blending mechanism for coherent overlaps, with geometry-aware augmentations for pose variations. The model is trained on ~1M samples from datasets including LVIS, Objects365, and VITON-HD. The experimental validation conducted by the authors claims the gains in compositing tasks on numerical scores, visuals, and user study feedbacks.", "questions": "1. Given observed fidelity losses in textures and labels (e.g., Figure 4 rows 2/4, Figure 5 last row), how will tuning the CFG scale or refining the shape encoder improve identity preservation without sacrificing harmony? Is there any other solution to improve visual fidelity?\n\n2. How does the proposed Interaction Transformer extend to more than two objects? For instance, would the overlap expert generalize to multi-way intersections, or require modifications such as hierarchical routing?\n\n3. In line 283, the authors mentioned they use the off-the-shelf single-view reconstruction model to render K auxiliary views as augmentation. Which model is used here? How does it impact inference time or robustness to extreme viewpoints?\n\nI would be happy to raise my score if the author rebuttal addresses my concerns.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper offers a novel parallel compositing paradigm and MoE-based transformer that targets pairwise relations, addressing instability in multi-turn diffusion edits. The paper is well-written and easy-to-read. The experimental design is reasonable.", "weaknesses": "1. Despite superior metrics, visual fidelity remains unsatisfactory in some samples, with identity preservation issues such as altered stitch on the bag (2nd row, Figure 4), distorted bottle label (4th row, Figure 4), and changed shoe patterns (last row, Figure 5), suggesting limitations in retaining fine details during interaction modeling.\n\n2. The restriction to exactly two objects may limit the method's broader applicability. Extensions to more than 2 objects are not explored in the paper, leaving scalability of the proposed method unclear. \n\n3. While failure cases are shown, their analysis is brief, attributing problems to the shape encoder without detailed remedies or further ablations. Also, is this one of the possible reasons that lead to the poor visual fidelity mention in the weakness 1?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qUwUnfSnNm", "reviewer": "Reviewer_M9rh", "summary": "PICS is a diffusion-based framework for composing two objects into a background while maintaining spatial and visual consistency. The key idea is to perform parallel compositing of two objects rather than sequential editing. This paper introduces an Interaction Transformer with mask-guided experts and adaptive α-blending to handle overlapping regions and preserve boundaries, along with geometry-aware augmentations for robustness to pose changes.", "questions": "Refer to Weaknesses", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "The paper identifies a real issue, multi-turn compositing instability, and addresses it with a structured, parallel formulation. The task definition is well-motivated.\n\nThe mask-guided MoE and α-blending are simple but intuitive mechanisms to ensure boundary consistency.", "weaknesses": "It seems that the method is explicitly limited to pairwise (two objects) compositing.  Could PICS perform well on three-object, four object compositing? Please give me several visual examples if available.\n\nBaselines are outdated. To the best of my knowledge, some open-source object insertion models can perform much better than the baselines you have chosen, such FreeCompose [1], OmniPaint [2], and Insert Anything [3]. Could you provide a comparison of these models?\n\nThe proposed mask-guided MoE and multiple cross-attention blocks likely increase inference cost. Could you report detailed runtime and parameter counts to quantify the overhead?\n\nFigure 8 briefly shows degradation of compositing, but the causes are not analyzed in detail.\n\nThere is no visualization or strong justification for why geometry-aware augmentation improves compositing quality.\n\n\n\n[1] FreeCompose: Generic Zero-Shot Image Composition with Diffusion Prior (ECCV 2024)\n\n[2] OmniPaint: Mastering Object-Oriented Editing via Disentangled Insertion-Removal Inpainting (ICCV 2025, release time: 2025.03)\n\n[3] Insert Anything: Image Insertion via In-Context Editing in DiT (arXiv 2025, release time: 2025.04)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "An5rio7arf", "reviewer": "Reviewer_z8Jf", "summary": "The paper tackles the task of composing objects in parallel by explicitly modeling the compositional interactions among visible objects and background. The core is an Interaction Transformer that employs mask-guided Mixture-of-Experts to route background, exclusive, and overlap regions to dedicated experts. The proposed method outperforms baselines in inserting multiple objects.", "questions": "See above", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. I appreciate the task and the motivation, that sequentially insert objects into an image will overwrite previously generated content and inserting multiple objects is a meaningful task itself.\n2. The visualized results seem good.\n3. The paper is overall well-written.\n4. I appreciate the interaction diffusion network as well as the interaction transformer block, that sounds reasonable.", "weaknesses": "1. It seems that most cases shown in the paper are inserting two objects. I wonder if there's any direct extension to multiple objects since previous methods can perform it sequentially. As the authors also claim in the limitation \"restricted to two objects\".\n2. It seems that during training, the model takes in modal part (visible part) of an object? But I am assuming that during in-the-wild inference, the ideal input would be unoccluded instances like those shown in Fig. 5. Could the authors attach more examples as well as explaining how the proposed method can generalize well to this kind of setting?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)