MiraGe: Editable 2D Images using Gaussian Splatting
Authors: Joanna Waczynska
Tomasz Szczepanik
Piotr Borycki
Slawomir Tadeja
Thomas Bohné
Przemysław Spurek
Venue: ICML 2025 poster (2025)
Keywords: Gaussian Splatting
image reconstruction
INR
Primary area: applications->computer_vision
TL;DR: We propose adaptation 3D Gaussian Splatting for 2D images which allows real-life modification of images.

Abstract: Implicit Neural Representations (INRs) approximate discrete data through continuous functions and are commonly used for encoding 2D images. Traditional image-based INRs employ neural networks to map pixel coordinates to RGB values, capturing shapes, colors, and textures within the network’s weights. Recently, GaussianImage has been proposed as an alternative, using Gaussian functions instead of neural networks to achieve comparable quality and compression. Such a solution obtains a quality and compression ratio similar to classical INR models but does not allow image modification. In contrast, our work introduces a novel method, MiraGe, which uses mirror reflections to perceive 2D images in 3D space and employs flat-controlled Gaussians for precise 2D image editing. Our approach improves the rendering quality and allows realistic image modifications, including human-inspired perception of photos in the 3D world. Thanks to modeling images in 3D space, we obtain the illusion of 3D-based modification in 2D images. We also show that our Gaussian representation can be easily combined with a physics engine to produce physics-based modification of 2D images. Consequently, MiraGe allows for better quality than the standard approach and natural modification of 2D images.

MiraGe: Editable 2D Images using Gaussian Splatting
Joanna Waczyúska' Tomasz Szezepanik'
Piotr Borycki' Slawomir Tadeja 2
Thomas Bohné
Przemyslaw Spurek 14
Abstract
Implicit Neural Representations (INRs) approxi- mate discrete data through continuous functions and are commonly used for encoding 2D images. Traditional image-based INRs employ neural net- works to map pixel coordinates to RGB values, capturing shapes, colors, and textures within the network's i weights. Recently, Gaussianlmage has been proposed as an alternative, using Gaussian functions instead of neural networks to achieve comparable quality and compression. Such a so- lution obtains a quality and compression ratio similar classical INR models but does not allow image modification. In contrast, our work intro- duces novel method, MiraGe, which uses mirror reflections to perceive 2D images in 3D space and employs flat-controlled Gaussians for precise 2D image editing. Our approach improves the rendering quality and allows realistic image mod- ifications, including human-inspired perception of photos in the 3D world. Thanks to modeling images in 3D space, we obtain the illusion of 3D-based modification in 2D images. We also show that our Gaussian representation can be eas- ily combined with a physics engine to produce physics-basee based modification of 2D images. Conse- quently, MiraGe allows for better quality than the standard approach and natural modification of 2D images.
GAUSSIAN SPLATTING
Reconstruction
3D
User-driven
modification
Physics: applied soft body
Modification
GAUSSIAN SPLATTING
scaling
Figure I. MiraGe encodes 2D images with paramcterized Gaus- sians, enabling high-quality reconstruction and real-life-like modi- fications. The selected parts of the image can be transformed in 3D space, creating effect with a physics engine controlling movement and interactions.
(Lu, 2019; Davoodi et al., 2023). In line with this trend, we introduce a model that encodes 2D images by simulating human interpretation. Specifically, our model perceives 2D image human would view photograph of paper, treating it as flat object within 3D space. This approach allows for intuitive and flexible image editing, capturing the nuances of human perception while enabling complex transformations (Fig. 1).
sheet
1. Introduction
Recent research has increasingly emphasized human per- ception and the understanding the world through this lens
'Jagiellonian University, Faculty of Mathematics and Computer Science "Doctoral School of Exact and Nat- ural Sciences UUniversity of Cambridge *IDEAS Re- Joanna Waczyáska
Gaussian Splatting (3DGS) framework models the structure of 3D scene using Gaussian components (Kerbl al., 2023). In the 2D domain, GaussianImage (Zhang al., 2024) has shown promising results in image reconstruc- tion by efficiently encoding images in the 2D space, with a strong focus on model efficiency and reduced training time. Unfortunately, Gaussian does not support user-driven adjustments of scene objects, which is key feature of
search Institute. Correspondence <joanna. waczynska @ poo.ooal..uj..du.p... Przemystaw Spurek <przemyslaw spurekkwwwssukkueee wwj.urkkkuje......o edu.pl>.
Proceedings of the ndnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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MiraGe: Editable 2D Images using Gaussian Splatting
Controlled flat Gaussians with GaMeS parametrization
Train set preparation
Animation & Triangle soup modification by user
Input: Image
Image's reconstruction & Triangle soup
Animation & Modification using Physics engine
Human Perception of 2D Photography
gv
pnyside angine
t
3D environment
smooth movement
Figure MiraGe employs 3D flat parameterized Gaussians space to encode 2D images, representing each Gaussian as three points, forming a cloud of triangles called a triangle soup. This representation enables real-time manipulation of the 3D triangle/point clouds, allowing for flexible, real-world modifications. The model seamlessly integrates with a physics engine, enhancing its applicability in dynamic environments.
3DGS. While GaussianImage has explored image represen- tation using 2D Gaussians primarily for data compression, our research highlights an additional benefit, i.e., the use of parameterized flat 3D Gaussians for editing 2D images. In our work, we address this by introducing the MiraGie,, model, which encodes 2D images through the lens human perception, bridging the gap between 2D image representa- tion and 3D spatial understanding (Fig. 2).
Reconstruction
Using 3rd dimension to modification
Building the foundational idea that humans intuitively can perform transformations on pooggrapps-primarri through affine transformations and bending them beyond the 2D plane-we introduce a novel approach using flat Gaus- sians with GaMeS parametrization (Waczyúska et al., 2024). This capability enables our model to support image editing in both 2D and 3D spaces. Notably, our framework sim- plifies often difficult perspective adjustments by allowing intuitive modifications directly within the third dimension (Fig. 3).
Animation Triangle Soup in 3D using Blender
Figure 3. Parameterized flat 3D Gaussians provide a powerful rep- resentation of 2D images, cnabling flexible editing in 3D space. Triangle Soup can be animated using tools like Blender. The col- ored lines depict the motion paths of 10 randomly selected points during the simulation.
In addition to classical edits, our model has the unique capa- bility of interfacing with physics engines, enabling applica- tions that enhance the realism and immersiveness of anima- tions (Jiang al., 2024). MiraGe treats the physics engine black box and offers three distinct methods for control- ling Gaussians, i.e., 2D, Amorphous and Graphite. For 2D representation (2D-MiraGe) we used Raichi_element for Craphite-MiraGey
we use Blender This flexibility makes our model highly applicable to various fields, such as computer graphics for populating spatial interfaces, where realistic, physies-factual 2D animations can be incorporated (Tadeja al., 2023).
Embedding 2D images in 3D space allows for seamless in- tegration of 2D and 3D objects, enabling the creation of dy- namic backgrounds or interactive elements within animated scenes. This versatility extends to applications such as vir- tual reality, where 2D images can function as backdrops (Yin et al., 2024). This capability opens up new avenues for creative composition, offering a powerful toolset for
3D representation AAmorphous-MiraCK
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MiraGe: Editable 2D Images using Gaussian Splatting
Concatenation
Ground truth
Gaussianlmage
MiraGe
Image 20 MiraGe model
Background modification
Image 2
2D-MiraGe model
Figure 5. Visual comparison of two Gaussian- basse methods for 2D image reconstruction. From left right, the columns display the ground truth image, the Gaussian reconstruction, and the MiraGe reconstruction. The bottom row illustrates the differences between the ground truth image and the results of each method.
Figure 4. Two images were encoded using the MiraGe model on distinct planes within a 3D space. This setup allows for seamless integration of the encoded images, resulting in a collage-like com- position. Morcover, the model facilitates editing capabilities, as illustrated here, with modifications to the background image (the rear plane).
for highly compact and efficient representations (Xie et al., 2022). This has led to the development of several special- ized models for image INR, such as SIREN (Sitzmann et al., 2020a), Fourier feature mapping (Tancik et al., 2020), and WIRE (Saragadam et al., 2023a). The growing field of re- search resulted in further improvements of already existing solutions, c.g. in (Liu et al., 2024b), certain limitations of SIREN, namely the arising capacity successfully alleviated with the idea of variable-periodic activation functions. Yet another worth noting work from this area is (Müller et al., 2022) with INR solution designed to effectively perform on modern computer architecture uti- lizing a simple data structure concept of hashmap to offer speed-oriented image representation with high fidelity of the outcomes.
users. The novelty of this work lies in its ability to enable easy, intuitive 3D transformations and integrations within a traditionally 2D framework, expanding the possibilities for both image editing and animation (Fig. 4).
gap, were
Since high-quality image reconstruction is critical in anima- tion, we compared MiraGe with other models, in particular with Gaussianlmage (Fig. 5), showing our state-of of-the-art performance in the image reconstruction task.
is worth highlighting that flat 3D Gaussians can be utilized for 2D images in four distinct scenarios, with modifications that emphasize how controlling the Gaussians during train- ing affects the perspective of viewing each image (Fig. 6). The following constitutes list of our key contributions:
Alternative approaches to INRs were presented in Gaus- sianImage (Zhang et al., 2024). Instead of neural networks, the authors propose approximate 2D images using 2D Gaussian components. In practice, Gaussianlmage is a 2D version of 3DGS (Kerbl et al., 2023) that uses 2D Gaussians instead of their 3D version and a simplified rendering proce- dure. Thanks to such a modification, the Gaussianlmage is invariant to the order Gaussian components. Therefore, such model is numerically efficient.
We introduce the MiraGe model, which represents 2D images using flat 3D Gaussian components, achieving state-of-the-art reconstruction quality.
MiraGe enables the manipulation of 2D images within 3D space, creating the illusion of 3D effects.
We integrate MiraGe with physics engine, enabling physics-based modifications and interactions for both 2D and 3D environments.
Gaussian represents each pixel color weighted sum of 2D Gaussians. The training procedure is similar to 3DGS without pruning. The authors show that such repre- sentation gives a similar reconstruction quality to classical INR models and is able to obtain a high compression ratio and fast rendering.
2. Related Works
Our work builds on several key research areas, including image reconstruction techniques, Gaussian- represen- tations and Gaussian animation frameworks.
The interactive image editing of 2D images has been widely explored in computer graphics Here, some methods lever- age the current advancements in generative models. For instance, (Pan et al., 2023) introduce DragGAN, enabling point- manipulation of images by performing them on the underlying manifold of GAN, achieving realistic edits. Similarly, (Shi et al., 2023) propose DragDiffusion, which
One rapidly growing area in image reconstruction is Im- plicit Neural Representations (INRs), which have attracted significant attention for their ability to model continuous signals, such as images, through neural networks (Klocek et al., 2019). INRs encode spatial coordinates and map them to corresponding values, such as RGB color, allowing
3

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MiraGe: Editable 2D Images using Gaussian Splatting
Modifications
Reconstrucion
Model
Gaussian control method
Baseline
Amorphous; one camera (3D representation)
MiraGe
Amorphous: mirror camera (3D representation)
MiraGe 2D: mirror camera (2D representation)
MiraGe Graphite; mirror camera
(3D representation)
Figure demonstrate three approaches for Gaussian control: Amorphous, 2D, and Graphite. As baseline, we utilize single camera from the Amorphous setup. After applying perspective editing in 3D, the image shows noticeable deformation. In contrast, deformation observed when employing an additional camera. model employs mirror setup during training, with Amorphous configuration achieving the best results for image reconstruction and 3D analysis. The 2D model represents images single plane. The Graphite model operates across multiple planes, making it idcal for 3D spatial reasoning and image combination.
extends the previous framework to diffusion models, enhanc- ing the control and applicability of image editing. On the other hand, (Jacobson et al., 201 1) propose bounded bihar- monic weights for linear blending, which produce smooth and intuitive deformation for handles of arbitrary topology. (Wang et al., 2015) further advances this field by proposing linear subspace design, unifying linear blend skinning and generalized barycentric coordinates to provide a practical way of controlling deformations.
outcomes (Waczyáska et al., 2024; Waczyáska et al., 2024). This approach enables flexible and continuous deformations, offering an intuitive method for controlling object shapes and rendering properties, which has proven particularly use- ful in tasks like texture mapping, surface smoothing, and dynamic simulations.
Gaussians enable precise and flexible editing of objects, pro- viding continuous control over shapes and transformations. Moreover, integrating physics engines enhances these ca- pabilities, allowing for more sophisticated and physically consistent modifications, such as simulating realistic inter- actions, deformations and movements in 3D environments. (Xie et al., 2024; Borycki et al., 2024).
The representation and editing of objects using Gaussians is well-explored topic in 3D graphics. In this field, meshes can be modified to simulate Gaussian editing (Guédon & Lepetit, 2024; Huang et al., 2024), Gaussians can be di- rectly parameterized and manipulated to achieve specific

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MiraGe: Editable 2D Images using Gaussian Splatting
Reconstruction
Physical modification
Ground Truth
DragGAN
MiraGe
Figure 8. Visual comparison of image editing techniques, demon- strating the effectiveness of representing 2DD images with parame- terized Gaussians applied to Triangle Soup. This approach enables highly realistic animations, achieving results comparable to those of generative models. Specifically, local editing operations pre- serve fine details. such as dimple related regions. Moreover, we can achieve precise manipulations. including subtle edits like closing lion's mouth, underscoring the flexihility and control inherent in our method.
face, without affecting un-
Figure 7. Integration of MiraGe with MPM enables realistic 2D image alterations. The first column shows the original image, the next two capture mid-simulation renders, and the last presents the final result with colored lines tracking point trajectories.
OX
50
-ur 50k
our-100k
PSNR
-Guussandmage GsussianSplattingg
40
3. MiraGe: Editable 2D Images using Gaussian Splatting
15
30
5k
15k
20k
25k
30k
Here, we describe in detail the inner workings of our MiraGe model. We start by presenting classical 3DGS. Next, we present GaMeS- based (Waczyńska et al., 2024) parametriza- tion of flat Gaussians. In the end, we present our MiraGe and how it relates to prior works.
Iteration
Figure 9. Comparison of PSNR obtained on a butterfly image from DIV2K dataset by MiraGe, Gaussianlmage and GS. Vertical lines represent iteration, where MiraGe obtained better results than Gaussianlmage, the time in minsec format above each line is the training time until this iteration.
3D Gaussian Splatting 3DGS models 3D scene by a set of Gaussian components with color and opacity:
GaMeS Parametrization of Gaussian Component In Mi- raGe, we use flat Gaussian components in 3D space. In such a model we use Gaussian components with a covari- ance matrix Σ, factored as: Σ = RSSRT. where rotation matrix, and S is diagonal matrix containing the scaling parameters. However, we foree of the scale parameters to be zero. Consequently, we obtain collection of flat Gaussian:
g ((M,m, Ex), o, G=)
defined by their mean (position) m;, covariance matrix E,, opacity oe and color c, which is represented using spherical harmonics (SH) KFridovich-Keil et al., 2022).
the
During the rasterization stage, the 3DGS produces a sorted Gaussian list based on the projected depth information. Then, the a-blending method is used to create the image. We refine the Gaussian parameters, color, and opacity in the training phase according to the reconstruction cost function. The optimal number of Gaussians required to represent a given object is not known a priori, and it is non-trivial to adjust the number of Gaussians. Hence, the initial number of Gaussians is a parameter of the method. The authors implement additional strategies for reducing and multiply- ing Gaussians. Gaussians with low opacity are removed, while those that change rapidly during optimization are mul- tiplied. These strategies make the 3D Gaussian approach very efficient and capable of generating high-quality ren- ders. We used this strategy to reconstruct 2D images, which distinguishes us from GaussianImage.
g = ((W(m, R,, oz),
(I)
where S = diag(s1. $2, s3), with =, and R is the ro- tation matrix defined as R = [rı, r2, r3], with r, E R². In such a case, we can use GaMeS parametrization to represent flat Gaussian by triangle-face mesh. This mapping is de- noted by T(-). When applied, this parametrization generates set of triangles labeled triangle soup.
To outline the GaMeS parametrization, consider Gaus- sian component N Wmm, R, S), characterized by the mean m, the rotation matrix R = [ri, r2, r3l and the scaling ma- trix S = diag(s, $2, 83). Then its face representation (V) is based on a triangle: V = [V1, V2. vs] T(m, R, S)
5

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MiraGe: Editable 2D Images using Gaussian Splatting
with the vertices defined as: V1 m, V2 8212, and V3 = + 83 Conversely, given face (tri- angle) representation V = [V1, V2, va], we can recover the Gaussian component (mm, R. S) = (T (V)) (V)) through the mean m, the rotation matrix R = F1, f2, it]. and the
computed by multiplying this value by the camera aspect ratio.
Consequently, the initialization of Gaussians is consistently performed on the XZ plane; however, we have opted to permit their movement within the 3D space. Drawing in- spiration from three distinct models, we introduce three conceptual approaches for manipulating the spatial position- ing of Gaussians.
scaling matrix S = diag($1, $2, 8z), where the parameters are defined by the following formulas:
(V2- - vi) (V3 - -v)
m = V1, f1 =
- ([v2-v1)
(V3- - v1i)l
Amorphous The baseline approach how to control Gaus- sians is based on the classical GaMeS parametrization, ini- tialized randomly on the X Z plane, with the mean parame- ter's y coordinate set to zero:
F2 = (V2 - V1)
F33 = orth(v3 - v1;
[[V2- v1)ll"
81 =E E, 82 = V22- V1ll. and 83 = (V3 - V1, fs).
g = [[V((m 0, ma), [ri, r2, ral, diag(e, 82, 83)), oi 6i)}
(2)
Here orth(.) denotes a single step of the Gran-Schmidt process (Björck, 1994). Accordingly, the corresponding covariance matrix of a Gaussian distribution is given as RT
where m [m1, 0, ms] S = diag(s1, 82, 8a), with 81 E, and R is the rotation matrix defined as R = [ri, r,,, r3). with E R
It should be highlighted that we only initialized the Gaussian component on the X plane. During training, Gaussians can move amorphously in 3D space. We use the classical loss function L1 combined with D-SSIM term:
The parametrization enables control over the Gaussians' position, scale, and rotation by manipulating the underlying triangle mesh. Applying transformations to the triangle directly alters the corresponding Gaussian.
C= (1- GS(() + XCD-SSIM GS(()), (L)()),,
MiraGe In this work, we present an approach that lever- ages the concept of flat Gaussian distributions in 3D space to model single 2D image input. Our methodology is grounded in human visual perception. This perspective allows us to reframe the problem: instead of merely process- ing a pixel matrix, we interpret the images as objects with a fixed spatial configuration in a 3D environment.
where I is the input image and GS(T) is the constraint ob- tained by the Gaussian renderer. While this solution enables the modeling of images using a collection of triangles, often referred to as "triangle soup." it proves insufficient for high- quality representations. During editing, significant artifacts emerge (Fig. 6 6-Baseline).
We put the 2D image on the XZ plane where the center is situated axes origin (0, 0) with the fixed distance from the camera origin. In practice, the distance from the plane is a hyper-parameter. In our approach, we model flat objects within 3D space, where the camera distance parameter effectively controls the perceived scale of the object. This relationship allows for intuitive adjustments of object size based on the desired visual effect. For instance, increasing the camera distance can naturally expand the apparent size of background elements like distant mountains (Fig. 4), making it easier to represent them as larger objects without additional modeling complexity. While this feature is beneficial, it is not strictly necessary for most application.
2D Building on the promising outcomes of GaussianIm- age we anchored all Gaussians to the X plane, translating flat image geometry into a spatial framework. We set the mean of these components to zero in the second coordinate. Morcover, we use the projection of flat Gaussians on a 2D plane. Unfortunately, orthogonal projection can produce artifacts. Therefore, we use a rotation of Gaussian compo- nents lay on the X Z plane. Since use flat Gaussians extract such rotation, two vectors to align the vector in 3D (Markley, 1993). We use the notation Rot(a, b) for the rotation matrix.
can use rotation matrix between
MiraGe on 2D plane is defined by set of 3D parameterized Gaussian components:
We propose a method that situates the Gaussians within the X plane, ensuring that the entire image remains vis- ible under perspective projection. To achieve this, the possible range of v-values and z-values is calculated us- ing the camera field of view. We first calculate the devia- tion from 0 on the X axis using the similarity of triangles dev, = camdis tan(} Fo) where camjist and Fov are camera distance from the XZ plane and camera field of view respectively. The deviation the X axis can be then
g (Wim,, R. Rot e2), S,,,,,)))
where 5 = diag(si, (81, 82, 83), with 81 = E, e2 = 00 1, 0]. m [m,, 0. ma). and R; is the rotation matrix defined as R. = [ri, r2 ra), with r, E E3,
Graphite Unfortunately, 2D-MiraGe produces artifacts when we use modification in 3D space (see the third row in
6

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MiraGe: Editable 2D Images using Gaussian Splatting
DragGAN
MiraGe
Reconstruction
Mocifications
Inpainting
Figure II. MiraGe model allows modifications in 3D space, but the model is limited by 2D images, which was used in training. When we move some elements from the foreground, we cannot see the background since the model only reconstructs objects. Next, we can use image Inpainting to fill the missing parts, allowing for more realistic modifications.
Figure 10. We compare the animation capabilities of MiraGe DragGAN model, highlighting the advantages of our Gaussian- based image representation. This approach enables highly realistic edits by not relying on generative techniques. Our method offers greater control during animation. For example, adjusting the posi- tion of leg does not inadvertently alter facial features.
flipping the image, denoted as M(I). This mirror-camera setup enhances the fidelity of the generated reflections, pro- viding a robust solution for accurately capturing visual el- ements. We consider the additional camera as a means of augmenting the dataset to improve the accuracy of the representation. The MiraGe is initialized according to equa- tion Eqn. and utilizes a cost function: We simultaneously model both the image and its mirrored reflection, shown in the second row in Fig. 6. We pro- vide numerical comparisons in the ablation study in the Appendix.
Fig. 6). Such an effect coursed by the Gaussians, which appear randomly according to the camera position. To solve such a problem and obtain the possibility of 3D modifica- tions, MiraGe allows the Gaussians to leave the XZ plane:
C(I) + L(M(I)).
g = [[( + ye2, R, Rot(rs, e2), S, S,,,,
where y is trainable parameter of translation scale along the vector e2 = [0, 1, 0). m = [m1,0, 0, ma], S= diaa(($), $2, $3), with $1 E, and R² is the rotation matrix defined as: R; = [ri, r2, r3], with r; R3, Such model allows for the order of Gassians according to camera positions.
After thorough experimentation, we find that our model, Amorphous-MiraGae utilizing mirror camera, achieves state-of-the-at reconstruction results. This model demon- strates significant advantages over alternative methods in terms of both performance and outcome quality.
Editability The ability to manipulate Gaussians based on their spatial positioning empowers MiraGie to effectively edit 2D images. When utilizing a mirror camera, the quality of the resulting images is sufficiently high, enabling the pa- rameterization and animation of Gaussians significantly reduce artifacts. Our findings demonstrate that our model fa- cilitates the animation of both segmented objects and entire scenes. Users can create manual animation or leverage auto- mated processes using physics engines like Taichi_element _elements or Blender (Fig. 3,7). To incorporate MiraGe with the 2D physics engine, we use 2D-MiraGe (Fig. 7). In Fig. 8, we demonstrate that our method can also be applied edit more complex scenes, such as changing human expression.
By leveraging parameterized Gaussians, we achieved pre- cise manipulation of 2D images directly within their native 2D space, enabling targeted edits of segmented regions and transformations of complete scenes in an easier way. While this approach demonstrated substantial promise, we observed significant artifacts when extending manipulations into the 3D domain, particularly along the Yaxxis, see first and last row in Fig 6.
Mirror camera We employ a novel approach utilizing two opposing cameras positioned along the Y axis, symmetri- cally aligned around the origin and directed towards one an- other. The first camera is tasked with reconstructing the orig- inal image, while the second models the mirror reflection. We introduced the mirror camera to ensure that Gaussians remain confined within a specific spatial region between the cameras, enhancing control and precision.
We argue that the traphite-inspire model allows the cre- ation of attractive compositions made of multiple images that effectively present the positive attributes of the layered structure, like Graphite, through the strategic positioning of Gaussians.
The reflection can be effectively represented by horizontally
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MiraGe: Editable 2D Images using Gaussian Splatting
Table Quantitative comparison with various baselines in PSNR and MS-SSIM. MiraGe gives state-of-the-ard results. Model-zr denotes that the model was initialized with æ Gaussians. The metrics for the baselines were taken from (Zhang et al., 2024); however, we re-ran the experiments for Gaussianlmage (GI) -70K (GI-70K) and our model using a V100 GPU to ensure a more reliable comparison. Those experiments are denoted in the following table by asterisk next to the method name.
Kodak dataset
DIV2K dataset
PSNR
MMS-SSIN
Train Time(s)
LPSNR MMSSSIM TTrai Timo(s) &
WIRE
41.47
0,9939
14339
35.64
0.9511
25684
SIREN
40.83
0,9960
6582
39.08
0,9958
15125
I-NGP
43.88
0.9976
491
37.06
0.9950
676
NeuRBF
43.78
0,9964
992
38.60
0.9913
1715
3DGS
43.69
0.9991
340
39,36
0.9979
481
Gausianlmaae-70K
44.08
0,9985
107
39.53
0,9975
121
G0ussian|mage-70k*
44.12
0.9985
116
39.53
0.9975
112
Gaussianlmage-100k
38.93
0.9948
126
41.48
0.9981
120
MiraGe- 70k; 5k iter (our)
49.07
0.9993
57
44.37
0,9989
MiraGe- 100k; 5k iter (our)
51.04
0.9995
59
46.23
0,9992
79
MiraGe-70ke 70k: 30k iter (our)
57.41
0,9998
547
53.22
0,9996
789
PeraGe-IO0k:: 30k iter (our)
59.52
0.9999
560
54.54
0.9998
946
Reconstruction quality Our image reconstruction assess- ment utilizes two widely recognized datasets. Specifically, employ the Kodak dataser', which includes 24 images at resolution of 768 x 512, alongside the DIV2K valida- tion set (Agustsson & Timofte, 2017), which involves 2x bicubic downscaling and comprises 100 images with sizes ranging from 408 x 1020 to 1020 x 1020. The dataset was selected to facilitate direct comparison with the work of GaussianImage. As a baselines we use competitive INR methods GaussianImage (Zhang et al., 2024), SIREN (Sitz- mann et al., 2020b), WIRE (Saragadam al., 2023b), I- NGP (Müller et al., 2022), and NeuRBF (Chen et al., 2023).
PhysGen
MiraGe
PhysGen
In Tab. 1, we demonstrate the performance outcomes of different methods on the Kodak and DIV2K datasets. We see that our proposition outperforms the previous solutions on both datasets. The quality measured by both metrics shows significant improvement compared to all the previ- approaches. Fig. 9 illustrates general trend observed during training in the contest of image reconstruction. The selection of hyperparameters, including the number of itera- tions, was inspired by the principles of 3DGS. We provide ablation studies and extensive numerical analyses in the appendix for further insights.
MiraGe
ILLI
Figure 12. Comparison between PhysGen (Liu et al., 2025) and MiraGe. In the animation of a red block, reasonable doubts related the correctness the simulated physics of PhysGen arise when a reader follows the behavior of a red element upon hitting the wall; contrary to everyday experienee, the front part (instcad of the back part) of the figure bounces off the tabletop. In another animation, domino pieces start overlapping throughout the simulation. Our renders have properly solved the mentioned issues.
Tab. 1 provides a comparison of training times across multi- ple baseline methods. For approach, we report the full training durations corresponding to 30000 and 5000 iteration steps. Our method achieved better PSNR with only 5000 iterations, while requiring significantly less training time overall. Using the butterfly image from the DIV2K dataset as case study, we showed that our method (Our- (Our-100K) surpasses GI in PSNR within just 30 seconds of training (Fig. 9).
4. Experiments
The experimental section is split into two main parts. First, we demonstrate that our approach achieves high-quality 2D reconstruction by comparing it with existing models. Sec- ond, we highlight the versatility of MiraGe in image editing full scenes (Fig. 8, 13) and selected objects (Fig. 1), pre- senting examples of user-driven modifications and demon- strations involving physical simulations (Fig. 3, 7).
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MiraGe: Editable 2D Images using Gaussian Splatting
Int camera
Change of camera perspective
move elements like the leg with realistic results and without compromising other aspects of the image.
Physics application in MiraGe Using 2D-MiraGe we can express Gaussian components with 2D point cloud. There- fore, we can use an MPM (Hu et al., 2018) based physics engine implemented, for example, in Taichi_elemente elliments. This high-performance physics engine supports multiple materi- als. We use inspiration from GASP (Borycki et al., 2024) and train simulation on 2D points, then use physical defor- mation triangle soup. In Fig. 7, we present simulation results obtained using Taichi elements. As we can see, we can add physical properties to 2D objects. On the other hand, using Amorphous-Minn MiraGe or Graphite-MiraGa we can use Blender and modify directly parameterized flat 3D Gaussian (Fig. 3). Moreover, we compare MiraGe with PhysGen (Liu al., 2025) (Fig. 12). Our method demon- strates better accuracy, ensuring that objects do not overlap. A key advantage is that users can directly and intuitively influence modifications.
Figure 13. Although the scene is not fully modeled in 3D, repre- senting a 2D image using 3D Gaussians enables novel viewpoints through camera movement. This creates a 2.5D effect, commonly used in games to simulate a background.
Fig. 9 includes the number of initial Gaussians, indicating how densely the space has been filled. We see clear up- ward trend in performance as the density of the Gaussian initialization increases.
Manual modification MiraGe allows for manual manipula- tion of 2D images. By leveraging GaMeS parameterization, each Gaussian component is represented as a triangle. Ver- tices can be independently adjusted and moved within 3D space, enabling flexible image modification (Fig. 2).
2.5D effect MiraGe presents a concept that combines 2D and 3D representations to achieve the 2.5D effect commonly used in video games and VR (Feyer et al., 2024). Although we do not reconstruct full 3D geometry, our method lever- ages the third dimension for spatial manipulation and ani- mation of 2D images. Fig. 13 illustrates this using a lantern image from the Kodak dataset, where camera movement reveals novel viewpoints. This 2.5D representation is espe- cially useful for static backgrounds such as mountains in VR, where full 3D modeling is unnecessary. In such cases, our approach offers an efficient alternative to generative 3D) models.
We demonstrate examples of modifications using datasets such as DIV2K, Kodak, and Animals Additionally, we generated our own 2D images using DALL-E 3 to illustrate the benefits of our method. We can obtain modifications of small details like changing fingers' position (Fig. 1), human facial expressions (Fig. 18), or dog poses (Fig. 11). As MiraGe can trained in a 3D context, we can implement modifications in the third dimension to create the illusion of 3D transformation (Fig. 3, 6).
5. Conclusion
crucial to note that when we displace elements from the foreground, the background remains unseen because the model only reconstructs the objects. This is demonstrated in Fig. 11, where artifacts are apparent on the hind paw of the animal depicted. To reduce such a problem, we can use Inpainting (Perche-Mahlow et al., 2024) on the image background.
In this paper, we introduce MiraGe that uses flat 3D Gaus- sian components to model 2D images. MiraGe gives state- of-the-art reconstruction quality and simultaneously allows image manipulation. Furthermore, we modify photos on plane (Fig. 7) and in 3D space (Fig. 3). In consequence, we obtain the illusion of 3D-based modifications. Further- more, we can combine our solution with a physics engine to obtain realistic motion in the image. Conducted experiments show that MiraGe is applicable in many different scenarios and produces high-quality simulations.
We conducted comparative analysis of our editing ap- proach against the DragGAN model (Pan et al., 2023). Here, we focus the ability to perform localized edits, such as closing the mouth, while preserving other features, such as dimples (Fig. 8). The visual results, presented in Fig. 10, highlight key distinctions between the two models. As Drag- GAN is a generative model, modifications often result in unintended global transformations, for instance, attempting to adjust leg's position may inadvertently modify facial features. In contrast, our method demonstrates the ability to
Limitation It is crucial to note that the model is not gen- erative, so improper adjustment of Gaussian positions can cause gaps in the image (e.g. missing dog's 's paw). This can be alleviated by using image Inpainting (Fig. 11). Although the model can produce realistic changes, a significant modifi- cation may introduce a visual artifact. Moreover, our model requires encoding more parameters than Gaussian achieve high-quality image reconstruction for animation.
hhtpps /wwp::///www.we aeg ctm datestts/ lesss peessicon oosssioooorddd999/ minmmmmlll
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MiraGe: Editable 2D Images using Gaussian Splatting
Acknowledgements
Feyer, S. P., Pinaud, B., Kobourov, S., Brich, N., Krone,
M., Kerren, A., Behrisch, M., Schreiber, F, and Klein, K. 2D, 2.5D, or 3D? An Exploratory Study on Multi- layer Network Visualisations in Virtual Reality- IEEE Transactions on Visualization & Computer Graphics, 30 (01):469-479, January 2024. ISSN 1941-0506. doi:
The project "Effective rendering of 3D objects using Gaus-
sian Splatting in an Augmented Reality environment" BEENG.02.02-IPOO5--4 14/23) is carried out within the First Team programme of the Foundation for Polish Science financed by the European Union under the European Funds for Smart Economy 2021-2027 (FENG). The work of P. Spurek was supported by the National Centre of Science, Poland Grant No. 2021/43/B/TT6004466 The research of T. Szezepanik was funded by the program Excellence Initia- tive Research University at the Jagiellonian University in Kraków.
Fridovich-Keie Keil, S., Yu, A., Tancik, M., Chen, Q.. Recht, B., and Kanazawa, A. Plenoxels: Radiance fields without neural networks. In The IEEE/ /VF Computer Vision and Pattern Recognition Conference, pp. 5501-5510, 2022.
Gao, J., Gu, C.. Lin, Y., Li, Z., Zhu, H., Cao, X., Zhang, L., and Yao, Y. Relightable 3d gaussians: Realistic point cloud relighting with brdf decomposition and ray tracing, 2024.
Impact statement
Editing images in pixel representation is a well-established technique with many existing solutions, and 2D image edit- ing tools (e.g. Photoshop. GIMP). our introduces represen- tation that integrates 2D images with 3D produce 2.5D effect, a technique widely adopted in video games and VR application. The approach streamlines image reconstruction and manipulation without complex 3D generative models. It democratizes 3D editing, benefiting digital art, AR/VR and scientific visualization. By making 3D effects accessible and computationally efficient.
Guédon, A. and Lepetit, V. Sugar: Surface aliiaed gaussian splatting for efficient 3d mesh reconstruction and high- quality mesh rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5354-5363, 2024.
Hu, Y., Fang, Y., Ge, Z., Qu, Z., Zhu, Y., Pradhana, A., and Jiang, C. A moving least squares material point method with displacement discontinuity and two-way rigid body coupling. ACM Transactions on Graphics (TOG), 37(4): 1-14, 2018
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MiraGe: Editable 2D Images using Gaussian Splatting
A. Appendix
Renders
Modification using Blander lattice (examples artifccis
Here, we provide a future directions and a comprehensive overview of the implementation details. Furthermore, we present supplementary experimental results, such as ex- tended performance evaluations and ablation studies focus- ing on camera settings.
Amorphous: cameras
(3D representation)
20; mirrr cameras (2D representation)
3
Graphiie; mirror
U2
cameras
U3
U2
representation)
U1
gV'
1
Figure 16. Example of artifacts generated during animation, typi- cally due to imperfect rendering. The model trained white background. leaving residual white Gaussians along the border of the camel's muzzle, leading artifacts. In this instance, the Graphine-MiraGi performed best in handling the head-turning movement
Figure 14, MiraGe GaMeS (Waczyúska al., 2024) represen- tations Gaussian by triangle soup. Therefore, we can use real-life modification by moving points.
A.1. Future direction
A.2. Implementation details
Since our model is built on flexible 3D Gaussian Splat- ting framework, can partially adapt techniques from other models. We identify two promising directions for extending the method. For instance, StyleGaussian (Liu et al., 2024a) allows for color adjustments during style transfer. We be- lieve that in practice, MiraGe can incorporate various style transfer methods for Gaussians. An important direction for future work is to investigate the adaptability of 3D Gaus- sians across diverse visual styles. Our model does not fully account for physically accurate lighting, leading to inconsis- tencies in light positioning. We believe that recent methods that incorporate real-world lighting (Gao et al., 2024; Bi al., 2024) are transferable. Unlike BRDF-based models, 3D Gaussian Ray Tracing (Moenne-Loccoz et al., 2024) ables secondary lighting effects via ray tracing. Integrating such techniques into MiraGe is feasible and promising for future development.
The source code for this project is available under. Our code was developed based on the GaMeS framework (Fig. 14) and is distributed under the GS Vanilla license. Compu- tational experiments in the main paper were conducted using NVIDIA GeForce RTX 4070 Laptop version and NVIDIA GeForce RTX 2080. Appendix time comparisons were re- ported using NVIDIA GeForce RTX 2080.
Building upon the GaMeS framework, we initialized the Gaussian distributions to lie perpendicular to the X plane. In our model, where all Gaussians are constrained to a 2D plane at rendering time, we consider only the rotation angle, denoted as Φ. as the primary rotation parameter. To facilitate the rendering of Gaussians positioned on the X plane, serves the primary learning parameter. The corresponding quaternions of rotation are computed as follows: for rotation about the x-axis Gx = [cos( z) sin g ), O, OJ. and for the z- axis = [cos 2 0, O, sin [].. Since no rotation occurs about the y-axis, the quaternion remains 9 = [1, O, O, 0). These quaternions are then combined through multiplication to form a new rotation matrix, ensuring precise alignment of the Gaussians on the X plane.
800
our-Sck
600
our-100c
Goussiniimage desssianSolatting
28
400
200
A.3. Supplementary Numerical Findings from the Primary Paper
0
5k
30k
15k
20%
25k
20k
Iteration
We conducted an extensive analysis of the MiraGe model due to its unique ability to control the behavior of Gaus- sians. Three distinct settings for Gaussian movement were explored:
Figure 15. Comparison of FPS obtained on a butterfly image from DIV2K datasct by MiraGe in comparison with GaussianImage (Zhang et al., 2024) and Gaussian Splatting (Kerbl et al., 2023). The experiment performed on the RTX 4070 GPU.
Amorphous the first allows Gaussians to move freely
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MiraGe: Editable 2D Images using Gaussian Splatting
Table Training time and memory usage of MiraGe across varying image resolutions. For Training we used 100K initial Gaussians;5k,
10k, 30k iterations on the V100 GPU. Experiment is provided on a butterfly from the DIV2K dataset.
Model
Our-5k iter
Our- Bur-10k iter
Our-30k iter
Gaussianlmage/defaull settings
scale
PSNR MB Train time
PSNR MB Train time
PSNR
MB
Train time
PSNR MB
Train time
1/none
30.76 3,56
209
36.17
12.2
586
47.04
35.65
2709
28.34 2.41
189
2
43.85 2.54
67
47.21 4.20
158
52.27
8.24
651
38.54 2.41
111
3
51.51 2.41
45
54.20
3.53
104
58.81
5.66
406
41.53 2.41
105
4
56.86 2.36
40
58.52 3.27
92
65.02
4.83
341
34.33 2.41
103
Table Ablation study of the effect of adding the mirror camera augmentation technique training time and the output image quality measured in widely recognized metrics: PSNR, MS-SSIM, LPSIS. The experiment was performed with an initial 100k Gaussians.
Kodak dataset
Gaussian control method
Camera Setting PSNR f MS-SSIM f LPSIS & Training Time(s) 1
One camera
51.56
0.9996
0.0050
448.73
Amorphous
Mirror cameras 59.52
0,9999
0.0005
639.66
One camera
42.49
0.9948
0.2984
398.54
Graphite
Mirror cameras 46.90
0.9983
0.1238
739.66
One camera
42.75
0.9950
0.2931
552.80
2D
Mirror cameras
48.82
0,9987
0.0071
942.78
DIV2K dataset
Gaussian control method
Camera Setting PSNR 7 MS-SSIM LPSIS | Training Time(s) !
One camera
46.00
0.9991
0.0162
690.98
Amorphous
Mirror cameras
54.54
0.9998
0.0033
946.35
One camera
40.02
0,9949
0.0312
582.50
Graphite
Mirror cameras 46.52
0.9986
0.0117
1082.41
One camera
39.99
0.9949
0.0310
869.62
2D
Mirror cameras
46.32
0.9985
0.0124
1278.33
in 3D space,
real-time.
2D: the second restricts their movement to align paral-
Tab. 2 presents an analysis of our model's scalability, We selected a butterfly image from the DIV2K dataset (Fig. 9), which provides official rescaling, to evaluate performance across different resolutions. Our model was trained on each rescaled version and compared with Gaussianlmage, only Gaussian- baseline. Even with only 5000 training iter- ations, method learns faster and achieves higher PSNR. Across all scales, our approach consistently outperforms the baseline terms of reconstruction quality.
lel to the X plane
Graphite the third confines all Gaussians to the X plane, effectively creating a 3D representation.
A qualitative analysis was performed, considering the im- pact of the mirror camera (Tab. 3), as well as the effect of varying the number initial Gaussians on the overall model behavior (Tab. 4). We also examined the impact of the camera using the Frames Per Second (FPS) metric and storage memory (Tab. 5). Given the ongoing development of various 3D Gaussian Splatting compression techniques, we employed the spz tool to effectively compress the data.
Tab. 3 shows the mirror camera view as the augmentation technique significantly improves the representation's 's fidelity of every proposed Gaussian method. This behavior be detected with the help of any of the measured metrics, i.e., PSNR, MS-SSIM and LPSIS. The drawback of improving the image quality is that a longer training time is required. The ablation study presented in Tab. 4 similarly suggests that our model scales well with the number of Gaussians used during model initialization. The striking example here is an average 62. PSNR score achieved by the Amorphous method the Kodak dataset. The price paid in time of
Due to our particular focus on animation, we analyzed FPS trends to benchmark real-time performance. Fig. 15 shows that while our model introduces a higher number of pa- rameters, leading to a decrease in FPS compared Gaus- sianImage, maintains the ability to render animations in
Phttps://gft thub. com ní minatti i clabs/sps
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MiraGe: Editable 2D Images using Gaussian Splatting
Reconstruction
Modifications
Using 3rd dimension modification
Reconstruction
Goussion
Geussian Spletting
Gaussian Splatting
Figure 17. MiraGe enables modifying 2D images, such as adjust- ing the scene's elements' sizes.
Figure 19. MiraGe simplifies intuitive editing of images, allowing transformations such adjusting the tilt of hand with minimal complexity. This is achieved by modifying the object along the third dimension.
Reconstruction
Face modifications
Integrating the representation into Blender can introduce automatic adjustments that may result in visual artifacts (Fig. 16), particularly when training on images with a white background. These modifications can lead to unrealistic renderings that are challenging to detect through automated means and currently require subjective evaluation by a hu- observer.
Figure 18. MiraGe allows produce realistic modifications of small details like changing human facial expressions.
training grows here slower, i.e., increasing the number of starting Gaussians by order of magnitude results in more extended though comparable training period length.
A.4. Extension of examples modification and Artifacts
Animating full scene be non-trivial, but it is possible. Fig. 17 demonstrates how painting can be enlarged to visualize the impact of its placement in room, offering clear view of the potential arrangement. It also possible to animate small, localized areas of the image, as demonstrated Fig. 18. For the facial animation, we utilized the Lattice modifier Blender. A simple editing concept using 3D is shown in Fig 19. Fig. 20 illustrates sculpture where the movement of the hand is achieved by adjusting the position of the shield behind the warrior. The image representation, based on parameterized Gaussians, facilitates precise edit- ing of fine details within the 3D space. MiraGe enables manual image editing and incorporates physics engine for image modifications (Fig. 21, 22, 20). It is crucial to remem- ber that if certain Gaussians are shifted without considering their dependencies others, the image will be disrupted. Therefore, the relationships between the Gaussians must be carefully modeled. We demonstrate this concept with the ex- ample children playing with blanket (Fig. 22). Despite the movement of the blanket (as seen in the supplementary video), the image remains uninterrupted and coherent.
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MiraGe: Editable 2D Images using Gaussian Splatting
Reconstruction
Phisics animation
Figure 20. MiraGe be integrated with Blender, by using flat 3D Gaussians in 3D space. The initial column presents the original image, the subsequent two columns display renders captured midway through the simulation, and the final column shows the outcome at the simulation's 's conclusion. The colored lines in the last column trace the paths of 10 randomly chosen points from the simulation.
Gaussians using GaMeS paramerization
Reconstruction/ Render to
Render after applied physic
Modification applied user
Figure 21. MiraGe allows for manual image edits and for using physies engine for real-life-like image modifications. The left image illustrates Gaussian representation achieved through a triangle mesh triangle soup, while the accompanying point-based depiction provides finer details, offering more refined visual comparison.
to
t1
Figure 22. MiraGe allows for the modification of larger scenes. We can selectively alter specific arcas and introduce smooth movements material adjustments. In this example, the bottom of the blanket is shown in motion. This, along with other modifications, available the supplementary files as videos.
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MiraGe: Editable 2D Images using Gaussian Splatting
Table Measuring the influence the initial number Gaussians on image reconstruction quality. using& mirror camera view for every table entry.
experiment was performed
Kodak dataset
Gaussian control method
Initial Gaussians PSNR f MS-SSIM LPSIS & Training Time(s) +
10k
50.66
0.9987
0.3531
584.84
Amorphous
50k
55.54
0.9997
0.0033
634.65
100k
59.52
0,9999
0.0005
639.66
150k
62.12
0,9999
0.0002
676.10
10k
40.39
0,9940
0.0599
651.32
Graphite
50k
44.90
0.9973
0.2024
732.91
100k
46.90
0.9983
0.1238
739.66
150k
48.16
0.9987
0.0105
801.18
10k
39.75
0,9886
0.0769
857.30
2D
50k
45.03
0.9955
0.2789
876.56
100k
48.82
0.9987
0.0071
942.78
150k
50.54
0.9992
0.0031
955.86
DIV2K dataset
Gaussian control method
Initial Gaussians PSNR f MS-SSIM LPSIS + Training Time(s) 4
10k
49.53
0.9987
0.0322
852. 19
Amorphous
50k
52.23
0.9995
0.0112
902.80
100k
54.54
0.9998
0.0033
946.35
150k
56.40
0.9999
0.0014
975.44
10k
40.75
0.9959
0.0457
983.41
Graphite
50k
44.67
0.9980
0.0216
1008.52
100k
46.52
0.9986
0.0117
1082.41
150k
47.61
0.9989
0.0083
1103.69
10k
38.40
0.9920
0.0616
1166.09
2D
50k
42.86
0.9967
0.0275
1256.62
100k
46.32
0,9985
0.0124
1278.33
150k
48.46
0.9990
0.0065
1415.54
Table 5. Ablation study of the effect of adding the mirror camera as augmentation technique on Kodak dataset measured using Frames Per Second (FPS) and memory storage.
Kodak dataset
Gaussian control method
Camera Setting
FPS
Memory (MB)
Compressed memory
(MB)
Gaussianlmage-700
2.41
Gaussianlmage-100)
3.44
One camera
583.28
31.25
2.42
Amorphous
Mirror cameras
620.10
117.25
7.80
One camera
1157.75
30.71
2.68
Graphite
Mirror cameras
650.75
117.91
9.22
One camera
1130.08
30.69
2.68
2D
Mirror cameras
418.39
173.64
12.82
17


Reviews:
[{"review_id": "16QCF92ty9", "reviewer": "Reviewer_bkJM", "summary": "The paper introduces MiraGe, a novel method for representing and editing 2D images using flat 3D Gaussian components. The approach leverages Gaussian splatting in 3D space to enable high-quality image reconstruction and realistic editing capabilities. MiraGe allows for both 2D and 3D manipulations of images, creating the illusion of 3D transformations while maintaining the integrity of the original 2D image. The method employs parameterized flat Gaussians and integrates with physics engines for dynamic modifications. Experimental results demonstrate state-of-the-art performance in image reconstruction quality and editing capabilities.\n\n## Update after rebuttal\n\nThanks for the author's rebuttal. It has well addressed my concerns.  I suggest accept this work.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the Reviewer for their valuable feedback and are pleased with the appreciation of our work. In particular, we are especially grateful for the recognition of the breadth and depth of our experiments \"covering multiple datasets and providing both quantitative and qualitative comparisons.\" \n\nW1. A small problem is that the author could add the PSNR/SSIM score on Fig.5.\n\nThank you for this suggestion. We will add the mentioned metrics to the Fig. 5 in the camera-ready version: \n\n| name | PSNR | SSIM |\n| --- | --- | --- |\n| GaussianImage | 43.73 | 0.9993 |\n| MiraGe | 62.00 | 0.9999 |\n\nC&S1. Does the MiraGe structure require training on a single image each time? Would this be considered low-efficiency?  \n\nThe pipeline does require training for each image individually. However, it is important to note that even with just 30 seconds of training, our method already produces better results compared to existing approaches (Fig. 9; Our-100k, using the butterfly image from DIV2K as an example). At the same time, longer training is beneficial for more-optimal PSNR performance. For more comparison, please refer to our answer to Reviewer u88z, W2 where we show that our method is surpassing baselines in PSNR with only 5k iterations, while also having a shorter training time. \n\nC&S2. What plans do the authors have for improving this aspect in the future? \n\nA possible direction to improve our approach with regards to training a different model for each individual image, could be the use of a generative model to prepare the collection of Gaussians based on the input image. While literature explores such an approach to the best of our knowledge current models work on foreground objects, and not full scenes like [1a]. In our case the images used are real life photographs, where both foreground and background are required to be reconstructed. Additionally, we would like to highlight that our model rapidly obtains high quality reconstruction. \n\n[1a] TANG, Jiaxiang, et al. DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation. In: The Twelfth International Conference on Learning Representations. \n\nQ1. If more physical factors, such as real-world lighting, BRDF, and materials, are considered, would this enhance the performance of the work? If it possible to incorporate these factors to improve MiraGe's performance? In other words, does MiraGe have the potential to expand in the direction of inverse rendering (i.e. NeRFactor, Ref-NeRF)? \n\nThank you for posting this interesting question. In literature there exist works that incorporate real-world lighting using 3D Gaussian Splatting [2a, 3a]. In contest to BRDF, 3D Gaussian Ray Tracing [4a] uses Ray Tracing to enable secondary lightning effects. Since all the mentioned methods (including ours) are based on 3D Gaussian Splatting, we believe that integrating similar ideas into MiraGe is possible. We consider this a promising direction for future work, which we will post in the main manuscript. \n\n[2a] J. Gao, et al. “Relightable 3D Gaussians: Realistic Point Cloud Relighting with BRDF Decomposition and Ray Tracing”; ECCV2024 \n\n[3a] Z. Bi, et al. GS^3: “Efficient Relighting with Triple Gaussian Splatting”; SIGGRAPH Asia 2024 \n\n[4a] N. Moenne-Loccoz, et al.; “3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes”; SIGGRAPH Asia 2024", "claims_and_evidence": "The paper makes several claims about the effectiveness of **MiraGe** in image reconstruction and editing, which are well-supported by the following evidence:\n\n- Quantitative results showing improvements in PSNR and MS-SSIM metrics compared to previous methods (Table 1).\n- Visual comparisons demonstrating better reconstruction quality and fewer artifacts than competing approaches (Figures 5, 8, 10).\n- Ablation studies highlighting the impact of different model components, such as the mirror camera setup (Table 2).\n- Demonstrations of editing capabilities, including manual modifications and physics-based animations (Figures 3, 7, 18, 19).\n\nThe evidence convincingly supports the claims made. The experiments cover multiple datasets and provide both quantitative and qualitative comparisons. The ablation studies effectively isolate the contributions of different components of the proposed framework.", "methods_and_evaluation_criteria": "The method is clear, and the evaluation criteria are well-defined. It includes many quantitative metrics, along with qualitative comparisons to other methods, making the experiments very thorough.", "theoretical_claims": "The paper does not present extensive theoretical proofs but rather focuses on conceptual framework and experimental validation.", "experimental_designs_or_analyses": "The experimental setup is comprehensive. \n\nAnd also, the authors compare their method with the generative approach Drag-GAN. Despite Drag-GAN being a generative model with extensive prior knowledge, this work still achieves comparable performance, which is very promising.", "relation_to_broader_scientific_literature": "This work demonstrates many applications. Compared to previous work, it offers better image representation and manipulation capabilities than Gaussian-Image (ECCV 2024). Unlike Drag-GAN (SIGGRAPH 2023), it doesn't require as much prior knowledge. \n\nAdditionally, it can integrate two images (Fig. 4), suggesting that it may also have potential for image harmonization, though real-world lighting conditions might add complexity.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "A small problem is that the author could add the PSNR/SSIM score on Fig.5, it would be more clear to see the advantage.\n\nOverall I believe this is a great work.", "other_comments_or_suggestions": "Does the MiraGe structure require training on a single image each time? Would this be considered low-efficiency? What plans do the authors have for improving this aspect in the future?", "questions_for_authors": "If more physical factors, such as real-world lighting, BRDF, and materials, are considered, would this enhance the performance of the work? If it possible to incorporate these factors to improve MiraGe's performance? In other words, does MiraGe have the potential to expand in the direction of inverse rendering (i.e. NeRFactor, Ref-NeRF)?", "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": "Az9GLQTolX", "reviewer": "Reviewer_u88z", "summary": "The paper proposes MiraGe, a novel approach for representing and editing 2D images using Gaussian Splatting. MiraGe uses flat-controlled Gaussian components positioned in 3D space, providing intuitive editing capabilities with a 3D perception. Key contributions include high-quality reconstruction results that outperform state-of-the-art methods such as GaussianImage, SIREN, and WIRE, and the integration with physics engines to enable physically plausible manipulations and realistic animations of 2D images. The method achieves notable improvements in reconstruction metrics (PSNR, MS-SSIM) compared to existing INR and 3D reconstruction approaches.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the Reviewer for the feedback and constructive remarks regarding our paper that we believe will improve our paper. In particular, we are grateful for the Reviewer’s recognition that \"the methods and evaluation criteria employed are appropriate and well-chosen\". \n\nW1\n\nWe acknowledge that significant edits can produce visual artifacts in the current version of our model and discuss it in in the Limitation Section. Nevertheless, our primary focus is on demonstrating the advantages of the proposed representation of 2D images using parametrized Gaussians and proving that such editing is feasible. We believe this work opens the door to numerous future research directions and improving even substantial edits. \n\nQ4\n\nIt is a non-trivial question requiring substantial consideration. \n\nAs the field of editing 3D Gaussians continues to develop, so do the methods for mitigating artifacts appearing in 3D objects. For example, a recent study (arXiv 03.2025) [1b] introduces a loss function that enforces Gaussians to maintain spherical shapes. In PhysGaussian the authors use Anisotropy Regularizer [2b]. In both cases this helps prevent the creation of \"sharp\" visible artifacts during rotations. \n\nIn the context of 2D image “we introduced the mirror camera to ensure that Gaussians remain confined within a specific spatial region between the cameras, enhancing control and precision.” This approach effectively serves as a form of Gaussian regularization, as seen in Fig. 6 (first and second rows). To enhance readability, we will enlarge this figure. \n\nIt is also important to highlight that artifacts may arise naturally when working with 2D representations, such as fading effects that occur with significant changes in perspective (e.g., rotations of 90 degrees in 3rd dimension). A useful analogy is a piece of paper or a photograph: when rotated 90 degrees in the third dimension, it effectively disappears from view.\n\n[1b] L Qiu, et al. ; “LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds”; arXiv  2025 \n\n[2b] T. Xie, et al.; “PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics”;  CVPR 2024 \n\n W2\n\nBelow we present the supplementation of Tab. 1 and a comparison with existing methods. The metrics for the baselines were taken from [3b]; however, we re-ran the experiments for GaussianImage (GI) -70K (GI-70K) and our using a V100 GPU to ensure a more reliable comparison. Those experiments are denoted in the following table by “*” next to the method name. \n\n|  | Kodak |  | Div2K |  |\n| --- | --- | --- | --- | --- |\n|  | PSNR | Train Time(s) | PSNR | Train Time(s) |\n| WIRE | 41.47 | 14339 | 35.64 | 25684 |\n| SIREN | 40.83 | 6582 | 39.08 | 15125 |\n| I-NGP | 43.88 | 491 | 37.06 | 676 |\n| NeuRBF | 43.78 | 992 | 38.60 | 1715 |\n| 3DGS | 43.69 | 340 | 39.36 | 481 |\n| GI-70K | 44.08 | 107 | 39.53 | 121 |\n| GI-70K* | 44.12 | 116 | 39.53 | 112 |\n| GI-100K* | 38.93 | 126 | 41.48 | 120 |\n| Our-70K; 5k iter | 49.07 |**57**| 44.37 |**75**|\n| Our-100K; 5k iter |  51.04 | 59 | 46.23 |79|\n| Our-70K; 30k iter | 57.41 | 547 | 53.22 | 789 |\n| Our-100K; 30k iter |**59.52**| 560 |**54.54**| 946 |\n\nWe include the table with training time comparison including multiple baseline methods. The time provided for our method is the full training time (30000  and 5000 iteration steps).  Additionally, using the butterfly image from the DIV2K dataset as an example, we demonstrated that our method (Our-100K) achieves higher PSNR than GI in just 30 seconds, see Fig. 3.  This is also supported by our method surpassing GI in PSNR with only 5k iterations, while also having a shorter training time. \n\nQ1\n\nWhile the inference of our method is fast, the storage cost introduced by the original 3DGS representation used by our method is high. To overcome this we  use the existing compression tool spz [3b]. This allows us to reduce the memory overhead by up to 95%; please see Tab. 4 in supplementary material. Additionally we include GI as our baseline. Note that GI compresses the Gaussian representation as part of their pipeline, and spz compression algorithm is not applicable to their representation: \n\n|  | Camera setting | PSNR | Memory(MB) | Compressed memory (MB) |\n| --- | --- | --- | --- | --- |\n| GI (70k) * | - | 44.12 | - | 2.41 |\n| GI (100k)* | - | 38.93 | -| 3.44 |\n| Our (100k) | A/ One camera | 51.56 | 31.25 | 2.42 |\n| Our (100k) | A/ Mirror camera | 59.52 | 117.25 | 7.80 |\n \n[3b] https://github.com/nianticlabs/spz\n\nQ2\n\nDue to the character limitation, answer to this question  is referenced in Q3, Reviewer VWK2\n\nQ3\n\nIndeed, in complex scenes with intricate backgrounds, the task becomes more challenging, as we mention in the Limitations Section. However, as shown in Fig. 11, our method allows for seamless object manipulation, such as repositioning a flower using a physic engine such as Taichi Elements, while effectively applying inpainting to ensure a gap-free result.", "claims_and_evidence": "The claims of improved reconstruction quality, realistic 3D manipulation, and physics-based editing are generally well-supported by experiments on standard benchmarks (Kodak and DIV2K datasets).", "methods_and_evaluation_criteria": "The methods and evaluation criteria employed are appropriate and well-chosen. The paper uses widely recognized benchmarks (Kodak and DIV2K) and metrics (PSNR, MS-SSIM) suitable for evaluating the proposed method against competitors. Using these datasets facilitates direct comparison with previous work.", "theoretical_claims": "The paper does not include explicit theoretical proofs or theoretical claims requiring validation. It mainly presents empirical evidence and algorithmic contributions.", "experimental_designs_or_analyses": "The experimental design and analyses are sound. The evaluations include comparative studies against relevant baseline methods (GaussianImage, NeuRBF, I-NGP, WIRE, SIREN). However, deeper investigation into computational complexity, particularly the trade-offs in memory and computational efficiency compared to baseline methods, could strengthen the paper.", "relation_to_broader_scientific_literature": "The paper positions itself well within existing literature by clearly explaining how it advances beyond GaussianImage's limitations of static representations. MiraGe leverages explicit Gaussian representations and integrates ideas inspired by recent developments in INRs and Gaussian Splatting's effectiveness in 3D representation. Thus, MiraGe contributes to both theoretical advancements in implicit neural representations and practical improvements in editable image representations.", "essential_references_not_discussed": "The literature review is thorough, and essential references such as WIRE, NeuRBF, SIREN, GaussianImage, and I-NGP are well-discussed. However, 4D Gaussian Splatting (CVPR 2024), extending Gaussian Splatting to dynamic scenes, were not discussed and would further enrich the paper's context and comparison.", "other_strengths_and_weaknesses": "### Strengths:\n\n- Clearly presents a novel and practical combination of Gaussian Splatting and 3D-like image editing.\n- Demonstrates significant quantitative and qualitative improvements over state-of-the-art baselines.\n- Integrates successfully with physics-based animation tools, extending applicability to various real-world use cases.\n\n### Weaknesses:\n\n- Significant edits produce visual artifacts, indicating potential limitations in practical usability without further refinement.\n- The training time appears to be lengthy. While the paper includes a comparison between GaussianImage and 3DGS, incorporating comparisons with additional baselines would further highlight the strengths of this method.", "other_comments_or_suggestions": "The paper is well-written and organized clearly.", "questions_for_authors": "1. Could you discuss the computational overhead introduced by your approach compared to other baselines? Clarifying runtime performance or potential computational bottlenecks would influence the practical significance of the proposed method.\n2. How does your method scale with image size or complexity (e.g., high-resolution images or more intricate scenes)? Providing additional insights or experiments on scalability would significantly improve the paper.\n3. How well does your approach handle editing scenes with complex backgrounds? The examples in the supplementary materials appear somewhat simplistic—can you demonstrate editing effectiveness on images with more intricate backgrounds?\n4. Can the artifacts arising from significant edits be mitigated systematically, or is the approach fundamentally limited by the Gaussian parametrization? A clear answer could help better understand the approach's limitations.", "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": "0RdHchQ8ju", "reviewer": "Reviewer_VWK2", "summary": "The paper introduces ​MiraGe, a method for representing and editing 2D images using parameterized 3D Gaussian components. By embedding 2D images in 3D space with flat Gaussians and leveraging mirror cameras for training, MiraGe achieves high-fidelity reconstruction and enables intuitive 3D-like editing (e.g., bending, physics-based animations). The method integrates with physics engines (e.g., Blender, Taichi) for realistic deformations and outperforms existing INR and Gaussian-based models (e.g., GaussianImage, SIREN) in reconstruction quality on Kodak and DIV2K datasets.\n\n## Update after rebuttal\nI thank the authors for their response. I have no further concerns and will maintain my positive score.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the Reviewer's thoughtful feedback. We are pleased for the recognition of the distinct advantage of our proposed method in seamlessly integrating with physics engines, allowing for more dynamic and realistic modifications compared to traditional tools.\n\nW1/2\n\nIn our paper, we address two tasks. The first, reconstruction, is evaluated using widely recognized benchmarks and metrics, as noted by Reviewer u88z, who found the methods and evaluation criteria appropriate.\n\nIn the second editing task, we focused on qualitative assessment, acknowledging the limitations of our evaluation as the first to apply parametric 3D Gaussians to 2D images. Nevertheless, we compare our approach to generative methods to highlight differences in editing capabilities, aiming to introduce a novel 2D representation and inspire future advancements.\n\nW3\n\nIn most generative methods, users have constrained control over edits. However, as shown in Fig. 10, our approach allows precise modifications, enabling users to adjust specific details. Additionally, we incorporate 3D representation and human perspective (Fig. 3), demonstrating that our method inherently possesses a 3D structure. This means that, beyond standard 2D transformations like rotation, we also have control over perspective.\n\nIn PhysGen authors claim that “despite advancements in video generative models the incorporation of real-world physics principles into the video generation process remains largely unexplored and unsolved.” We show that our method can leverage physics engines, producing videos from 2D images without the need for generative model predicting the simulation (see visualization in supplementary materials). We show that using Taichi to apply materials such as sand to objects, as demonstrated in Fig.7.\n \nQ1\n\nOur goal is not to compete with tools like Photoshop but to highlight the advantages of representing 2D images with parameterized 3D Gaussians. While Photoshop allows for seamless image modifications, it relies on numerous hidden features such as specialized interpolation, imputation, and other corrections. When applying a simple affine transformation to an image, artifacts often emerge, necessitating pixel interpolation to correct them. In contrast, Gaussian components inherently preserve their structure under affine transformations, as an affine transformation of a Gaussian remains a Gaussian. For this reason, we believe directly comparing MiraGe to the Photoshop tool would be inappropriate.\n\nHowever similar to Photoshop layering, our image representation can incorporate the idea of image layers (as illustrated in Fig. 4). This allows for inpainting when parts of the image are occluded—for example, seamlessly inpainting in the background, as demonstrated in Fig. 11. \n\nIt is worth mentioning that our model builds on features from 3DGS, enabling the application of existing Gaussian-based models. We believe that in practice, MiraGe can incorporate various style transfer methods for Gaussians. For instance, StyleGaussian [1c] allows for color adjustments during style transfer. However, such modifications fall outside the scope of this paper, as our focus is on shape editing rather than texture or color alterations.  We recognize this as a promising future research direction. \n\n[1c] K. Liu, et al. \"StyleGaussian: Instant 3d style transfer with gaussian splatting.\" SIGGRAPH Asia 2024\n\nQ2\n\nDue to the character limitation, answer to this question is referenced in W1 and Q4, Reviewer u88z\n\nQ3\n\nWe consider this an interesting question, as also noted by Reviewer u88z. We will address this in the camera-ready version.\n\nAs part of our rebuttal, we conducted an experiment on a single image to illustrate how method scale. We selected a butterfly from the DIV2K dataset[2c]. For Training we used 100K initial Gaussians;5k, 10k, 30k iterations on the V100 GPU. In table we report compressed memory (MB) and training time (seconds).\n\nFrom the table below, we can observe that our method consistently achieves higher PSNR results compared to the baseline.\n\n[2c] https://data.vision.ee.ethz.ch/cvl/DIV2K/\n\n|Model||Our-5k iter|||Our-10k iter|||Our-30k iter|||GaussianImage|default settings|\n|---|---|---|---|---|---|---|:---|---|---|:---|---|---|\n||PSNR|MB|Train time|⎮PSNR|MB|Train time|⎮PSNR|MB|Train time|⎮PSNR|MB|Train time|\n|1/none|30.76|3.56|209|⎮36.17|12.2|586|⎮47.04|35.65|2709|⎮28.34|2.41|189|\n|2|43.85|2.54|67|⎮47.21|4.20|158|⎮52.27|8.24| 651|⎮38.54|2.41|111|\n|3|51.51|2.41|45|⎮54.20|3.53|104|⎮58.81|5.66|406|⎮41.53|2.41|105|\n|4|56.86|2.36|40|⎮58.52|3.27|92|⎮65.02|4.83|341|⎮34.33|2.41|103|\n\nIn practice, for high-resolution images, GS can be initially trained at a lower resolution and progressively refined to higher resolutions, following the Hierarchical GS strategy [3c], which we believe can be applied as a future work.\n\n[3c] B. Kerbl, et al. \"A hierarchical 3d gaussian representation for real-time rendering of very large datasets.\" ACM Transactions on Graphics, 2024", "claims_and_evidence": "The authors claimed 1) state-of-the-art reconstruction quality, which is supported by the PSNR/MS-SSIM scores reported in Table 1; 2) 3D-like editing of 2D images, which is supported by Figures 3, 6, and 8 show manual edits (e.g., facial expressions, object bending) and physics-driven animations in Fig. 7.", "methods_and_evaluation_criteria": "​Methods:\nThe use of flat 3D Gaussians with GaMeS parametrization (from prior work) is innovative for bridging 2D/3D editing. Mirror cameras enhance spatial consistency, and three Gaussian control methods (Amorphous, 2D, Graphite) offer flexibility. However, physics integration is treated as a \"black box,\" with no details on coupling Gaussians with engine dynamics.\n\n​Evaluation:\nStandard datasets (Kodak, DIV2K) and metrics (PSNR, MS-SSIM) are appropriate for reconstruction. However, editing capabilities are assessed only qualitatively. This paper only chose DragGAN and PhysGen as editing capabilities baselines and no non-generative baselines are compared in this paper.", "theoretical_claims": "No theoretical proofs are provided for novel claims, but the method’s foundation in established techniques (3DGS, GaMeS) is reasonable.", "experimental_designs_or_analyses": "Manual edits (Fig. 8) and physics animations (Fig. 7) are visually compelling but lack quantitative benchmarks. Many of the editing effects demonstrated in the paper and supplementary material appear to involve simple warping, which could also be achieved manually using traditional image editing tools like Photoshop. However, the proposed method offers a distinct advantage in its ability to integrate seamlessly with physics engines, enabling more dynamic and realistic modifications.", "relation_to_broader_scientific_literature": "The proposed method combines the ​efficiency of Gaussian-based representations with editability, and integrates ​physics engines for realistic 2D image editing, expanding the application of physics-based simulations to traditionally 2D domains.", "essential_references_not_discussed": "The references are sufficient.", "other_strengths_and_weaknesses": "​Strengths:\n1. Novel integration of 3D Gaussians for 2D image editing.\n2. High reconstruction quality and support for intuitive 3D manipulations.\n3. Demonstrated compatibility with physics engines.\n\n​Weaknesses:\n1. Limited quantitative evaluation of editing/physics realism.\n2. Limited baseline methods for comparisons.\n3. Most of the editing effects are relatively simple and subtle due to the method's non-generative nature, which limits its versatility and practical applicability.", "other_comments_or_suggestions": "No other comments.", "questions_for_authors": "1. How does MiraGe handle ​complex edits (e.g., occlusions, texture changes) compared to traditional tools like Photoshop?\n2. How does the model handle ​edge cases where significant modifications introduce visual inconsistencies?\n3. How does MiraGe scale to ​high-resolution images in terms of training time and memory usage?", "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": "hRiz4hpTWz", "reviewer": "Reviewer_vmYq", "summary": "This paper presents approaches to editing 2D images represented by Gaussian Splatting. The authors propose to use 3D flat Gaussians optimized with mirrored cameras from two opposite sides to represent 2D images, with quality better than other prior works. With the GS-represented images, this paper demonstrates different ways to do image editing with the assistance of various geometry processing tools.\n\n## update after rebuttal\n\nI appreciate the efforts made by the authors during the rebuttal. The authors’ responses addressed some of my concerns. The proposed method to fit Gaussians for representing and editing 2D images is somewhat effective. Regarding the editing capability, editing the image by manipulating augmented Gaussians is interesting, but I don’t think that manipulating Gaussians can make image editing simpler or more efficient. The authors should clearly state such limitations or tradeoffs in the paper. I will keep my borderline rating (\"weak reject\") on this submission, however, I won't have objections if other reviewers agree to accept this work.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the Reviewer’s feedback. We also thank Reviewer for appreciating the concept and work: “utilizing two opposite cameras to fit 3D Gaussians to represent 2D images is an interesting idea, and evaluation also shows the effectiveness of this idea”.  Since the section for questions directed to us is empty, we have opted to refer to other sketches.  We have made every effort to resolve any ambiguities. We hope the additional clarifications provide further clarity. We remain open and keen to provide further explanations if needed.  \n\nEDOA: The comparisons with DragGAN and PhysGen are not sound to me... \n\nWe would like to mention that representing a 2D image using Gaussian splatting for editing is a new concept, and it is difficult to compare it with existing tools. Therefore, we apply comparisons with existing solutions such as DragGAN and PhysGen, which shows the potential of our approach. \n\nIn the case of PhysGen, we chose these models to highlight their differences. An interesting case is PhysGen's input control, which offers limited editing capabilities. In PhysGen, the authors utilized a physics engine for training, with a diffusion model serving as an intermediary. However, this approach restricts user control. In contrast, we demonstrate that MiraGe allows direct use of a physics engine for Gaussian representation, eliminating the need for a complex diffusion model conditioned on the physics engine. Additionally, our representation is compatible with multiple engines and tools, including Taichi Elements and Blender. \nIn summary our representation enables editing directly in 3D, making a comparison with simple pixel modifications insufficient. Ultimately, we use this comparison to highlight the strengths of our model and its potential applications. \n\nC&E/ W\n\nIn 2D-MiraGe primitives are constrained to a 2D plane, meaning edits should only be made on a plane parallel to the selected one. Otherwise, during rotation, they will fade away, - much like a thin piece of paper viewed from the edge in real life. This is why 2D-MiraGe was designed for compatibility with 2D engines, similar to Graphine-MiraGe, which excels in layered editing scenarios. \n\nAmorphous-MiraGe, on the other hand, is not strictly confined to a single plane, giving it a natural 3D effect. From a practical standpoint, this makes it the most intuitive to edit in 3D space. If only 2D edits, such as simple rotations in one plane, were applied, there would be no noticeable difference between the model versions. This distinction is illustrated in Fig. 6. However, we acknowledge that the image is too small, and we will enlarge it to improve clarity. We thank Reviewer for pointing this out. \n\nWe hope our explanation is sufficient, and we remain open to more questions.\n\nW:  (...) editing those Gaussians is quite trivial (..)\n\nAs 3D Gaussian editing still advances, so do methods for reducing artifacts. A recent study (arXiv 03.2025) [1b] enforces spherical shapes, while PhysGaussian [2b] uses an Anisotropy Regularizer—both preventing sharp artifacts during rotations.\nIn 2D achieving high-quality and robust nonlinear modifications images is still challenging, especially when performing affine transformations on low-resolution images, where inaccuracies can lead to the appearance of holes. However, with Gaussians, affine transformations preserve key properties such as color and opacity, allowing us to avoid numerical errors and eliminate this issue.  Moreover, the additional 3rd dimension can simplify animation creation in certain cases. For example, it facilitates the natural motion of a waving flag as illustrated in Fig. 3. We hope that the use of Gaussians for 2D image representation will continue to evolve, leading to more advanced and mature editing tools in the future. Our work demonstrates that such editing is not only possible but also promising.  \n\nOC&S: (...) I am concerned whether ICML is a good fit for this work... \n\nIn our paper, we introduce a novel approach to 2D image representation, specifically designed for manual editing and integration with physical engines. As the reviewers pointed out, our method lacks direct competitors - a fact that, in our view, underscores its originality. \n\nIn the reconstruction task, our method achieves superior results in a shorter time. As discussed in our response to Reviewer u88z, W2, our approach outperforms the baselines in PSNR with just 5k iterations  Moreover, MiraGe enables a range of capabilities: \n- Integrating physical engines with 2D images \n- Editing 2D images directly within a 3D space \n- Enabling complex nonlinear modifications of 2D images \n\nTherefore, we believe ICML is a suitable venue for this work, as we present a new approach to representing 2D images. \n\nWe are committed to improving our paper and greatly appreciate the feedback regarding the visibility of Fig. 6. We will address this concern and make the necessary adjustments to the camera-ready version.", "claims_and_evidence": "In the following 3 questions, I assume the method and evaluation in this paper refer to the proposed approach to representing 2D images with 3D Gaussians.\n\nThe claims about their new approach to better fitting 3D Gaussians to represent 2D images are somewhat supported by their evaluations. However, the authors fail to give a clear reason why amorphous-MiraGe gives better editing quality, and how it resolves the artifacts shown in other variants.", "methods_and_evaluation_criteria": "The evaluation makes sense.", "theoretical_claims": "There’s no proof for theoretical claims.", "experimental_designs_or_analyses": "The comparisons with DragGAN (Fig. 8 & 10) and PhysGen (Fig. 12) are not sound to me. Although the proposed method may give better visual quality from those figures, the manual efforts to achieve MiraGe’s results are way more than DragGAN and PhysGen. This comparison is like classical PhotoShop vs. Generative image editing, which does not mean anything.", "relation_to_broader_scientific_literature": "This work is somewhat related to implicit neural representation (just a bit “neural” and “implicit”) and 2D/3D geometry processing.", "essential_references_not_discussed": "References are sufficient.I reviewed all the parts.", "other_strengths_and_weaknesses": "Strengths:\n* Utilizing two opposite cameras to fit 3D Gaussians to represent 2D images is an interesting idea, and evaluation also shows the effectiveness of this idea.\n* I appreciate the authors’ manual efforts in making all these image edits/animations.\n\nWeaknesses:\n* Once an image is represented as Gaussians, editing those Gaussians is quite trivial, since Gaussians here are just discrete 3D points that can be freely moved. The real difficulty is how to easily edit the 2D image with 3D Gaussians. When I read the title of this paper, I was expecting to see some novel ways to ease the Gaussian editing for 2D images. However, the editing part still relies on existing tools, which usually require a lot of manual effort to bind the Gaussian points and to precisely define deformation or motions. Moreover, regular images represented by pixels can also do these 2D editings with more mature tools/pipelines. Finally, I acknowledge the proposed improvement from amorphous-MiraGe with artifacts after editing compared to other variants. However, this comparison is only shown in a small figure (Fig. 6), which makes it hard to extensively evaluate the proposed improvements.", "other_comments_or_suggestions": "This paper is fundamentally an image processing paper. There are a few machine learning contents inside (e.g., new ways to fit 3DGS for 2D images) but not too many. I am concerned whether ICML is a good fit for this work, as opposed to other computer vision/graphics or image processing conferences.", "questions_for_authors": "None", "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:

Meta-review: There were three acceptance and one rejection recommendations.
After the rebuttal, the rejecting reviewer maintained their recommendation but also agreed to accept the paper for publication.
After careful consideration of the reviews, the paper was accepted. However, the authors are encouraged to address the limitations or trade-offs pointed out by the reviewer.
Decision: Accept (poster)