Generative Blocks World: Moving Things Around in Pictures
Authors: Vaibhav Vavilala
Seemandhar Jain
Rahul Vasanth
David Forsyth
Anand Bhattad
Venue: ICLR 2026 Poster (2026)
Keywords: 3D primitives
Diffusion Models
Primary area: generative models
TL;DR: We can use 3D primitives to geometrically control diffusion models

Abstract: We describe Generative Blocks World to interact with the scene of a generated image by manipulating simple geometric abstractions. Our method represents scenes as assemblies of convex 3D primitives, and the same scene can be represented by different numbers of primitives, allowing an editor to move either whole structures or small details. Once the scene geometry has been edited, the image is generated by a flow-based method, which is conditioned on depth and a texture hint. Our texture hint takes into account the modified 3D primitives, exceeding the texture-consistency provided by existing techniques. These texture hints (a) allow accurate object and camera moves and (b) preserve the identity of objects. Our experiments demonstrate that our approach outperforms prior works in visual fidelity, editability, and compositional generalization.

Published as conference paper ICLR 2026
GENERATIVE BLOCKS WORLD: MOVING THINGS AROUND IN PICTURES
Vaibhav Vavilala*
Seemandhar Jain'
Rahul Vasanth
D.A. Forsyth'
Anand Bhattad²
University of Illinois Urbana-Champaign
Johns Hopkins University
Source Image
Move cat left
Move right
Camera
Scale cat's head
Figure 1: Generative Blocks World. Given an input image (bottom left), we extract set of 3D convex primitives (top left) that provide editable and controllable representation of the scene. These primitives are used to generate new images that respect geometry, texture, and the text prompt. The first column shows the original input and its primitive decomposition. Subsequent columns show sequential edits: translating the cat to the left (second column), translating it to the right (third column), moving the yarn in front of the cat and shifting the camera toward the scene center (fourth column), and scaling up the cat's head (burgundy primitive; fifth column). Our method enables 3D-aware semantic image editing through intuitive manipulation of these learned primitives.
ABSTRACT
We describe Generative Blocks World interact with the scene of generated image by manipulating simple geometric abstractions. Our method represents scenes as assemblies of convex 3D primitives, and the same scene can be represented by different numbers of primitives, allowing an editor to move either whole structures small details. Once the scene geometry has been edited, the image generated by flow-based method, which is conditioned depth and texture hint. Our texture hint takes into account the modified 3D primitives, exceeding the texture-consistency provided by existing techniques. These texture hints (a) allow accurate object and camera moves and (b) preserve the identity of objects. Our experiments demonstrate that our approach outperforms prior works in visual fidelity, editability, and compositional generalization.

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1 INTRODUCTION
There rich literature treating editing real and generated images using various image-centered interfaces like dragging features. Interaction paradigms that exploit explicit representations of 3D are much less com- mon. This paper describes an image editor built on a full 3D interaction paradigm, using a representation that is both compact and accurate generative blocks world.
Any scene representation that supports a camera move has some form of 3D representation. An explicit 3D representation helps, Fig D Explicit 3D representations have other important advantages. First, they offer shape constancy. When an object is moved across a perspective view, it is seen from a new aspect because the location of the focal point moves in object coordinates. This means that (a) the shape of the object may change, with a change that depends on the field of view of the camera and the shape of the object and (b) some surface markings will become visible invisible (e.g. bar code on soda can, Fig B When an object is moved toward or away from the camera, its image should expand or shrink (e.g. cat, Fig D. If an editor does not preserve these properties correctly, the viewer may conclude that the shape or size the object has changed. A properly constructed 3D representation will prevent this. Second, they offer contact consistency. A user who moves (say) a tin on a table generally expects the tin to remain in contact with the table. An explicit 3D representation allows the user to manage whether it does or not (e.g. dog in Fig 7 soda can, Fig 3 Third, they offer shape completion. Objects have backs that are not visible, but may have an effect when another object is moved in scene. An explicit 3D representation can capture this effeet.
It has been hard to build a 3D representation that: (a) represents the scene accurately enough that edited images are realistic and (b) is compact enough to support interactions. This paper uses modern fitting methods to represent scenes as small assemblies of meaningful parts or primitives (cf. Blocks WorldRRober (1963) or geons Biederman (1987). We call our method Generative Blocks World, though our learned primitives are richer than cuboids. Our method yields assemblies by decomposing an input image into a sparse set of convex polytopes (Vavilala et al. 2025a) that approximate the scene's depth map well enough to enable view-consistent texture projection. Further, our primitives respect object boundaries rather well. A user can reach into the scene and move a primitive, with predictable results. Our simple scene representations yield hints as to the appearance of the final image. These hints, together with the primitive depth map, are inputs to an off- the-shelf image generator, which renders accurate images.
Primitive decompositions have very attractive properties. They are selectable: individual primitives can be intuitively selected and manipulated (Fig. []. They are object-linked: segmentation by primitives is close to a segmentation by objects, meaning an editor is often able to move an object or part by moving primitive (Figs
4 They are accurate: the depth map from a properly constructed primitive representation can be very close to the original depth map (Section 3.T. which means primitives can be used to build texture hints (Section
L
3.2) that support accurate camera moves (Figs 2 5 They have variable scale: one can represent
the same scene with different numbers of primitives, allowing multi-scale edits (Figs
B
Contributions: (1) We describe a pipeline that fuses convex primitive abstractions with a flow- bosed gener- ator to yield natural 3D interaction paradigm for image editing. Our pipeline uses texture-hii hint procedure that supports camera moves and edits at the object-level, while preserving identity. (2) We provide extensive evaluation demonstrating superior geometric control, texture retention, and edit flexibility relative recent state-of-the-ard baselines.
2 RELATED WORK
Primitive Decomposition: Early vision and graphics pursued parsimonious part-based descriptions, from Roberts' Blocks World RRbertt (963)) and Binford's generalized cylinders [Bintord (197I) to Biederman's ss geons Biederman (198)). ). Efforts to apply similar reasoning to real-world imagery have been periodically
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revisited Gupta et al.
2010 Monnier et al./ (2023) Bhattad et al. (2025) from various contexts and applica-
tions. Modern neural models revive this idea: ESP--Net Chen et al. (2020). CSG-Net Sharrma et al. (2018),
and CVXNet Deng et al. (020)) represent shapes as unions of convex polytopes, while Neural Parts Tulsiani
et al. (2017) SPD Zou et al. (2018), and subsequent works Liu et al. (2022) learn adaptive primitive sets.
Recent systems extend from objects to scenes: Convex Decomposition of Indoor Scenes (CDIS) Vavilala
& Forsyth (2023) and its ensembling/Boolean refinement Vavilala vafinaa et al. (2025a) fit CVXNet-like polytopes
to RGB-D images, using a hybrid strategy. CubeDiff Kalischek et al. (2025) fits panoramas inside cuboids. Our work leverages CDIS as the backbone, but (i) improves robustness to in- the-wild depth/pose noise and (ii) couples the primitives to a Rectified Flow (RF) renderer, enabling controllable synthesis.
Conditioned Image Synthesis: Layout-to to-image translation was pioneered in GANs Isola et al. (2017): Zhu
al.
(017)) Park et al. (209)) and is now dominated by diffusion models such as Stable Diffusion Rombach
et al.
2022 ControlNet Zhang al. (2023), and T21-Adapter Mou et al. (2024). These models can com-
pose multiple spatial controls (Vavilala et al. 2024), perform color edits ( (Vavilala et al. 2025b) and relight
scenes Xing et al. (2025). We utilize pretrained depth-conditional FLUX model, conditioning on depth
maps derived from our 3D primitives.
Point-Based Interactive Manipulation: Methods like DragGAN (aan et al. 2023 and its diffusion-based
successors (Shi et al. 2024 Mou et al., 2023 Cui et al. 2024 Pandey et al.) 2024) offer intuitive 2D control by dragging handle points. Some approaches extend this to 3D using NeRFs for multi-vier con- sistency Guang et al. (205)) or leverage self-guidance for layout control (Epstein et al. 2023). Our work
differs by operating on editable 3D primitives instead of 2D points. This enables multi-resolution control and camera movement while handling perspective, occlusion, and texture.
Object-Levvl and Scene-Level Editing: Many recent works embed 3D priors for editing, though often fo-
cusing on single objects Gu et al. 2022); [Wang et al. (2023); ;; Poole et al. (2023); ; Tang et al. (023)) Cheng
ct al. (2055) or using language to guide transformations Michel et al. (202)). Our Generative Blocks World
generalizes to complex edits not easily described by text. Another paradigm, seen in Image Sculpting yen- phraphai et al. (2024) and OMG3D Zhao et al. (225)). reconstructs an explicit 3D mesh for manipulation before re-rendering. While precise, these multi stage pipelines be bottlenecked by reconstruction qual- ity. Our method provides a more streamlined approach by operating on abstract primitives, achieving strong geometric control without the complexity of direct mesh manipulation.
Primitive-Basee Based Scene Authoring: LooseControl (Bhat et al. 2024) enables control via box-like primitives by fine-tuning a diffusion model with LoRA weights. This training is necessary to bridge the domain gap between its coarse primitive-based depth and standard depth maps (Yang et al. 2024h. In contrast, our un- derlying primitive representation is accurate enough to require no fine-tuning. Furthermore, by abstracting objects into single, monolithic boxes, LooseControl is limited to holistic transformations and cannot perform part-level edits. Our method uses structured geometry, decomposing objects into multiple convex polytopes at variable levels of detail for more granular control. A more recent work, Build-A-Scan (Fldesokey & Wonka 2025), uses similar pipeline to LooseControl and thus inherits its limitations. Our approach dif- fers by: (i) decomposing objects into multiple convex polytopes for finer control, (ii) supporting camera movement, and (iii) allowing novel scenes to be authored from scratch via primitive assembly.
3 METHOD
Generative Blocks World generates realistic images conditioned on a parsimonious and editable geometric representation of scene: set of convex primitives. The process consists of four main stages (Fig- - (i) primitive extraction from any image via convex decomposition (Sec. I. (ii) generating an image condi- tioned on the primitives (and text prompt), (iii) user edits the primitives and/or camera, and (iv) generates
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Primitives
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Extract primitives
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Inference Pipeline
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Figure 2: Pipeline Overview. Top left: We use convex decomposition models Vavilala et al. 2025a to extract primitives from input image multiple scales. Bottom: Users manipulate these primitives and the camera to define new scene layout. We render the modified primitives into a depth map and generate a texture hint image. These serve as inputs which requires no fine-tuning (Top right). The generated image respects the modified geometry, preserves texture where possible, and remains aligned with the text prompt.
pretrained depth-to-image model Labs (204).
new image conditioned on the updated primitives, while preserving texture from the source image (Sec. 3.3 We describe each component in detail below.
3.1 CONVEX DECOMPOSITION FOR PRIMITIVE EXTRACTION
Our primitive vocabulary is blended 3D convex polytopes described in Deng al. 2020). CVXnet represents the union of convex polytopes using indicator functions O(x) + [0, that identify whether a query point E I is inside outside the shape. Each convex polytope is defined by collection of half-planes.
A half- aalf-plane (x) n4 x d, provides the signed distance from point to the h-th plane, where ny is the normal vector and d, is the offset parameter.
While the signed distance function (SDF) of any convex object be computed the maximum of the SDFs its constituent planes, CVXnet uses a differentiable approximation. To facilitate gradient learning. instead of the hard maximum, the smooth LogSumExp function employed to define the approximate SDF, ((x):
x =
LogSumExpf (HHx(z))
The signed distance function is then converted to an indicator function Sigmoid(-o((z))..
3 - [0, using: C(r) =
The collection of hyperplane parameters for primitive is denoted as = ((n,, dz)). and the overall set of parameters for convex B [h, a). While is treated hyperparameter, the remaining parameters are learnable. The parameter controls the smoothness of the generated convex polytope, while o controls the sharpness of the indicator function transition. The soft classification boundary created by the sigmoid
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function facilitates training through differentiable optimization. For our primitive model we use ResNet- 18 Encoder E, followed by 3 fully-connected layers that decode into the parameters of the primitives Do- While the model lightweight, the SOTA of primitive prediction requires different trained model for each primitive count K.
Recent work has adapted primitive decomposition to real-world scenes (as opposed to well-defined, isolated objects, such as those in ShapeNet Vavilala & Forsyth (2023)) These methods combine neural prediction with post-training refinement: an encoder-decoder network predicts an initial set of convex polytopes, which is followed by gradient-based optimization align the primitives closely to observed geometry. This ap- proach is viable because the primary supervision for primitive fitting is a depth map (with heuristics that create 3D samples, and auxiliary losses to avoid degenerate solutions). Note that ground truth primitive parameters are not available (as they could be in many other computer vision settings e.g.. segmentation Kir- illov al. (2023)). This is why the losses encourage the primitives classify points near the depth map boundary correctly instead of directly predicting the parameters.
Rendering the primitives. We condition the RF model on the primitive representation via a depth map, ob- tained by ray-marching the SDF from the original viewpoint of the scene. Depth conditioning abstracts away potential 'chatter' in the primitive representation from e.g. over-segmentation, while simultaneously yielding flexibility in fine details (depth maps typically lack pixel-level high-frequency details). Depth-conditioned image synthesis models are well-established e.g. Zhang al. (2023). Because it's hard edit a depth map, but easy to edit 3D primitives, our work adds new level of control to the existing image synthesis models. As we establish quantitatively in Table - our primitive generator is extremely accurate, and our evaluations show that we get very tight control over the synthesized image via our primitives. This means that whatever domain gap there is between depth from primitives and depth from SOTA depth estimation networks is not significant.
Scaling to in-the-wild scenes. We collect 1,8M images from LAION to train our primitive prediction models. To obtain ground truth depth supervision, we use DepthAnythingy2 Yang al. 2024). We lift the depth map to a 3D point cloud using the pinhole camera model.
3.2 DEPTH- CONDITIONDDD INPAINTING IN RECTIFIED FLOW TRANSFORMERS
Adding Spatial Conditions. We build upon the sfate-of-the-ar Flux, rectified flow model Esser et al.
(2024): Labs 2024). Older ControlNet implementations Zhang et al. (003)) train an auxiliary encoder that adds information to decoder layers of base frozen U-Net. Newer implementations, including models supplied by the Black Forest Labs developers, concatenate the latent x and condition (e.g., depth map) c an input the network, yielding tighter control. FLUX. 1 Depth [dev] re-trains the RF model with the added conditioning; FLUX.1 Depth [dev] LoRA trains LoRA layers on top of a frozen base RF model. Both options give tight control and work well with our primitives, though LoRA exposes an added parameter loraweight E. [0, 1] tuning how tightly the depth map should influence synthesis. This is helpful when the primitive abstraction is too coarse relative to the geometric complexity of the desired scene (see Fig.
Role of Hint and Mask. core contribution of this work is an algorithm to generate "hint" image to guide the image generation process, as well as confidence mask (see See 3.3). The hint and mask influence the generation within timesteps lnnd S l. S (san, which are hyperparameters. The mask m E [0, 1] specifies regions where the hint should guide the output. The hint is encoded into latents Xhint via the VAE. During denoising, the latents are updated as x; = (1 -m) Xaint, + m X2, where Xpint, is the noised hint latent timestep t: Xhim,t current timestep's s noise level before incorporation, ensuring consistency with the denoising process. Outside [fend, ) the hint and mask are ignored.
SchedulerSSalleNoise (Xtin, f, c). Thus, the hint image is noised to match the
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"Piysi can and matal hall
Scale primitives
Move primitives
Remove object
Move camera
Editable Primitives
peps
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pepsi
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Figure 3: Editable Primitives as a Structured Depth Prior for Generative Models. Our method uses 3D convex primitives depth maps (shown as insets in the top row) are used to condition a pretrained depth-to-image generative model. The top row shows primitive configurations after sequential camera motion-alongside their corresponding derived depth maps. The bottom row shows the resulting synthesized images. Unlike direct depth editing, which is unintuitive and underconstrained, manipulating primitives offers structured, interpretable, and geometry-awann interface for controllable image generation.
editable intermediate representation from which depth maps derived. These
scaling, deletion, and
Source Image
Drag points
Drag Diffusion
Source primitives
Move primitives
Ours
Figure 4: Comparison with Drag Diffusion Shi et al. 2024). First row: Given a scene (first column), attempt reposition objects using recent point-based image editing method by drawing drag handles (second column). However, drag points are ambiguous: it is unclear whether the intended operation translation scaling. As result, the output lacks geometric consistency (third column). E.g., the clock changes shape, and pushing it deeper into the scene fails to reduce its size appropriately; fine details on the lost. In contrast, Generative Blocks World infers 3D primitives (fourth column) that can be explicitly manipulated (fifth column), producing a plausible image that respects object geometry, scale, positioning, and texture (last column). We also compare with proprictary models supplement. Second row Drag Diffusion requires many arrows place the objects. Notice how objects still do not move precisely where we want them, and there are shape and color mismatches on the rendered watermelon and potato. Our result respects both texture and geometry.
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3.3 TEXTURE HINT GENERATION FOR CAMERA AND OBJECT EDITS
number of methods have been proposed to preserve texture/object identity upon editing an image. common and simple technique is to copy the keys and values from a style image into the newly generated image (dubbed "style preserving edits"). For older U-Net-based systems, this is done in the bottleneck layers Bhat et al. (024). For newer DiT', this is done at selected "vital" layers Avrahamii et al. (025
our testing, key-value copying methods are insufficient for camera/primitive moves (see Fig. G Further, because of our primitives, we have geometric representation of the scene. Here we demonstrate a routine to obtain a source "hint" image Xhint as well as a confidence mask m that can be incorporated in the diffusion process. The hint image is rough approximation of what the synthesized image should look like using known spatial correspondences between primitives in the first view and the second. The confidence mask indicates where we can and cannot trust the hint, commonly occurring near depth discontinuities. We rely on the diffusion machinery to essentially clean up the hint, filling gaps and refining blurry projected textures so it looks like real image. The result of our process is an image that respects the text prompt, source texture, and newly edited primitives/camera.
Creating point cloud correspondences We develop method that accepts point clouds at the ray--aiimm ray-primitive intersection points, a conve_map integer array indicating which primitive was hit at each pixel, a list of per- primitive transforms (such as scale, rotate, translate), and a hyperparameter max _distance for discarding correspondences. This procedure also robustly handles camera moves because the input point clouds are representations of the same scene in world space.
Creating texture hint Given correspondence map of each 3D point in the new view relative to the original view, we can apply this correspondence to generate a hint image that essentially projects pixels in the old view onto the new view. This is the Xhint supplied to the image generation model, taking into account both camera moves and primitive edits like rotation, translation, and scaling. The point cloud correspondence ensures that if a primitive moves, its texture moves with it. In practice, this hint is essential for good texture preservation (see Fig. (). Correspondence and hint generation take about 1-2 seconds per image; 30 denoising steps of FLUX at 512 resolution take about 3 seconds H100 GPU.
3.4 EVALUATION
We seek error metrics establish (1) geometric consistency between the primitives requested vs. the image that was synthesized and (2) texture consistency between the source and edited image. For (1) we compute the AbsRel between the depth map supplied to the depth-to-image model (obtained by rendering the prim- itives) and the estimated depth of the synthesized image (we use the hypersim metric depth module from Yang et al. (2024) to get linear depth). Consistent with standard practice in depth estimation, we use least squares to fit scale and shift parameters onto the depth from RGB (letting the primitive depth supplied to the DM be GT).
To evaluate texture consistency, we apply ideas from the novel view synthesis literature and our existing point cloud correspondence pipeline. Given the source RGB image and the synthesized RGB image (conditioned on the texture hint), we warp the second image back into the first image's frame using our point cloud correspondence algorithm. If we were to synthesize an image the first render's viewpoint using the second render, this is the texture hint we would use. In error metric calculation, the first RGB image is considered ground truth, the warped RGB image from the edited synthesized image is the prediction, and the confidence mask filters pixels that are not visible in view 1, given view 2. This evaluation procedure falls in the category of cycle consistency/photomerri losses that estimate reprojection error Fang et al. (202)); Jeong
et al. (2024) Li al. 2025); Qin al. (2025).
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Initial
Edited
Initial
Edited
Initial
Edited
Initial
Edited
Depth
Primitives
LooseControl Ours
Figure 5: Comparison with LooseControl Bhat (2024). Existing work struggles with camera moves. Four scenes (left side of each pair), synthesized from the depth maps shown. In each case, the camera is moved to the right (right side of each pair), and the image resynthesized. Note how, for LooseControl, the number of apples changes (first pair); the level of water in the glass changes and there is an extra ice cube (second pair); the duck changes (third pair); and an extra rock appears (fourth pair). In each case, our method shows the same scene from different view, because the texture hint image is derived from the underlying geometry, and strongly constrains any change.
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Figure 6: Projection- Based Texture Hints Preserve Object Identity After Edits. This figure compares projection-basee texture hints against StableFlow Avrahami et al. (025). which uses vital-layer key-value injection. First two columns: input primitives and image. Third: edited primitives. Fourth: synthesis from original depth, revealing consistent geometry but altered texture. Fifth: StableFlow's approach often changes texture object identity. Sixth: our projection-based baaedd hints maintain texture fidelity despite edits. Seventh: combining both approaches sometimes improves fine detail recovery (c.g., the treasure chest).
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Table 1: Comparison of image reconstruction and generation metrics between our method and LooseCon- trol. AbsRelae and AbsRelas absolute relative emrors evaluating how well the generated images adhere to the requested primitive geometry (source and modified, respectively). PSNR and SSIM are evaluated by reprojecting the second synthesized image back to the original camera viewpoint (see Sec 3.4) and measur- ing texture consistency with the source. Observe how our procedure simultaneously offers tight geometric adherence the primitives while preserving the source texture. Results obtained by averaging 48 test im- ages with random camera moves. Because [bht et al. (2024) does not offer primitive extraction code, we supply our own primitives to both methods for evaluation. We use 10 parts for this evaluation.
Method
AbsRel.rc + AbsRelan +
PSNR f
SSIM 1
Ours
0.072
0.076
18.7
0.874
LonseControl Bhat et al. 2024)
0.143
0.146
6.65
0.670
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Figure 7: Applying same primitive edit for different text prompts at coarse scale (K E {6, {6 parts). First row each subplot contains source primitives and depth (first two columns); the confidence mask for hint generation, followed by four source RGB images. Second row shows the modified primitives and depth, followed by the hint image Thints followed by the four corresponding edited images. At coarse scales, moving primitive can move a lot of texture at once. Observe how our hint generation procedure automatically yields confidence masks and hints, assigning low confidence to boundaries of primitives that moved (e.g., the dog's hair) and reveals holes when moving objects. The image model cleans up the low- confidence regions and even handles blurry/aliased texture in the hint when fend >> O, meaning that the hint is not used for some denoising steps.

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Soance
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Figure 8: Failure cases. Top: Illumination misalignments. Our pixel-space texture hints fail to model lighting (e.g., reflections, shadows) outside primitive boundaries. Consequently, moving an object like the bread stack does not update its static reflection. Middle: Poor de- composition. In cluttered scenes or near image edges, sparse depth can cause primitive fitting to fail, incorrectly merging adjacent objects (bottle and paper towel) and resulting in poor control. Rotation artifacts. Large object rota- tions (50 degrees) disrupt geometry and texture consistency, causing distortions or hallucinated content (warped text), likely due to distribution shift in the texture hints.
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4 RESULTS
Fig. shows how users can manipulate depth map inputs to depth-to-image synthesizers; Fig shows camera moves. We have precise control over synthesized geometry while respecting texture. The evaluation in Table D demonstrates we hit both goals conclusively. Existing texture preservation based on key-value transfer not preserve details very well, only high-level semantics and style. We ablate the advantage of texture preservation approach in Fig. 6 When there are few primitives, moving one primitive affects big part of the scene; when there are of primitives, we can make fine-scale edits. We show several such examples in Figs.
I0 in supplement.
5 DISCUSSION
3D primitives offer precise geometric control over image generation model outputs, and preserve high-level textures more effectively than key-value transfer methods. Our method works because primitive decomposi- tions offer several useful properties: they selectable; they object-linked; they are compact; they allow edits at coarse and fine grain; and they are accurate enough to yield depth maps that support high-quality texture projection. Our pipeline designed allow users to choose between coarse and fine control by adjusting the number of primitives to suit the editing task and scene context.
Our methods have difficulty with fee mug); additional segmentation and masking, more primitives, or more types of primitive might help. Depth-of-field blurring/bokch may not resolved sharpened when bringing out-of-focus objects into focus. Significant object rotations may also fail (see Fig. B.. In an interactive workflow, manually expand- ing the confidence mask include problematic regions e.g., unwanted reflections that don't 't move with a primitive, can fix some issues. Future work that applies our point correspondences within the network layers themselves (e.g., in vital layers) may yield more robust solutions. Our method does not account for view- dependent lighting effects and does not enforce temporal consistency across frames for video synthesis.
non-convex shapes (e.g. underside of chair or handle of cof-
Our work highlights the delicate links between text prompt, hint image, initial noise tensor, and depth map. Current inverters do not support our editing model, apparently because edited images should start from the same noise tensor and prompt as the source image to achieve good results. Certain edits that are at odds with the text prompt likely cause problems (e.g., g., if the prompt mentions object is the right, but a user manipulates the primitives to move the object to the left). Changing the text prompt could work in some circumstances (Fig.
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ACKNOWLEDGEMENT
This material is based upon work supported by the National Science Foundation under Grant No. 2106825. This research used both the DeltaAl advanced computing and data resouree, which is supported by the Na- tional Science Foundation (award OAC 2320345) and the State of Illinois, and the Delta advanced computing and data resource which is supported by the National Science Foundation (award OAC 2005572) and the State of Illinois. Delta and DeltaAl are joint efforts of the University of Illinois Urbana-Chane Chaana-Champaign and its National Center for Supereomputing Applications.
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15


Reviews:
[{"review_id": "4pznSkl8Jy", "reviewer": "Reviewer_UHGg", "summary": "This paper introduces a method for 3d aware image editing. This method directly edits 3D primitives, which is very accurate, and more intuitive to interact. The method can be divided into these stages: 1) decompose 2d image into 3d primitives, using pretrained convex decomposition models 2) edit the 3d primitives 3) render the modified primitives into depth map and texuture hint image 4) use a diffusion model Flux Depth to convert the depth map and texture hint image into the final edited image", "questions": "1. Is it possible to show some hint images for text textures, like the cases in row 3 of Figure 8, I feel this will helps us to better understand the texture mismatch.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. This method is training free, by leveraging existing models, it is able to provide a very good image editing result\n2. The speed is very fast, better than other 3d aware editing methods I known\n3. The texture Hint injection is pretty good, do not require training the diffusion model, just doing the injection during inference, but still get a very good result. It even keeps the text texture, which is amazing.\n4. Overall, I love this paper very much, it leverages foundamental graphics techniques, but achieving something big! The quality is very good, and the methods offer a great speed.\n5. The failure cases are fully discussed", "weaknesses": "Thanks a lot for the authors taking a time to discuss the failure cases, I feel the discussions are very valuable. All the weaknesses are acceptable, and I feels some can be solved by more advanced models within this framework. For example, the first row in Figure 8 is very likely to be a failure of Flux Depth, not the framework itself.", "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": "miK7gtAAwn", "reviewer": "Reviewer_6yrZ", "summary": "Learn to do controlled spatial edits to a photo by learning to extract editable primitive 3D representations of the image contents which can then be modified by a human easily and conditioned on do generate a new view of the modified scene. Core idea is to find a way of doing 3D modifications to an image that is easier to do for a human and condition a diffusion model on.", "questions": "Is there any reason why more quantitative baselines cannot be run to better inform the audience of how this work sits in comparison to other existing works?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "Lots of qualitative demos for the paper and the quality of the model at preserving scene contents.", "weaknesses": "Very minimal quantitative results to compare with other works, quantitative results feel like they're lacking overall. There are other works that do a similar style of task prompting from optical flow / correspondences (motion prompting and go-with-the-flow were the ones I knew, but you also referenced drag-diffusion). I might be wrong, but it feels like some more quantitative comparisons could be done against these kinds of models perhaps? This paper is an interesting way of approaching the problem of image edits but as someone that isn't working on this specific problem, I just don't have enough of an idea of how this sits in the landscape of existing works from the quantitative results provided.", "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": "hKUyTOoIMp", "reviewer": "Reviewer_VvNT", "summary": "This paper introduces \"Generative Blocks World,\" a novel and interesting framework for image editing that leverages explicit 3D representations. The core idea is to decompose an image into a set of 3D convex primitives, which a user can directly manipulate (e.g., move, scale, rotate) to edit the scene's geometry. A new image is then synthesized by a pretrained, depth-conditioned generative model (FLUX), guided by a depth map rendered from the edited primitives and a novel texture hint mechanism designed to preserve object identity. The motivation is clear, and the proposed method offers a training-free (at inference time) approach to solving challenging, geometry-aware image editing tasks.", "questions": "Same as weakness.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Novelty and Motivation: The approach of revitalizing classic \"blocks world\" concepts for controlling modern generative models is highly innovative. It provides a clear and compelling solution for 3D-aware image manipulation, which is a significant problem in the field.\n\n2. Decoupling of Geometry and Texture: The framework effectively decouples geometric control (via primitives and depth maps) from appearance generation (via the generative model and texture hints). This modularity is a key strength, allowing for precise and predictable geometric edits.", "weaknesses": "1. Limited Quantitative Evaluation: The quantitative comparison is confined to a single baseline (LooseControl) on a small, unstated set of test images. The paper's claims of superiority would be significantly strengthened by a more extensive evaluation on standard image editing benchmarks and against a wider array of recent methods, especially those with different interaction paradigms (e.g., drag-based).\n\n2. Lack of Critical Ablation Studies: The paper is missing important ablation studies that would provide valuable insight into the method's internal mechanics and sensitivity to hyperparameters. For instance, an analysis of how the texture hint's application window (i.e., the start and end timesteps, $t_{start}$ and $t_{end}$) affects generation quality is crucial. Without such analysis, the reader cannot fully appreciate the contribution of this specific component or understand the trade-offs involved in its tuning.\n\n3. Unaddressed User Interaction Challenges: The paper claims that manipulating primitives is an \"intuitive\" and \"natural\"  interaction paradigm. However, it does not address the significant practical challenges of this interface. For complex scenes or objects decomposed into many primitives, requiring the user to manually select, group, and manipulate these components could be tedious and imprecise. The paper would benefit from a more detailed discussion of the proposed interaction workflow and, ideally, a user study to validate its usability claims against other interfaces.\n\n4. Unintended Background Alterations: A notable weakness, stemming from the reliance on a holistic generative model like FLUX, is the tendency for static background regions to change during an edit. When a foreground object is manipulated via the depth map, the model re-synthesizes the entire image to ensure global coherence. This often results in undesirable and unpredictable alterations to the background, which should have remained unchanged. This lack of localized control undermines the precision that the primitive-based editing promises.", "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": "OpOzPVS5uy", "reviewer": "Reviewer_Ka9k", "summary": "This paper proposes a new system named Generative Blocks World, for interactive image editing. The authors argue that primitive is a more straightforward, and intuitive concept for user to interact with rather than raw depth map. The detailed process of the model includes a primitive extraction model, hint image generation process and final image editing model. The authors first train the primitive extraction model on a subset of LAION dataset to predict primitives (with known number for each trained model). Then given an image to be edited, user can manipulate the extracted primitives to achieve both camera view editing and scene components editing. The edited primitives are used to generate hints: including depth map, and a \"warped\" hint image. These components are used to condition a depth-conditioned FLUX model for final output. The authors also conduct extensive analysis of the proposed system to show the validness of each component.", "questions": "I feel like the paper is written in a rush and some details are not clear, so maybe my aforementioned weakness is with some misunderstandings.\nMy questions include:\n1. did the authors actually fine-tune the FLUX-Depth to take hint image as input, or actually not, then that step is just SDEdit? (Because it seems very much like SDEdit)\n2. What exactly are the conditions from primitive manipulation? Depth? Hint image? What about the confidence mask, I think this part requires an additional figure to explain for better understanding.\n3. What's the training time consumption for the primitive extraction model? And if there is, what's the training consumption for FLUX-Depth finetune? Is there any difference in training dynamics for primitive extraction model with different primitive numbers?\n4. See weakness above.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The proposed Generative Blocks World has a good motivation. Primitive is a more straightforward concept comparing to raw depth map, and varying number of primitives also provide a flexible way to understand the image content. \n\nAuthors also conduct thorough analysis on the proposed method's components, which provides a very detailed understanding.\n\nThe results look promising with both camera control and image component control.", "weaknesses": "The most important weakness of this paper is currently I think the authors are lack of analysis of the importance of the proposed \"primitives\". From my perspective, the actual image generation model is a finetuned FLUX model that conditions on a combination of \"hint image, depth, (mask?)\" The authors should:\n1. Validate the effectiveness of each components in the condition combination through some qualitative examples or analysis. But I also understand if this requires retrain the model and time is limited. But I think at least some intuitive discussion is needed.\n2. This pipeline, in my opinion, can actually be achieved via: use segmentation mask to achieve the same usage of primitives, and use SDEdit + a depth-conditioned FLUX model to accept both hint image and depth control. So I think authors should do some qualitative comparisons with this one or even three baselines to prove the validness of the proposed Generative Block World:\n- move segmentation mask to warp the depth and hint image, still use current (finetuned?) FLUX-Depth\n- primitive pipeline as current, but use original FLUX-Depth + SDEdit for hint image condition\n- move segmentation mask to warp the depth and hint image, use original FLUX-Depth + SDEdit for hint image condition\nI think these baselines are valid, but maybe time is limited so some comparison examples would be fine", "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)