VMDiff: Visual Mixing Diffusion for Limitless Cross-Object Synthesis
Authors: Zeren Xiong
Yue Yu
Ze-dong Zhang
Shuo Chen
Jian Yang
Jun Li
Venue: ICLR 2026 Poster (2026)
Keywords: Image generation
Image concept fusion
Primary area: generative models

Abstract: Creating novel images by fusing visual cues from multiple sources is a fundamental yet underexplored problem in image-to-image generation, with broad applications in artistic creation, virtual reality and visual media. Existing methods often face two key challenges: coexistent generation, where multiple objects are simply juxtaposed without true integration, and bias generation, where one object dominates the output due to semantic imbalance. To address these issues, we propose **Visual Mixing Diffusion (VMDiff)**, a simple yet effective diffusion-based framework that synthesizes a single, coherent object by integrating two input images at both noise and latent levels. Our approach comprises: (1) a **hybrid sampling process** that combines guided denoising, inversion, and spherical interpolation with adjustable parameters to achieve structure-aware fusion, mitigating coexistent generation; and (2) an **efficient adaptive adjustment module**, which introduces a novel similarity-based score to automatically and adaptively search for optimal parameters, countering semantic bias. Experiments on a curated benchmark of 780 concept pairs demonstrate that our method outperforms strong baselines in visual quality, semantic consistency, and human-rated creativity.

VMDIFF: VISUAL MIXING DIFFUSION FOR LIMITLESS CROSS-OBJECT SYNTHESIS
Zeren Xiong', Yue Yu', Zedong Zhang', Shuo Chen", Jian Yang & Jun Li
1 School of Computer Science and Engineering, Nanjing University of Science and Technology, China Schooo of Intelligence Science and Technology, Nanjing University, China (xiong_zeren, zandyz, yuue) just edu. en; (shuo. chen, csjyang, istjunlifenjin cn;
* corresponding author
Origital (Sinage
Chyana Siagee
Two groups (rows) illustrating our VMDiff's capability to generate coherent hybrid ob- jects. For each group, images from the 2nd to the 5th column are the product of fusing the source image in the 1st column with the corresponding image in the top left
Figure 1:
ABSTRACT
Creating novel images by fusing visual cues from multiple sources is funda- mental yet underexplored problem in image-to-image generation, with broad ap- plications in artistic creation, virtual reality and visual media. Existing methods often face two key challenges: coexistent generation, where multiple objects are simply juxtaposed without true integration, and bias generation, where one ob- ject dominates the output due to semantic imbalance. To address these issues, we propose Visual Mixing Diffusion (VMDiff), simple effective diffusion- based framework that synthesizes a single, coherent object by integrating two in- put images at both noise and latent levels. Our approach comprises: (1) hybrid sampling process that combines guided denoising. inversion, and spherical inter- polation with adjustable parameters achieve structure-aware fusion, mitigating coexistent generation; and (2) an efficient adaptive adjustment module, which in- troduces novel similarity-based score automatically and adaptively search for optimal parameters, countering semantic bias. Experiments a curated bench- mark of 780 concept pairs demonstrate that our method outperforms strong base- lines in visual quality, semantic consistency, and human-rated creativity. Project
INTRODUCTION
Synthesizing novel images by combining visual elements from multiple sources fundamental challenge in image-to-image generation, with wide applications in virtual reality (Haque et al. 2023
Our method's 's results, presented under the title Creative Toys Series, were awarded the Silver Award at the NY Digital Awards (Generative AT) 2025 (Award Page Video).

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Original Image
GPT-40
Ours
Origtnal iaag
Dreamo
Ours
G Ceesistent goneration
Blas goneration
Figure 2: Failed fusions between two object images. GPT-44 OpenAl (2025) performs coexistent generations (left), while DreamO (Mou et al.) 2025) exhibits bias generations (right). In contrast, our method achieves seamless and harmonious fusion of the two objects.
Chen et al. 2024), digital media (Zheng et al. 2024 Zhao et al. 2024), product design (Ju et al.
2024
Sheynin al., 2024 Wang al. 2024 and film and game (Ceylan et al. 2023 Liu al.
2024
In particular, visual composition methods generate high-fidelity images by composing ob-
jects through various strategies, such as combining object words into complex sentences (Liu al.)
2022). merging multiple objects (Liu et al. (021), or blending scenes and styles (Zou et al., 2025). Although these approaches effectively position different objects or parts within an image, they often struggle to seamlessly integrate distinct elements into single object. Recent semantic mixing (Li jet al. 2024, Xiong et al. 2024) explores novel object synthesis by combining textual descriptions of object with another images text. In contrast, this work focuses visual mixing-directly blending two object images into a single, imaginative, and visually cohesive concept.
However, when existing powerful methods are used perform this visual mixing task, identify two key limitations. First, coexistent generation (see Fig. left) occurs when different objects merely appear in the same scene-either side-by-sida true visual and semantic integration. While the resulting compositions are spatially coherent, they remain conceptually disjoint. For example, OpenAl's recent GPT-4o (OpenAI (025) produces an image where the glass jar and owl overlap but fail to meaningfully fuse. Second, bias generation (see Fig. [] right) arises when the model generates only one object while omitting the other. This asymmetry likely stems from imbalanced representations or unresolved semantic conflicts, leading to outputs that disproportionately emphasize one object. For instance, DreamO (Mou et 2025) generates the lipstick while entirely neglecting the iron man figurine.
partially overlapped overlapped-without without achieving
To address these limitations, we develop Visual Mixing Diffusion (VMDiff), simple yet effec- tive framework for synthesizing novel, coherent objects that seamlessly integrate two input images. VMDiff ensures structural plausibility and semantic balance through two key components: Hy- brid Sampling Process (HSP) and an Efficient Adaptive Adjustment (EAA). HSP integrates the two inputs through noise inversion and feature fusion. The inversion refines an initial noise vector conditioned on concatenated input object embedding with two parameters and their corresponding text prompt, ensuring deep information mixing to prevent mere juxtaposition. Subsequently. feature fusion employs curvature-respecting interpolation to blend image embeddings, with a scale factor controlling either object from dominating and thus countering bias generation. EAA automates the search for optimal parameters by proposing novel similarity-based score that measures alignment with both visual/semanti similarity and balance between the fused object and the input object im- ages/their category labels. By maximizing this score, the EAA dynamically adjusts the influence of each input, ensuring semantically coherent and visually faithful fusions across diverse object pairs.
Our contributions are summarized follows: (1) We introduce hybrid sampling process that con- structs optimized semantic noise via guided denoising and inversion, combined with curvature- aware latent fusion strategy using spherical interpolation for smooth and tunable blending. (2) We present an efficient adaptive adjustment algorithm that adjusts fusion parameters to achieve seman- tic and visual balance via lightweight score-driven search. (3) By integrating them, we propose VMDiff, unified and controllable framework for object-level visual concept fusion. Experiments on a curated benchmark of 780 concept pairs demonstrate that our method achieves superior object synthesis, excelling in semantic consistency, visual harmony, and user-rated creativity.

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2
RELATED WORK
Multi-Concept Generation. Multi-concept generation seeks to synthesize images representing multiple user-defined concepts, typically from a few reference images per concept. Early works such as Custom Diffusion (Kumari et al. (023) and SVDiff (Han et al., 2023) extend single-concept personalization by fine-tuning on joint data or merging customized models. Later methods (Gu et al.)
2023 Liu et al. 2023b) enhance compositionality by merging LoRA modules or token embeddings via gradient fusion (Gu et al. 2023) or spatial inversion (Zhang et al.
2024). More recent approaches further improve efficiency and flexibility: FreeCustom (Ding al. 2024) employs multi-reference
self-attention and weighted masks for training-free fren composition, while MIP-Adapter (Huang et al, 2025) mitigates object confusion with a weighted-merge strategy. OmniGen (Xiao [l.|2025) and
DreamO (Mou et al., (225)) provide unified instruction- bassed frameworks for diverse generation tasks. Unlike prior methods that explicitly separate input concepts, our approach introduces uni- fied fusion framework that integrates two concept inputs into novel object with coherent structure and balanced semantics.
Semantic Mixing. Creativity, spanning domains from scientific theories to culinary recipes, has long been a key driver of progress in artificial intelligence (Boden 2004 Maher 2010 Wang al.]
2023 Xiong et al. 2025b). In this context, semantic mixing has emerged as promising approach for generating novel objects by fusing features from multiple concepts into single coherent representa-
tion. Unlike traditional style transfer (Zhang et al. 2023 Tang ct al., 2023 Ke ct al., 2023) image
editing (Avrahami et al. 2025 Dong & Han 2023 Brooks et al. 2023 Gal et al. 2023 wwicc
emphasize texture transfer or localized modifications while preserving layout semantic mixing focuses on concept-level integration within a single entity. Conceptlab (Richardson et al. 2024
interpolates token embeddings to synthesize imaginative entities, while TP2O (Li et al., (2024) en-
hances controllability by aligning and blending prompt embeddings. However, both operate purely in the textual domain and lack support for real visual content. MagicMix (Liew al. 2022) fuses
image latents with text prompts during denoising, preserving spatial structure, while ATIH (Xiong al., (024) improves semantic aligmment through more coordinated integration of visual and tex- tual inputs. FreeBlend (Zhou et al. (2025) performs staged interpolation in latent space to produce blended objects. In contrast, our method integrates structural and semantic cues from real image concepts, generating hybrid objects that are both visually coherent and semantically balanced.
3 VISUAL MIXING DIFFUSION
In this section, present Visual Mixing Diffusion (VMDifff for synthesizing novel objects im- ages in Fig. 3 Our method consists of two key components. We introduce a Hybrid Sampling Process (HSP, $3.1p that generates new object image by blending two distinct inputs using learned scale factors and noise. An Efficient Adaptive Adjustment (EAA, $3.2) dynamically adjusts the scale factors and noise based on a Similarity Score (SS), ensuring high-quality object synthesis.
3.1 HYBRID SAMPLING PROCESS
Given two distinct images / and 12, along with their respective category labels Ti and T2 (e.g., Iron Man and Duck), we first construct a guiding prompt Pg: "A photo of Ti > creatively fused with < T2 >." and sample an initial Gaussian noise ~ (0, I). For convenience, we denote an input data
D [I, I2, Tı, T2, }. We first employ pretrained image/text encoders E.(-)/E(()) of FLUX.1 Krea (Lee et al. 2025) to project both visual and textual modalities into a unified langa-la latent space. Specifically, these embeddings are extracted by 21 = (T)), 22 = (T2),, Ep =
(Pc)). Using these embeddings, HSP includes blending noise and mixing denoise.
Blending Noise (BNoise): Directly sampling standard Gaussian noise to generate a blend of two ob-
jects frequently produces incomplete results, with key features such as arms or legs missing (Fig. 4 This occurs because random noise contains no information about the input objects. Our solution is to refine initial noise vector transforming it into visually and cemantically-inoomme that faithfully represents the source data. Inspired by Rectified Flow (Albergo & Vanden Manden-Eijjden, 2023), this is achieved through a guided denoising and inversion process. Using inputs c, 21, 22, Zp.
estimate

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Prompt: A photo of (T' charizard figurine) creativelly fused with Ti panda figurine
Hybrid Sampling Process (HSP)
To Aigurine
Similarity (S5)
max (
Pd O
S
Scale Concatenation (SCa))
Deusiiig
5,09
Nouse
1-4
5,00
S
5,00
Fyx (09 - (9g
Scale Interpalation (Shup)
4
SCat &
visual
semsntic balance
5,00
Slup
simularity
Ya!
panda Agurtne
S = S. (9) 51,09
S' 5, Sr,
Blending (BNoiwe)
Tg like imsge. 10B the fiord imagee ssing & and with 9. T the of 5,00 19 the sinilariiy betwoon M
TBE
Figure 3: Overview of our VMDiff framework, Given two input images and their categories, the Hybrid Sampling Process (HSP) fuses them using noise inversion, scale interpolation (SInp) and scale concatenation (SCat). Efficient adaptive adjustment (EAA) optimizes fusion parameters 0 = {ax, 31, (2, }) via similarity score (SS) that measures visual, semantic, and balance consistency.
denoise to intermediate timestep len, and invert refined noise eb, which is defined as:
denoise: t decreases from T to feen starting zy=t
It-1 24 (or ot (t-1)0s( t, escar (21, 22 B1, 82). Yden zp); 2, + (a.+1 01) uu (2e, L, Esca (Z1, 22; B1, 32), Tinvs zp) 1+11
(1)
6b
TT
BNoise
inversion:
fave T, starting 2
where xy and i are latent variables at timestep Us denotes the noise prediction network, 0y con- trols the sampler parameter. For conditioning, adopt parameters from (Bai et al.) (025): a high denoising strength "len = 5 ensures strong guidance, while an inversion strength of Tinv = 0 is used to reduce distortion in the noise space. The total number of timesteps T is 999, with predefined intermediate denoising timestep at tden = 652. equation I Ep provides the semantic information, while zsca provides visual information. Here, we introduce two learnable factors 31, 832 E R4 to create scale concatenation (SCat) of the input latents: ZSCar(a) 22; B1, B2) = concat (31 Z1, 32
Discussion BNoise: concatenate vs. interpolate. We hypothesize that interpolating mismatched embeddings obscures subtle features, while con- catenation preserves them, allowing the inversion process to refine noise containing the full concept. To test this, we compare Interpolate before BNoise: Blend embeddings first, then refine the noise, and Interpolate after BNoise: Refine noise from each embedding first, then blend the results. Fig. shows that both interpolation methods fail to capture intricate details (e.g., legs), whereas concatenation yields superior visual quality and faithfulness by preserving input details and ensuring a coherent denoising pathway. Quantitative results in Appdx. (A
Grighha/ louape
Randoos soll
Aeep Opr shocee
CV
Figure 4: Different BNoise strategies.
Mixing Denoise (MDeNoise): Using the blended noise es, we denoise it to finally produces a cross- object fusion by mixing the inputs, 21, 22, Zp. Specifically, we formulate this process as:
MDeNoise: decreases from 0, starting #r=<
I = D(oo), where 2o + xt-1 =
(a, (t-1)us(22 t, ZSinp (21, 223 a)), Ygen 2p)
(2)
Here, Ygen = 4.0 is fixed guidance scale, and the decoder D(-) generate the final fusion image using the FLUX.1 Krea decoder (Lee al. 2025). The scale interpolation (SInp), 2SInp (21, 225 a)), mixes the two visual embeddings and 2g into single coherent representation, which is imple- sin(a-6) sin(8)
mented by spherical interpolation (Shoemake 1985):
2Slnp
+ siu((1-a)-b) 22. sin(8)

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where 6 cos cos- 22), and 0 1 is learnable factor to control the mixing ratio. This MDeNoise process in equation outputs the final fusion image I.
Discussion MDeNoois: interpolate vs. concatenate. MDeNoise prioritizes fusing its two inputs, unlike BNoise which preserves them. While concatena- tion retains more input information, its rigid separation often creates disjointed representations and generations. How- ever, interpolation enables seamless in- tegration. To demonstrate this, we com- pare with concatenation-fusion vari- ant: ESIop replaced by =sCar(a:)
Figure 5: Different MDeNoise generations across a.
concat(az), (1 ())22) in equation 2
(Fig. 5) which tends to produce isolated objects rather than a unified hybrid. Our interpolation
instead creates single, coherent entity with harmonious consistency.
HSP: Overall, for a given input D, the hybrid sampling process combines the BNoise (equation and MDeNoise (equation 2. To simplify the notation, we formalize this process as the function:
I
((0) HSP(D:,,0)) =D(zo).
(3)
where 0 = {a, B1, B2, }} are learnable parameters, and ê {Tdcn = 5, Tivv = 0, 7/een 999, tden 652} are fixed defaults in this paper.
4,
3.2 EFFICIENT ADAPTIVE ADJUSTMENT (EAA)
The HSP process yields distinct fusion results [() defined in equation Snctation@iitt with parameters 6, defaults i and inputs D, making parameter selection critical for high-quality synthesis. We propose an adaptive framework to jointly adjust 0 (a,, 31, $2. <). aiming to achieve both semantic coherence and visual fidelity. Inspired by prior work (Li et al., [2024] Xiong et al., (2024), we first introduce Similarity Score (SS) to guide this search: (For simplicity, input D and defaults 0 are not shown.)
S(0) (0, Sr. (0) + ST,(0) (0) + (T,
(0) -,,0)))((00)) (0)
ST. (0) - Sr. ST,(0)) (0))
(4)
visual simmlarity
semantic similarity
visual balance
semantic bulance
where Si, (1=1, is the visual similarity between T(0) and the source image J; computed via DINO encoder (Oquab et al., 2024), while Sr, (0) (i 1) is the semantic similarity between I (0) and the category label Tü measured using CLIP (Radford et al. 2021). This scoring function designed to optimize two key objectives for successful fusion: (i) maximizing similarity, and (ii) enforcing balance. The first two terms ensure that the generated image / (0) retains high perceptual and semantic fidelity both input images and their corresponding category labels. By maximizing similarity to both sources, these terms preserve the core features of the original concepts. The final two terms- perns--enalizing the absolute differences- eeiierences-explciiittyy enforce balance, preventing the model from overfitting to one input and encouraging fair integration of both objects' features. Together, these components create unified SS objective that balances fidelity and symmetry, offering prin- cipled framework for optimizing feature fusion parameters.
Our EAA Algorithm. To maximize this objective S(0) in equation we present a hierarchical adjustment strategy that learns the parameters 0 {a, 31, 32,c) using the acceptance threshold Th = 2.4. The key loop iterates from to K = 3, performing these steps:
Sample (initial) Gaussian noise: ~ No, 1), initialize the parameters: a = 0.5, B1 = 32 = 1.0.
Searching a: Fixed 31 82 1.0 and e, perform golden section search (Teukolsky 1992) to find the optimal mixing factor a*:
et al.
a = arg max S(a, B1, E) ac(0.1]
(5)

---

Adjusting 31, B2: Fixed a², if S(a", 32, Th, then update the noise factors:
[8 31 & 02 = arg max S(a", , 31,8,0) if it S2
D2ER+
(6)
88 B2 & ß arg max S(a", B C), otherwise.
BIER,
where S1 = Sr, + ST,, S2 = Sr + ST., and S > S indicates that the mixing noise favors the object 41, and vice versa.
Acceptance criterion:
- & return 0" = 1aa, 1 ; Eg
if , 82, > Th, if > K, otherwise.
return 0* = "i.",,,,3000 B3,e") { & break,
(7)
turn to the step to resample c & h +
where the fused object image (0) is defined in equation 3 Our adaptive loop efficiently explores low-dimensional yet expressive parameter space 0 = {a, Bi, 32, 32,c). yielding conceptually balanced and perceptually smooth fusion results (Fig. (9) By reusing intermediate predictions and limiting optimization to scalar-level searches (via golden section search), the method enhances sample effi-
ciency-avoiding the computational overhead of gradient- gradient-based latent-space backpropagation.
Discussion on resampling c. During our blending process, sampling random Gaussian noise can occasionally yield low-quality or failed fusions. While first-order optimization an intuitive solu- tion, it offers no significant advantage over simple zero-order resampling for diffusion generation, despite its higher cost (Ma et al. 2025). Consequently, we adopt a zero-order resampling strategy to search for e, and small number of resamples K 3 proves sufficient for high-quality fusion. For fair comparison, this resampling disabled, = 1, and the random seed is fixed at 42.
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Datasets. We introduce IIOF (Image-Image Object Fusion), a new benchmark of 780 image pairs derived from 40 objects across four classes (i.e.. animals. fruits, artificial objects, and character figurines). Most images are from PIE-Bench (Ju et al., (024) and Pexel figurines were self- captured for quality. To evaluate order-sensitive methods, also generate all ordered pairs (1,560 total), ensuring comprehensive and fair benchmark. More details in Appdx.
B
Implementation Details. Our method builds upon FLUX.1 Krea (Lee al.) 2025). implementing E, with Redux (Black Forest Labs 2024) for latent-space aligmment. We generate all images at 512x 512 resolution using the Mrt.aaacchuaeerriieeeeeeeeeeeuee
(Lipman et al., (202) with 20 denoising steps. For the Efficient Adaptive Adjustment (EAA) module, we use Grounded-SAN. (Ren et al., 2024) and the query "most prominent object" to localize main regions for visual and semantic similarity computation. Each parameter search for and 3 involves at most 10 image generations. All experiments are conducted on two NVIDIA RTX 4090 GPUs.
Evaluation Metrics. To evaluate our method, we use two metric families: Semantic Align- ment (SA) and Single-entity Coherence (SCE). SA is computed on the generated prompt Pa using VQAScore (Lin et al.) (024b) and LLaVA-Critic (Xiong et al. (025a).. VQAScore em- ploys CLIP-FlanTS (Roberts et al. 2022 and LLaVA (Liu et al. 2023a). denoted as VQA SA and VQALLaa respectively; the LLaVA- score is LC SCE assesses if the image forms uni- fied concept by asking: "A photo of a seamless fusion of <Ti> and <T>> into a single coherent entity." Its scores are VQA SCE VQA and LCS We also compute the SS score and the balance metric Baim = |Sr, (8) Sy, (0)l -1 (0) Sr, (8)). where S7, (9) are normalized to [0, 1] using empirical bounds 0.15 and 0.45 to align the scales of visual and textual modalities.
4.2 MAIN RESULTS
We compare with leading methods across three categories: (i) multi-concept generation (e.g., OmniGen (Xiao et al., 2025), FreeCustom (Ding et al.] 2024). MIP-Adapter (Huang et al.]
https:/ httpp:://www.. http:///wwwpxxxee/1 com/

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+
ultraman figurie
sheep
+ car
figurine
+
corg!
time
+
glass jar
cake
+
owf
apple
panda
Input
Ours
Dreamo
MIP Adapter
OmniGen
GPT-40
Figure 6:
Comparisons with Multi-Concept Generation Methods. Our approach yields hybrid objects with improved structural coherence and visual balance over existing methods.
2025). DreamO (Mou et al. 2025 ) (ii) mixing-based (e.g., ATIH (Xiong et al. 2024), Concept-
lab (Richardson al. 2024). FreeBlend (Zhou al. (025)), )) and (iii) image editing (e.g., Stable
Flow (Avrahami et al. 2025). ). We also include qualitative results from GPT-4o (OpenAl 2025).
Inputs vary: multi-concept methods use two images and text prompt: ATIH and Stable Flow use
one image and text; Conceptlab uses text only. More examples in Appdx. G
method with multi-concept generation basclines (c.g.. MIP- Adapter,, OmniGen, DreamO, GPT-4o), highlighting two observations. First, basclines output often merely overlay features rather than fusing them-for example, lime enclosed in glass jar without integration-while our method creates a coherent hybrid. Second, baselines fre- quently favor one concept, such as generating either doll corgi but not a unified blend. In contrast, our approach balances both concepts, producing structurally unified and semantically con- sistent results. This demonstrates our method's superior ability achieve fine- grained visual fusion.
Qualitative Comparison. Fig. 6 compares
Fig. qualitatively compares our method with mixing/editing baselines (c.g., Conceptlab, ATIH, FreeBlend, Stable Flow). Conceptlab often biases toward one concept, while Stable Flow and ATIH make only subtle edits, such as color or texture transfer. FreeBlend frequently loses original in- formation and yields fragmented outputs. In contrast, our approach synthesizes novel objects that structurally and visually integrate both concepts, achieving a deeper, more harmonious fusion and demonstrating superior blending capability.

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astronaut monkeyking 4P
+
figurine
figurine
+
panda
mai figurine +P
figurine
squirble
+
wolf
figurine +
venusaur
Input
Ours
Stable Flow
ATIH
Conceptlab
FreeBlend
Figure 7: Comparisons with Mixing and Image Editing Methods. Our method produces more coherent and balanced hybrids, while baselines often favor one concept apply minimal edits.
Table 1: Quantitative comparisons on our IIOF dataset.
Models
VQA
VQATE
LC
LC
VQJAE
VQATEE
88
Bsiml
X.302
01.90
01411
PreeCustom JCVPR
Digg cl 2004
0.579
0.452
6.958
6.946
0.360
0.388
1.580
0.776
MIP-Adapter (AAAI
OrnanGenn (CVPR
Auo elal M05
0.570
0.469
7.550
7.233
0.352
0.348
1.705
0.617
Concepriab
0.573
0.395
ATTH (NarlPS
Ninta a 20:3
0.523
0.465
7.275
6.816
0.317
0.367
Stable Flow (CVPK
1024
0.460
0.372
6.020
5.024
0.266
0.294
DreumO (SIGGRAPI)
3og Mouel
0.591
0.167
7.592
7.013
0.370
0.316
1.793
0.644
FreaBlend (oXiv
Zheu
0.588
0.507
7,836
7.788
0.341
0.383
1.870
0.479
Quantitative Comparison. Table presents guantitative comparisons on key metrics, including VQASA. SA VQA VQASCE VQALLaa LOSA LCSCE similarity score (SS), and fusion balance Bai- Although MIP SCE attains the highest VQASCE it ranks only second or below on the other VQA, SS, and Bum metrics, indicating that its improvements are not holistic. In contrast, our method consis- tently outperforms all baselines most met-
Miblingedditing
Multi-cnpast generation
Reece Fiow
DevenO
SA
(aFT-oo
53
(87.15))
Figure 8: User studies.

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Table 2: Quantitative ablation study IIOF dataset.
Midels
VQAiE
VQATS
L.
1
VONILaY
VQA
88
Asim]
Baseline
0.497
0.488
7.261
7.077
0.287
0.314
1.570
0.682
Baseline
0.508
0.411
7.426
7.291
0.298
0.325
1.586
0.693
Baseline 24c-search
0.625
0.532
8.278
8.276
0.382
0.405
0.358
Baseline 2ea-searche Syrscarch
0,639
0,540
8.372
8.392
0,390
0.413
2,068
0.324
rics, demonstrating strong capability in gener-
ating coherent and natural blended objects.
These results reinforce qualitative findings and confirm the effectiveness of approach in achieving high-quality visual fusion.
User Study. To evaluate the perceptual quality of our fusions, we conducted two user studies (Fig. 8). 76 participants each rated 12 results-6 from Multi-Concepi Generation and from Mix- ing/Editing yielding 912 total votes. Our VMDiff received the highest preference in both groups: 67.3% and 87.1 % respectively. GPT-4o and ATIH ranked second, but with significantly lower votes (12.9% and 7.5%). These results indicate that our VMDiff aligns better with human preferences in visual coherence and creativity. More details in Appdx.
4.3 ABLATION STUDY
We conducted ablation study to evaluate the contributions of
Orgina//nage
BaseEne
I
Sasclhee
Baselin 2 search
Bapeine 2 zearch search
VMDiff's key components, as shown in Fig. 9 and Table 2 Progressively adding each ele- ment-(i) baseline 1: random noise+MDeNoosse
0.5), (ii) baseline 2: baseline I+BNoise
(31 82 1). (iii) base-
line 2 + MDeNoise (a search), and (iv) baseline
BNoise (31, B2 search) + MDeNoise (a syarch)-yielded consistent im- provements. Without noise re- finement, outputs lacked detail. Its
Figure 9: Ablation study in VMDiff. Noise refinement improves detail and structure, while adaptive a and B search progressively enhance semantic balance and visual coherence.
inclusion enhanced structural delity and preserved input fea- tures. Adaptive a improved fusion balance, while adaptive 3 refined noise influence for greater visual harmony. Fig. Trig illustraae the optimization process for representative case (doll figurine rabbif). Throughout iterations, similarity S(0) (green) increased steadily, while the blending bal- metric (dark blue) decreased. The a search (light blue) rapidly boosted similarity, and search (orange) smoothed visual-textual alignment. These results confirm that our EAA design effectively optimizes both similarity and symmetry for high-quality blending. Limitations are discussed in Appdx. D
4.4 MULTI-IMAGE FUSION.
Multi-image fusion. We also explore extending VMDiff beyond pairwise fusion. Figure IT shows preliminary three-image results obtained by sequentially applying our pipeline (e.g.. first fusing (4,, .2) and then fusing the hybrid with 1z). The method can still produce single coherent enti- ties that blend attributes from all three categories, indicating that our formulation can, in principle, scale to more inputs. However, compared with the pairwise case, these hybrids exhibit stronger information loss and imbalance across sources, so in this work we focus on image pairs and leave commutation-invariant learned aggregation of multiple image embeddings future work.

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2.5
2.376
2:430
2,441
2.441
2:441
2:45
3.445
2,579
2
24484
2
2,22
2222
1.794
1.924
1.932
1.322
1916
1.5
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Figure 10: Visualizing the updated process of our EAA based on input images (doll figurine) and 1n (rabbir). The parameter (blue) improves fusion quality, while 3 (orange) enhances semantic balance. The green curve (similarity) rises and the dark blue curve (imbalance) falls over iterations. The final output is coherent hybrid with high similarity and minimal imbalance.
Figure 11: Multi-image fusion. Our method can sequentially fuse three images into single coher- ent entity, though this results in greater information loss compared to pairwise fusion.
CONCLUSION
In this paper, presented VMDiff, novel unified and controllable framework for visual concept fusion that synthesizes coherent new objects directly from two input images. Our approach en- ables fine-grained control by semantically integrating concepts both the noise and latent levels. VMDiff consists of two core components: (1) hybrid sampling process that constructs optimized semantic noise through guided denoising and inversion, followed by curvature-awan latent fusion using spherical interpolation, and (2) an efficient adaptive adjustment algorithm that refines fusion parameters via lightweight, score-driven search. Experimental results on curated benchmark demonstrate VMDiff's superior performance, excelling in semantic consistency, visual harmony, and user-rated creativity, thereby establishing new paradigm for hybrid object synthesis. This work offers practical and valuable insights for professionals developing combinational characters, directly applicable to diverse fields from film and animation to figures and industrial design.
ACKNOWLEDGMENTS
This work supported by National Natural Science Fund of China (Nos. U24A20330, 62361166670, 62506155), Provincial Natural Science Fund of Jiangsu (Nos. BK20251985), and Suzhou Municipal Leading Talents Fund (Nos. 2XI.2025320).

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Reviews:
[{"review_id": "yjCLJEf5nv", "reviewer": "Reviewer_EaME", "summary": "This paper presents VMDiff, a diffusion-based framework for visual mixing: fusing two input images into a single, coherent hybrid object rather than mere juxtaposition or style transfer. The method has two pillars: (1) a Hybrid Sampling Process (HSP) that first builds BNoise (guided denoising + inversion over a scale-concatenated pair of visual embeddings) and then performs MDeNoise with spherical interpolation of the two image embeddings, and (2) an Efficient Adaptive Adjustment (EAA) that automatically searches fusion parameters \nusing a Similarity Score balancing visual and semantic similarity and balance terms. The authors also introduce IIOF, a new benchmark and report strong quantitative metrics, extensive ablations, and user studies showing clear preference for VMDiff over recent multi-concept and mixing/editing baselines.", "questions": "Will you release the model and the data? It would be helpful for the image editing community.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The task is interesting. The paper targets object-level fusion—a regime where many image editing/compositional models struggle (coexistence or bias toward one concept).\n\n2. Well-designed fusion pipeline. The BNoise + MDeNoise split is principled: concatenation before inversion preserves fine details; spherical interpolation during denoising yields seamless integration.\n\n3. The authors conduct a thorough evaluation. A new benchmark (IIOF) + diverse baselines, VQA- and critic-based scores, balance metrics, extensive ablations, and user studies strengthen empirical claims.\n\n4. The authors provide Insightful analysis. The paper’s BNoise vs. MDeNoise discussion and concatenate vs. interpolate design choices are well argued and supported by qualitative/quantitative evidence.\n\n5. The presentation, figures, and algorithm descriptions are clean and easy to follow.", "weaknesses": "I do not have too many concerns. My main concerns are about the computational cost and robustness of the whole pipeline for in-the-wild input.\n\n1. The multi-stage HSP + EAA (multiple generations per search step, occasional resampling) can increase inference latency and compute cost, limiting practical throughput.\n\n2. Robustness and failure analysis are light. The paper lacks a systematic robustness study and only briefly touches on failure cases (e.g., large semantic/structural gaps).", "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": "YaMpy76ovu", "reviewer": "Reviewer_S3DV", "summary": "The paper presents VMDiff, a inference-time optimization framework for two image visual mixing using FLUX-Krea. When attempting visual mixing with existing image generation models, they often struggle with coexistence and bias issues, resulting in conceptually disjoint images. To address these problems, the authors introduce Hybrid Sampling Process (HSP) and Efficient Adaptive Adjustment (EAA). To address these issues, the authors introduce Hybrid Sampling Process (HSP) and Efficient Adaptive Adjustment (EAA). The Hybrid Sampling Process (HSP) includes Blending Noise (BNoise), which concatenates two latents during the inversion process to preserve the original information of both images, and Mixing Denoise (MDeNoise), which interpolates the two latents to fuse the images. Efficient Adaptive Adjustment (EAA) searches the optimal $\\alpha$ and then adjusts $\\beta_1$, $\\beta_2$ based on Similarity Score (SS) between the input images and texts, which are used as parameters in MDeNoise. Experiments demonstrate the effectiveness of the proposed framework for visual mixing on the newly proposed IIOF (Image-Image Object Fusion) dataset, both quantitatively and qualitatively.", "questions": "1. How much GPU memory is required to mix one pair of images?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper presents strong qualitative results demonstrating visually compelling outputs. \n2. The training-free approach built upon FLUX-Krea is practical.\n3. The experiments show that Blending Noise and Mixing Denoise are sufficient to achieve effective visual concept mixing.\n4. A diverse set of experiments provides thorough analysis and validation of the proposed methods.", "weaknesses": "1. The citation format in paragraphs appears inconsistent with the conference’s official style guidelines, which significantly reduces readability.\n2. I wonder whether the proposed method is specifically tailored to FLUX-Krea or if it can also be applied to other flow-based models, potentially with different optimal parameters. Demonstrating such generalizability would substantially strengthen the paper and highlight the broader applicability of the approach.\n3. The proposed evaluation dataset covers a limited range of categories (40 selected classes), though this limitation is not critical to the overall contribution. \n4. In Lines 79–80, the paper seems to reference the wrong figure example. The sentence “For instance, OmniGen Xiao et al. (2025) generates the doll figurine while entirely neglecting the horse.” likely refers instead to DreamO Mou et al. (2025). \n5. In Line 373, there is a typographical error, an extra comma.", "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": "dgoe9dskRR", "reviewer": "Reviewer_wsRz", "summary": "This paper proposes a framework (VMDiff) for visual cues fusion and mitigates the coexistent generation and bias generation problems that exist in previous works. The pipeline incorporates two important modules: the hybrid sampling process and the adaptive parameter adjustment for efficient latent space manipulation for semantic information integration and conditioning. VMDiff generally outperforms previous approaches under different evaluation settings.", "questions": "1. Evaluation: (a). It would help to validate the comparison if more elaborations or explanations about the evaluation could be provided based on the questions in weakness 1. (b) It's interesting to see how different values given under these metrics for different inputs are semantically good. What are the prompts? Are they all the same? For example, the first column in Figure 6, the results about VMDiff, DreamO, OmniGen, and GPT-4o all seem valid.\n2. In the Table 2 ablation part, the comparisons between rows 1 vs. 2 and 3 vs. 4 indicate that the improvements from pure BNoise and $\\beta$ search are not obvious. It would help if we had more scores in the setting baseline1 vs baseline 2 vs baseline 1+ BNoise ($\\beta$ search) to isolate the improvement caused by BNoise; setting: baseline 1 vs. random noise + MDNoise ($\\alpha$ search) to isolate the influence of  MDNoise. It is currently not obvious why the $\\epsilon_b$ incorporates more semantic information instead of just Gaussian noise differently sampled.\n3. Is this framework commutative? For example, will \"a photo of <$T_1$> fused with <$T_2$>\" be more similar to $I_1$ while \"a photo of <$T_2$> fused with <$T_1$>\" is inclined to generate an object similar to $I_2$?\n4. How to isolate the improvement proposed by base models? Specifically, the MIP-Adapter is based on sdxl, DreamO is under Flux-1.0-dev, with Krea is more updated. Would some quick validation on these base models using the proposed framework help prove the effectiveness?\n5. One possible problem with the current evaluation of concatenation in the second row is that the $\\alpha$ changes so small, which may influence a lot under the interpolated space but not be obvious when two latens are separately used. What would happen for other simple values, such as concat($\\alpha z_1,(1-\\alpha)z_2$)", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The method proposed in the paper introduces free lunch, which can be seamlessly integrated into different diffusion architectures, which could be an interesting direction to explore the general usage cases.\n\n2. Detailed information on design choices over qualitative figures and quantitative numbers is given to showcase the effectiveness of the proposed approaches.\n\n3. A benchmark is proposed to demonstrate the effectiveness of the proposed method.", "weaknesses": "1. The quantitative evaluation details are ambiguous. For example, in Table 1, apart from the proposed approach, MIP-Adapter outperforms OmniGen and DreamO; however, this seems opposite in Figure 6, where MIP-Adapter is less photorealistic compared to all others, which can also be demonstrated by Table 5 in the user study. This phenomenon makes the values less convincing.\n\n2. In the ablation study, most value improvements are introduced by MDNoise; the changes brought by BNoise are not obvious.\n\n3. The paper proposes a method focusing on the fusion of two different objects into another one. In some baselines, such as DreamO and OmniGen, they target a broader and more generalizable application (supporting different prompts with the coexistence of two objects). It would be more intuitive to compare with other papers targeting the same application scale. Besides, it seems this framework is training-free. It could help to compare with other training-free approaches, which might help increase the soundness of the proposed method.", "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": "cdOJmWgqeg", "reviewer": "Reviewer_4syL", "summary": "This paper introduces an inference-time method for generating novel, visually coherent hybrid objects from customized image inputs. It requires no retraining or fine-tuning of the underlying diffusion model; instead, it optimizes a small set of fusion parameters during inference to effectively combine visual and semantic information from two distinct sources.\n\nThe authors identify two key failure modes in existing pretrained models when performing cross-object synthesis: coexistent generation, where objects are merely overlaid or placed side-by-side without true integration, and bias generation, where one object dominates the output while the other is largely ignored due to semantic imbalance. To address these issues, VMDiff proposes a Hybrid Sampling Process (HSP) and an Efficient Adaptive Adjustment (EAA) module. HSP integrates inputs at both the noise and latent levels, enabling seamless fusion through curvature-aware spherical interpolation. Meanwhile, EAA automatically searches for optimal fusion parameters by maximizing a similarity-based score that balances visual fidelity, semantic consistency, and inter-object symmetry.\n\nExperiments on the newly curated IIOF benchmark (780 images) demonstrate that VMDiff consistently outperforms state-of-the-art baselines. User studies also show a strong preference for VMDiff’s outputs.", "questions": "1. Is manual intervention feasible—for instance, to selectively enhance the identity features of one image or the stylistic attributes of another?\n2. In certain scenarios, fine-grained control over specific regions may be required—such as replacing a human hand with a robotic arm, or even transforming a foot into a robotic arm. Can this method reliably discern subtle distinctions in the prompt and generate outputs that accurately reflect such targeted modifications?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. This work clearly defines the visual mixing task and identifies two key failure modes—coexistent and bias generation—in existing methods.\n2. The proposed Hybrid Sampling Process effectively combines noise-level and latent-level fusion, enabling both detail preservation and semantic integration.\n3. The method is evaluated on a new benchmark with diverse object pairs and shows consistent improvements over strong baselines in both automatic metrics and human studies.\n4. This work shows a clear generation process of the proposed pipeline", "weaknesses": "1. **Lack of multi-image generalization**: All experiments are limited to pairwise fusion; the method’s extensibility to three or more input images is neither explored nor validated.  \n\n2. **No cross-model evaluation**: As an inference-time approach, its compatibility with other diffusion backbones (e.g., SD3, SDXL) is not tested, leaving the potential of this method unclear\n\n3. **Missing time-cost ablation**: Owing to high inference latency (166 sec/pair), the paper should break down the computational overhead of individual components (e.g., BNoise, α/β search, SS scoring), to justify the necessity of each design", "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)