SpaceControl: Introducing Test-Time Spatial Control to 3D Generative Modeling
Authors: Elisabetta Fedele
Francis Engelmann
Ian Huang
Or Litany
Marc Pollefeys
Leonidas Guibas
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Vision
3D Generative Models
Geometric Primitives
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Generative methods for 3D assets have recently achieved remarkable progress, yet providing intuitive and precise control over the object geometry remains a key challenge.Existing approaches predominantly rely on text or image prompts, which often fall short in geometric specificity: language can be ambiguous, and images are difficult to manipulate. In this work, we introduce SpaceControl, a training-free test-time method for explicit spatial control of 3D asset generation. Our approach accepts a wide range of geometric inputs, from coarse primitives to detailed meshes, and integrates seamlessly with modern generative models without requiring any additional training. A control parameter lets users trade off between geometric fidelity and output realism. Extensive quantitative evaluation and user studies demonstrate that SpaceControl outperforms both training-based and optimization-based baselines in geometric faithfulness while preserving high visual quality. Finally, we present an interactive interface for real-time superquadric editing and direct 3D asset generation, enabling seamless use in creative workflows. Project page: https://spacecontrol3d.github.io/

Published as a conference paper at ICLR 2026
SPACECONTROL: INTRODUCING TEST-TIME SPATIAL CONTROL TO 3D GENERATIVE MODELING
Elisabetta F Francis Engelmann 23
Ian Huang",
Or Litany
Mare Pollefeys'. Leonidas Guibas
'ETH Zurich Staanford University
UUS Lugano
*Technion
NVIDIA
"Equal contribution
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Wed
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Gereeis loe.
Figure 1: SPACECONTROL enables spatially controlled 3D asset generation from simple geometrie primi- tives such as superquadrics (light blue) other geometry types such as polygon meshes. Top: Rapid asset generation. From quick 3D sketches and brief text prompts, we can generate high quality assets. Bottom: Fine-grained editing, including adjusting a chair's backrest and adding armrests (left) or precisely controlling a sofa's 's dimensions and pillow arrangements (right).
ABSTRACT
Generative methods for 3D assets have recently achieved remarkable progress, yet providing intuitive and precise control over the object geometry remains a key challenge. Existing approaches predominantly rely text image prompts, which often fall short geometric specificity: language can be ambiguous, and images are difficult manipulate. In this work, we introduce SPACECONTROL. a training-free test-time method for explicit spatial control of 3D asset generation. Our approach accepts wide range of geometric inputs, from coarse primitives detailed meshes, and integrates seamlessly with modern generative models with- out requiring any additional training. A control parameter lets users trade off between geometric fidelity and output realism. Extensive quantitative evaluation and user studies demonstrate that SPaCECONTROl. outperforms both training- based and optimization-baat ing high visual quality. Finally, we present an interactive interface for real-time superquadric editing and direct 3D asset generation, enabling seamless in ere- ative workflows. Project page: https: : / /sppcccoottoooooo/ rol. g thub. io/.
baselines in geometric faithfulness while preserv-
INTRODUCTION
Generating 3D assets is a fundamental step in building virtual worlds, useful for gaming, simulation, virtual reality applications, and digital design. Recently the field of 3D object generation gained immense traction, and we are now able to create assets of previously unseen quality (Xiang et al., 2025; Zhang al., 2024; Vahdat et al., 2022; Gao et al., 2022; Wu et al., 2025; Siddiqui al., 2024;

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Zhao al., 2025; Chen et al., 2025b; Huang et al., 2025; Deb Sarkar et al., 2025). A continuing difficulty, however, is controllability i.e. the ability for users to reliably guide the object generation process that it matches the intended shapes and geometric characteristies.
Most controllable 3D generation approaches are conditioned on either text or images. Text is flexible and easy to use, but its ambiguity makes it poorly suited for specifying precise geometry. Images better constrain 3D structure, yet they are difficult to edit and unintuitive for fine-grained control. Consequently, neither modality allows artists or designers directly manipulate the object geome- try. The aim of this work is to lower the barrier to 3D asset creation by shifting control into 3D space itself. Rather than relying on text or image conditioning, this work introduces what we call spatial control: the use of 3D geometry as three-dimensional sketches that steer the synthesis of detailed 3D assets.
Existing approaches that provide spatially controlled 3D object generation fall into two categories: training-busn -based and guidance-based methods. The former extend generative models by fine-tuning them to accept particular forms of geometric inputs, for example, voxel conditioning in LION (Vah- dat et al., 2022) or primitive- and mesh-based inputs in Spice-E (Sella et al., 2024). Such approaches deliver explicit spatial control while maintaining the inference time of the base model, but require additional training, which often reduces their generalization capabilities. By contrast, guidance- based methods such LatentNeRF (Metzer et al., 2023) operate solely at inference time, avoiding retraining, but typically incur substantial optimization overhead. Other approaches combine fine- tuning with test-time optimization. For instance, Coin3D (Dong et al., 2024) fine-tunes multi-view generative model to produce geometry-consistent images, which are then used jointly with the geo- metric conditioning to optimize geometry-aware radiance field.
Another line of works focuses on enriching existing 3D assets with geometric and appearance de- tails (Michel et al., 2022; Chen et al., 2023; Barda et al., 2025), but typically assumes fine- fine-grained input geometry, limiting its usefulness in creative workflows where artists tend to begin with coarse sketches.
In this work, we present SPACECONTROL, a training-free method that introduces explicit spatial control into modern generative frameworks for text- or image-conditioned 3D generation, such as Trellis (Xiang et al., 2025) or SAM 3D (Chen et al., 2025a). The approach directly encodes user- specified geometry into the latent space as explicit guidance, requiring no additional training and enabling controllable generation from a broad range of geometric inputs, from simple geometric primitives to detailed meshes.
We evaluate SPACECONTROL against existing training-based (Sella et al., 2024) and guidance- based (Dong et al., 2024) approaches, as well as a stronger training-based variant Spice-E adapted for Trellis. Despite requiring no fine-tuning, SPaCECONTROL achieves superior geometric faithful- ness while maintaining high visual realism. Furthermore, we introduce a user interface for inter- active superquadric editing and real-time textured asset generation, facilitating seamless integration into practical design flows.
In summary, contributions are follows:
We introduce a training free guidance method that conditions powerful pre-trained gen- erative model (Xiang et al., 2025) on user-defined geometry via latent-space intervention, enabling geometry-aware generation without costly fine-tuning.
We provide extensive evaluations, including quantitative analysis and a user study, demonstrating that our method outperforms prior state-of-the-art approaches for shape- conditioned 3D asset generation.
We develop an interactive interface for intuitive superquadric editing and real-time conver- sion into detailed, textured 3D assets, facilitating practical use in creative workflows.
RELATED WORK
2.1
3D GENERATIVE MODELS
The field of 3D generation has experienced a rapid growth during the past few years both in terms of output modalities and controllability. Similar to the first image diffusion models (Ramesh al.,
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2021), early applications of diffusion models for 3D generation (Nichol et al., 2022) were conduct- ing the diffusion process in the original input space and were limited in the generated output type. More recent approaches (Vahdat et al., 2022; Jun & Nichol, 2023) started running the generation in a more compact latent space, leading to substantial improvements both terms of quality and efficiency. To achieve greater efficiency, Zhang al. (2024); Xiang et al. (2025) have begun to disentangle geometric structure from its appearance, leading to unprecedented quality in 3D genera- tions. This separation enables the isolated modification of geometry via spatial conditioning, which we introduce in this work.
2.2 CONTROLLABLE GENERATIVE MODELS
Given a pretrained generative model, there are two main approaches to introduce a new control modality: (1) methods which finetune part or the whole network to take new types of conditioning as input, and (2) training- free methods which condition the generation via inference- time guidance. In the last years, many approaches have been developed to control image generative models (Meng et al., 2022; Zhang et al., 2023), enabling conditioning in several forms such as strokes or depth maps, contrast, analogous mechanisms for object generation remain far less mature.
CONTROLLING IMAGE GENERATIVE MODELS
wide variety of methods have been proposed to introduce new control modalities to image gen- erative models. Among works based on fine-tuning, we identify two main lines of research. One category follows ControlNet (Zhang et al., 2023; Bhat et al., 2024), which adds conditional control by introducing trainable copy of the network connected via zero convolutions. The key idea is to learn control without discarding information from the original pre-trained model. Alternatively, other approaches add specialized layers to provide additional network control (Garibi et al., 2025; Hertz et al., 2022). Among training-fiee methods (Von Rütte et al., 2024; Meng et al., 2022; Sajnani al., 2025), SDEdit (Meng et al., 2022) is closely related our work. It leverages the denois- ing process of SDE-based generative models by using for example stroke paintings to initialize and condition image generation.
CONTROLLING 3D GENERATIVE MODELS
Only a few works have explored spatially grounded control of 3D generative models. One cat- egory of approaches, including Latent-NeRF (Metzer et al., 2023), Coin3D (Dong et al., 2024), Fantasia3D (Chen et al., 2023), Instant3dit (Barda et al., 2025), and Phidias (Wang et al., 2025), applies spatial control to image generative models by projecting the 3D conditioning onto multiple views and leveraging test-time optimization to synthesize a 3D representation. Alternatively, Spice- E (Sella et al., 2024) applies control directly in 3D space by fine-tuning Shap-E (Jun & Nichol, 2023) on specific ShapeNet (Chang et al., 2015) categories. While both strategies attempt explicit spatial control, they fall short of the flexibility seen in 2D counterparts. The former requires lengthy optimization and conditions 2D projections rather than the 3D volume directly. The latter necessi- tates class-specific fine-tuning, which limits generalization and prevents tuning the strength of the geometric control. In contrast, SPACECONtROl introduces test-time guidance directly within 3D generative models, providing framework that efficient, accurate, and fast.
PRELIMINARIES
Before introducing our SPACECONTROL, we review the foundations on which builds: rectified flow matching, the Trellis (Xiang et al., 2025) generative model, well as superquadrics.
3.1 RECTIFIED FLOW MODELS
Rectified flow models use linear interpolation forward (diffusion) process where for specific time step t E [0, 1], the latent z; can be expressed as z, = (1 t)zgg + te, where ~ No I) and Zo is a clean sample from the target data distribution. The backward (denoising) process is represented by time dependent velocity field v(z,, t) = V,z, In practice, starting from a noisy sample z,, we can obtain the denoised version 2n by discretizing the time interval [0, into T discrete steps, possibly
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Output
Spatial
Noise up to tp
Ze.0
Zih Skip Flow to
65
Mesh
RF
Text
A sofa"
feur
Do
Image
Structure FM
X
Flow
Rorva
Appearance FM
Input
Structure Generation (Secs. 4.2)
Appearance Generation (Sec. 4.2)
Figure 2: Model Overview, Given input conditioning which includes a spatial control, text prompt and an image (optional). SPACECONTROL produces realistic 3D assets. First the different conditioning are encoded in a latent space. Specifically, the spatial control is voxelized and encoded by Trellis' encoder E. the text is encoded by a CLIP encoder ECLIP, and the image (if present) is encoded by a DINOv2 encoder EDINO- The obtained latents Zu noised up Flow Model (FM), guided by the text prompt fcatures. The clean latents zo are then fed into the decoder DD which outputs
to obtain Ztg. From to 0, Zto
denoised by the Structure
voxel grid Xo. Then, the active voxels are augmented with point-wise noisy latent features, denoised by the Appearance Flow Model (FM), using either text or image conditioning. The clean latents can then be decoded into versatile output formats such 3D gaussians (GS), radiance fields (RF), and meshes (M) via specific decoders Do {Das, DRE, DMs.
not uniformly distributed, and recursively applying the equation
(I) where i E [ - 1), and the vector field va(-) is predicted for example by Diffusion Trans-
1((++) z(()) Vo (())))))(()))))))))))))))))))))) (t(i) ((()
t(i + 1))
former (Peebles & Xie, 2023) as in Trellis (Xiang al., 2025) or SAM 3D (Chen et al., 2025a).
Step Scheduler. Time steps are initially defined as t(T) = 1 - T/T for E [0,T], and then rescaled by factor A:
Xt(r) {(T) I+ (A- 1)((T)
(2)
Since / can be obtained from and vice versa, we will refer either one interchangeably.
3.2 TRELLIS
Trellis (Xiang et al., 2025) is recent 3D generative model which employs rectified flow models to generate 3D assets from either textual image conditioning. Specifically, it consists of two separate steps of generations, where the first aims to generate the geometric structure, while the second focus on the appearance. Note that SPACECONTROL is equally applicable more recent works such as SAM 3D (Chen et al., 2025a), which follow the same two-step generation approach.
Structure Generation. In the first stage, noisy latent variable zı g16x16x16x8 is sampled from (O, 1) and denoised by a rectified flow model iteratively applying Eq, using either image or text conditioning. Specifically, text conditions are encoded via the CLIP (Radford et al., 2021) text encoder, while image conditions are encoded via DINOv2 (Oquab et al., 2024). The denoised latent 2n then decoded by decoder into a binary occupancy grid x E (0, 1) 111x61161 which represents the coarse spatial structure of the generated 3D asset. Note that the pretrained decoder 'D is accompanied by a corresponding pretrained encoder E. While & is not utilized during standard Trellis inference, it is essential for our guidance mechanism, as it maps the spatial conditioning signal into the shared latent space (see Sec. 4).
Appearance Generation. In the second stage, the L active voxels of the binary occupancy grid are augmented with point-wise noisy latent features $ E _ sampled from (0, which are denoised by second flow model using either text image conditioning. The clean latents So E Lx8 can then be decoded into versatile formats such as 3D) Gaussians (GS), Radiance Fields (RF), and Meshes (M) via specific decoders Do (Das, DRF DM ). We refer the reader Xiang et al. (2025) for further details.
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Realistic
Faithful
To ==
%g 1)
Eg =
To 5p
Figure 4: Crellss-faithfulnee tradcoff. The hyperparameter allows smooth control over the strength of the control. In the left figure show how variations of affects the generations quantitatively in terms spatial control (lower means more faithful and of FID score (lower means more
of Chamfer distance realistic). In the right figure we show qualitatively, visualizing how higher values of To lead to assets whose geometry looks even more similar to the control. For conciseness we only show the untextured geometry.
Superquadries. Superquadries (Barr, 1981) provide E compact parametric family of shapes capable of repre- senting diverse geometries. A canonical superquadric is defined by five parameters: scales (8x, 85; 82) and expo- nents (61, <2). With parametric coordinates (1), w) we can define their surface
17
0.9
c,s (1) cos (=)
w) cos (1)) sin (w)
(3)
sin (1)
0.1
0.5
1,3
62
Extending world coordinates requires 6 additional pose parameters (3 translation, 3 rotation), giving 11 parame- ters in total. Their compactness makes them well-suited spatial control primitives.
Figure 3: Superquadric primitives.
4 METHOD
4.1 SETUP
To introduce spatial control 3D generation, the user provides geometric conditioning signal alongside text or image prompt. Our goal is to produce 3D assets that satisfy two key criteria:
Faithfulness: the generated asset should be aligned with the control geometry.
Realism: the generated asset should retain the quality of the original model.
4.2 APPROACH
Next, we introduce SPACECONTROL (see Fig. 2) and describe how it enables guided 3D asset gen- eration by injecting spatial guidance into pretrained Trellis model. Because our control strategy differs between the first and second stages of generation, we outline each procedure in Secs. 4.2 and 4.2, respectively.
Structure generation. To control the first step of generation given an explicit control geometry we employ a similar framework to SEdit (Meng et al., 2022), where instead of using strokes to guide the generation of 2D images, we use either coarse detailed 3D geometry to guide the generation of 3D assets. Specifically, given a user-specified 3D geometry, we voxelize it to obtain x. 1 ( 664x64664 and feed x. into the pretrained encoder E to obtain Ze.0 I 1216x16x16x 16x8, Then given a specific time step la E [0, 1] we noise up the latents 2c,0 to that specific step via the rectified flows forward equation:
(4) where (N(O Z). Given Ztg can then be obtained by iteratively applying Eq. I starting from ty and employing the original Structure Flow Model. We note that this process does not require any training. We guide the generation with additional textual prompt,
Z = tozz + +(1 - to) Z..0
need of architectural changes which is helpful to disambiguate the semantics of the object. As in the standard setting, the denoised latent 2n is then decoded into final geometric structure Xy E {0, 1) 61x611x61 by D.
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Appearance generation. Given the geometric structure generated in the first stage, then play either text or image conditioning to guide the generation of its appearance, by first expanding the active voxels with point noisy latent features and then denoising them using the Appear- ance Flow Model. Notice that, even the structure generation always conditioned on text, image conditioning still be used to guide the appearance generation, allowing for finer control over the visual details (see Fig. 7a and Appendix).
Controlling the strength of spatial control. The strength of spatial control can be tuned through the parameter To- For lower values of To, the latent 2t, initialized closer the noise 21 than to the control signal 2c.0. leading the model to perform more denoising steps. This favors samples that follow the data distribution of the original Trellis, producing outputs that are generally more realistic but less faithful to the spatial conditioning. In contrast, higher values of To bias 2t, towards 2,,0- effectively skipping earlier denoising steps and preserving more of the injected spatial structure, albeit sometimes at the expense of realism.
EXPERIMENTS
5.1 COMPARING WITH STATE-OF-THEE-ART METHOSS
Tasks. We evaluate SPACECONTROL under two types of spatial conditions: (1) coarse geometry and (2) detailed geometry, using simple geometric primitives and full object meshes, respectively.
Baselines. We compare SPACECONTROL to state-of-the-art baselines for the task of 3D- conditioned object generation. We evaluate against Spice-E (Sella et al., 2024), which fine-tunes Shap-E (Jun & Nichol, 2023) to support cuboid primitives as spatial guidance. To ensure fairer comparison between backbones, we implement corresponding version for Trellis (Xiang et al., 2025), which we refer as SPICE-E-T; details on its implementation and training are provided in
Boat
Chicken
Cow
Elephant
Radio
Submarine
Tree
Whale
Control
Coin3D
Spice-E
Ours SPICE-E-T E-T
Figure 5: Qualitative Comparison of Spatially Conditioned Generation. We show generations obtained baselines on text prompts and superquadrics from the Toys4K dataset. While other methods either fail to follow the conditioning (e.g. the antenna from the radio generated by Spice- E is wrongly placed) or to generate visually appcaling 3D assets (e.g., the chicken generated by SPICE-E- T exhibits anatomically incorrect body part placements), SPACECONTROL exhibits good balance between realism and faithfulness.
conditioning our SPACECONTROL

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Table Comparison with Baselines. We evaluate faithfulness spatial and textual control via Chamfer Distance (CD, x10") and CLIP-I, and realism via FID (lexture) and P-FID (geometry). Results for SPACE- CONTROL use TO = indicates methods fine-tuned on chair and table categories. Trellis (Xiang et al., 2025)
(XX-DiTTXLL) is included for reference only as it does not support spatial guidance.
Toys4K
Chair
Table
Method
CD2
CLIP-II
FIDJ P-FID,
CD2
CLIP-II
FID, P-FIDU|
CDJ
CLIP-If
FID,
P-FID]
Trellis
117
0.33
217
78.60
14.7
0.31
129
40.82
19.7
0.30
132
49.40
Geometric Primitives
Coin3D
54.4
0.21
231
102.0
18.5
0.25
218
47.54
28.82
0.22
245
71.58
Spice-Et
65.9
0.29
233
66.52
7.66
0.29
166
38.66
10.3
0.29
148
78.85
SPICE-B-TT
39.1
0.32
223
53.51
5.92
0.31
135
39.22
4.73
0.30
122
47.36
SPACECONTROL (Ours)
14.0
0.32
221
81.3
0.98
0.30
146
34.06
3.72
0.29
157
46.28
Meshes
Coin3D
77.8
0.04
293
182.5
14.6
0.01
308
111.0
20.4
0.01
224
178.2
Spice-E (stylization)
7.40
0.30
81.21
6.37
0.30
41.51
28.2
0.29
132
58.01
SPICE-E-T'
23.3
0.32
222
90.99
22.7
0.31
132
39.70
7.59
0.30
116
46.76
SPACECONTROL (Ours)
4.89
0.29
244
72.47
0.66
0.29
137
30.96
0.48
0.28
130
42.33
the Appendix. We note that Spice-E utilizes a separate checkpoint for shape stylization when eval- uating mesh conditioning, as it yields superior results. Additionally, we compare to Coin3D (Dong al., 2024), which leverages shape guidance to first generate single view of the desired 3D asset, then uses a fine-tuned multi-view diffusion model to generate consistent views, and finally extracts the 3D representation via volumetric-base score distillation sampling.
Datasets. To evaluate how various approaches handle geometric conditioning, we construct a dataset comprising original meshes, their respective geometric primitive decompositions, and cor- responding textual descriptions. This dataset allows for a two-fold evaluation: assessing methods on mesh-conditioned generation using the original assets and shape-conditioned generation via the primitives. To measure both generation and generalization performance, our evaluation spans two ShapeNet (Chang al., 2015) categories (chairs and tables) included in the Spice-E training set, alongside objects from the Toys4K (Stojanov et al., 2021) dataset, which remain unseen by all methods during training. We utilize SuperDec (Fedele al., 2025) decompose the 3D assets into superquadrics and employ Gemini on rendered views to generate textual descriptions for the ShapeNet assets. For the Toys4K objects, we adopt the textual descriptions provided by Trellis.
Metrics. Our experiments aim to evaluate both the faithfulness to the spatial and textual control and the realism of the generated assets. Faithfulness to the spatial control is quantified using the L2 Chamfer Distance (CD) between vertices sampled from the input superquadric primitives and the generated mesh decoded by DMr. Faithfulness to the textual control is quantified with the CLIP similarity (CLIP-I) between the renderings of generated assets and the textual prompts. Realism is evaluated for texture via the Fréchet Inception Distance (FID) (Heusel et al., 2017) image renderings and for geometry, via the P-FID (Nichol et al., 2022), the point cloud analog for FID. measure the distance between the inception features
To measure the FID image rendering extracted from the original image renderings of the datasets and the generated ones. To measure the P-FID of the generated meshes we measure the distance between the PointNet++ (Qi et al., 2017) features of the generated and original object meshes.
Results. Quantitative results are reported in Tab. 1, while qualitative results are shown in Fig, 5. Both Spice-E and SPICE-E-T perform well on chairs and tables but struggle to faithfully gener- ate objects that they were not fine-tuned on (Toys4K). SPACECONTROL. significantly outperforms the baselines in all experiments terms of Chamfer Distance (CD) to the spatial control, while achieving comparable CLIP-I, FID, and P-FID scores. For completeness, we also report scores for the rext-conditioned Trellis using the DiT-XL backbone, which is also the base model used in our SPaCECONTROL. Note that for the sake of simplicity in Tab. I we only report results of SPACE- CONTROL with To = 6. However, Tp can be chosen freely by the user, depending on the desired strength of conditioning. For completeness, we report results for different values of To in Tab. 2. We

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Table Analysis T.- evaluate faithfulness spatial and textual control via Chamfer Distance (CD, scaled by 10") and CLIP-I, respectively. Realism is assessed via FID for texture and P-FID for geometry. Results are reported for spatial control provided as geometric primitives (P) and meshes (M).
Toy4K
Chair
Table
CD
CLIPIT
RD
P-FID І
CLIPA
P-FID
CD
CLIPHT
P-FID
70
P
M
P
M
M
P
P
M
P
0
117
75.1
0.33
0.29
217
251.9
76.6
79.1
11.
30.6
0.31
0.29
129
135.7
00.5
39.9
19.7
19.21
0.30
0.28
132
137.5
19.40
19.3
2
110
65.5
0.33
0.29
256.9
52.7
131
136.7
412
41.
18.5
43.51
134.7
51.97
41.5
66.8
32.4
0.32
252
137
34.1
31.3
6.33
2.8
133.5
61.79
6
14.0
4.80
0.32
0.29
244.
81.3
72.5
0.98
0.06
0:30
0.29
146
196.
34.0
31.0
3.72
0.48
0.29
0.28
157
131.0
46
28
42.3
8
800
1.57
0.29
0.29
211.3
94.0
77.0
0.27
0.30
0.28
156
134.3
37.1
29.2
3:29
0.19
0.29
0.28
175
127.3
60-16
43.2
209.3
can see that by increasing the value of and thus strength of the spatial conditioning, we obtain generations which align more closely to the input spatial control.
User Study. To validate the numerical results, we conduct user study (Fig. 6) involving 52 volunteers, each one evaluating randomly selected samples. Participants were asked compare pairs of generated objects, voting which one was more faithful to the in- put control shape, which model looked realistic, and which one they liked overall bet- ter (see Appendix for more details). The study is performed on the same datasets discussed above, Le.on ShapeNet (Chang al., 2015) and Toys4k (Stojanov et al., 2021). We compare our SPACECONTROL to the Spice-E and Spice-E- T baselines. We observe that our SPACECON- TROL is always the preferred method both terms of overall appearance and alignment to the input spatial control.
Overall
Faithful
Realism
average 20
23%
23%
10%
85
Wins
85%
87%
21%
69%
Losses
42%
32%
Spioe-E Spice-E-T
Spice-E Spice-E-T
Spice-E Spice-ESpiic-F-TT Spice-E-T
Figure 6: User Study Results. The bar plots present the proportion of favorable comparisons achieved by our SPACECONTROP against the baselines on overall appearance, faithfulness spatial control, and realism, respectively.
Qualitative results are shown in Fig. 5, and 7. Additional results for object editing are in the Appendix, visualizing outputs of different methods conditioned on both coarse and detailed input controls. In general, taiinig--based methods struggle to generate objects in specific poses, whereas SPACECONTROL consistently produces plausible results. For example, other methods generate cow with two heads (Spice-E and Spice-E-T), an elephant with an eye on back (Spice-F), or shapes that fail to strictly follow the spatial conditioning or exhibit low quality (Coin3D)).
5.2 ANALYSIS EXPERIMENTS
The Effect of the Control Parameter Tü- While existing methods for 3D spatial conditioning do not provide way to control its strength, our SPACECONTROL enables flexible interpolation between different levels of adherence. In this section, we evaluate how the parameter Tp governs the trade-off between fidelity to the spatial control signal and the realism of the generated asset. Quantitative results are reported in Tab. 2, using the same metrics and datasets as in Tab. 1. We further present qualitative results in Fig. 4 and in the Appendix, showing how varying the conditioning strength produces different outcomes. Adjusting To allows users to regulate this trade-off according to their preferences, balancing higher shape quality against stronger adherence to the spatial guidance. Ad- ditionally, the plot in Fig. (left) illustrates this trade-off on Toys4K, indicating that [4, 6] generally provides a good compromise between spatial adherence and shape quality.
The Role of Image Conditioning. SPACECONTROL supports multi-modal control for 3D asset generation by combining spatial guidance with natural language and optional image conditioning. While the model images are particularly useful for maintaining visual consistency during object edits, as shown in Fig. 7a and in the Appendix. Moreover, we only image prompts in the Appearance Flow
synthesize assets using geometric conditioning and textual prompts alone,
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Trellis
Coin3D
Spice-E
SPICE-E-T
OurSro
Control
n/a
"floral chair"
Mesh
Overland
"floral chair"
(a) Image Conditioning, Given the two different spatial controls shown in thefirst row, show objects generated by our SPACECONTROL without (second row) and with (third row) image conditioning.
(b) Spatial alignment. We show how different methods align the generated 3D asset with the imput condition. In the first row show the input control, in second generated and in the third, we overlay the two. All the generations use the same prompt "A wooden chair.".
Figure 7: Image conditioning and fine-grained alignment. We show analysis experiments the role of image conditioning (leff) and fine-grained spatial alignment (right).
Model of Trellis, they primarily affect texture, with only minor influence the geometry. While this capability originates from the pre-trained Trellis, SPaCECONTROl enables its practical use for cross-modal texture transfer, effectively performing style transfer from 2D images to generated 3D shapes.
Spatial Alignment. We believe that a key advantage training-free approach that performs conditioning directly in 3D space is its ability to achieve fine-grained spatial control. In this section, we provide an example where the conditioning shapes are not aligned with axis-oriented rotations. As shown in Fig. 7b, our method is the only one that perfectly aligns with the input conditioning while preserving the quality of the generated mesh. Additional results are provided in the Appendix.
6 DISCUSSION AND CONCLUSION
In summary, SPACECONTROL introduces the first training-free framework that operates directly in 3D space to enable precise spatial conditioning for high-quality asset generation. Future Work and Potential. The flexibility of SPACECONTROL. provides several promising directions for fu- ture exploration. Currently, the adherence parameter To allows users to manually navigate the realism-faithfulness tradeoff. While this enables fine-grained user control, future work could in- vestigate adaptive or learned scheduling for To based on class-specific geometry or sample density. Furthermore, formulation establishes foundation for part-aware control; extending the global adherence mechanism to localized regions would allow for varying degrees of geometric fidelity within a single object.Beyond individual assets, our method's training-free nature makes naturally extensible to complex 3D scenes. The integration of scene abstraction techniques like SuperDec (Fedele al., 2025) or Search3D (Takmaz al., 2025) offers a path toward generating realistic spatial layouts from geometric primitives. Moreover, incorporating functional scene graphs (Zhang et al., 2025) would allow SpaCECONTROL to respect semantic relationships between objects, ul- timately enabling the automated generation of "digital cousins" (Dai et al., 2024) for real-world environments.
Reproducibility Statement. Our approach builds the open-source Trellis model (Xiang al., 2025), and our experiments use open-source datasets, namely ShapeNet (Chang et al., 2015) and Toys4k (Stojanov et al., 2021). Our code is publicly available project page.

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Acknowledgments. Elisabetta Fedele acknowledges support from the ETH AI Center doctoral fellowship. the Swiss National Science Foundation (SNSF) Advanced Grant 216260 (Beyond Frozen Worlds: Capturing Functional 3D Digital Twins from the Real World), and an SNSF Mobility Grant. Francis Engelmann acknowledges support from an SNSF PostDoe mobility fellowship. Or Litany acknowledges support from the Israel Science Foundation (grant 624/25) and the Azrieli Foundation Early Career Faculty Fellowship. This research was also supported in part by an academic gift from NVIDIA and Meta. The authors also acknowledge the support from SwissAl Grant for Small Projects and an Academic Grant from NVIDIA.
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A ADDITIONAL RESULTS
A. FINE-GRAINED SPATIAL EDITING
In this section we provide additional results which show how the generations from our SPACECON- TROL. are influenced by the change of the spatial control. We show results in pairs where the textual and/or image prompts are kept fixed. We notice that by providing additional image control, we are able to preserve the texture between different generations.
Control
Mesh
Prompt Geometry
"Birthday cake"
"Daisy flower"
"Satellite"
Control
Mesh
Prompt Geometry
"House"
"Knife"
"Clock"
Figure Fine-grained spatial editing with superquadrics. Superquadries offer ane-grained spatial control that is useful not only for generating a wide variety of 3D assets, but also for editing them. They enable intuitive and localized modifications of 3D shapes. in more direct manner than text- image-only generative models in practicality. In addition to natural language prompts (top), SPACECONTROL. supports image conditioned generation (bottom), enabling consistent visual appearance across edits.
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A.2 COARSE AND FINE-GRAINED SPATIAL CONTROL WITH SUPERQUADRICS
In this section, we provide additional results generated with different control strengths. Here the hyperparameter chosen that
satisfied with the final result. Superquadrics prove be an effective tool to provide both coarse and fine-grained control to the 3D generation. By combining the expressivity of superquadries with the flexible control strength offered by our SPACECONTROL, users can condition the generation by either carefully designing geometric details or only drafting the spatial setting of the desired output.
=4
To
TV
Th
Prompt Geometry Mesh Control
"An airplane"
"A motorbike"
"Staircase"
"A snowman" "
"A duck" "
TO
TO
TO
To
Mesh Control
I
Geometry
"Greek column"
"A guitar" "Military helicopter"
"Fighter jet"
"Drumkit"
Prompt
Figure 9: Coarse and fine-grained control with superquadries. Superquadrics offer both fine- inneggrined spatial control when used to sculpt precise geometry (motorbike, staircase, helicopter) and coarse control, when only used to draft a 3D sketch (duck, drumkit).
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A.3 FINE-GRAINED ALIGNMENT WITH STATE-OF-THER METHODS
In Fig. 10 we show the results for the same experiment provided in the main paper, but with different control strengths.
T
TO
3
TO
TO
TO
TO 11
Control
Mesh
Overlaid
Figure 10: Fine-grained alignment of SPACECONTROL with different 7o. the first row we show the input control, in the second generated asset the third, we overlay the two, hetter visualize alignment. the generations use the same spatial control and the same prompt "A wooden chair.".
Furthermore, in Fig. we show practical application when fine- fine-grained spatial control can be par- ticularly useful. With method, can provide sketch of the geometric primitives composing the scene and directly condition the generation on this input, without requiring any time-consuming post-processing align the generated shapes.
Input spatial control
Output 3D assets
be used generate objects of full scenes starting from coarse conditioning. On the left show the superquadries for the scene, where each object is represented
Figure 11: SPACECONTROL for 3D generation. We show how SPACECONTROL
the right we show the assets generated with SPACECONTROI. using the geometrie primitives from the right as spatial condition. Note that each object is generated independently, by scaling the superquadrics to unit cube and giving them as spatial control to SPACECONtROL Generated objects then automatically placed, by undoing the transformation.
different color.
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A.4 LOCAL CONTROL
Explicit 3D geometric conditioning also paves the way for local semantic conditioning. While a full exploration goes beyond the scope of this work, implemented baseline approach which demonstrates that part-level semantic control can be achieved without any conceptual modification, providing solid foundation for future work.
In our setting, shape conditioning already determines the geometry of the generated object. There- fore, we are interested in using local semantic conditioning control the visual appearance of the final generation. We consider the case where the input geometric conditioning is given as su- perquadries and the user defines both global semantic prompt which defines the global semantic of the object and some local semantic prompts, which attached to each superquadric and define their respective semantics. We use the geometry of the conditioning superquadries together with the global prompt to generate the geometry of the object in the Structure FM. Once obtained the geometry of the object, the local semantic prompts are then used to condition the visual appearance generation, in the Appearance FM. In order to combine the local and the global conditioning, modify the cross-attention layer of the Appearance FM DiT's so that instead of only cross-attending to global prompt. points also cross-attend to the corresponding local prompts. In practice, first cross-attend separately to each conditioning prompt, and the feature of a point is updated with the linear combination of the cross-attention performed with global (Cglobat,)) and local (Ctocat,)) conditioning:
2,
f 0.5 CA(z, Cylobal, + 0.5 CA Qlocal,s)
where Clocul, the local semantic conditioning of the nearest superquadric point i. A qualitative result of this approach in Fig. 12, where we show how this approach can be used to generate a white chair with a red seat.
Without local semantic conditioning.
With local semantic conditioning.
Figure 12: Local semantic control. From left to right we show: the input geometric control, the 3D asset generated by globally conditioning on "A white chair." the asset generated by conditioning globally on "A white chair." and locally (on the superquadric highlighted in red) on "A read seat."
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B INTERACTIVE USER INTERFACE
In Fig. 13 we visualize our interactive user interface. Starting from scratch or from a template of superquadries, users can freely edit superquadries using their parameters, and add/delete them. Once given the conditioning, they can select a control strength (higher control strength means that the generated shape looks more like the primitives) and text (and optionally image) conditioning. They can then toggle between the input primitives and meshes and proceed with new generations. We provide a demo of the user interface in the supplementary video.
Scergearies
Suergarcc
Figure Visualization our interactive user interface. Users can control generated geometry by changing the shape the geometric primitives and deciding the strength the conditioning. Other than spatial control, users can use text and, optionally, images.
C USER STUDY
In Fig. 14, we show the web interface of our user study, From left to right, we show the given con- trol shape, and two com- peting methods. The par- ticipants then choose which generated object is more faithful to the input control shape, which model looks more realistic, and which one they like best.
SpaceControl: Controllable 3D Object Generation
at the ponell no resdu appa You 2000 shape scutac apvead procest
Contral Shape
Model
Hoidd
dltc you menaa
Figure 14: User study interface.
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D SPICE-E-T
Input
Input
Feature
Cond.
Foature
Sa
Text
We obtain our training-based baseline SPICE-E-T by adding additional conditioning layer the flow transformer blocks in the structure generator of text- conditioned Trellis model (see Fig. 15) which perform cross attention on the shape conditioning. We encode the shape conditioning using the Trellis encoder &, and we perform the Cross-Attention in that feature space. We initialize the original layers with the weights from the text-conditioned Trellis and the newly added ones ran- domly. We then train the modified Structure Generator for 120.000 iterations with batch size of 4 on the ABO dataset (Collins et al., 2022), where the shape condi- tioning obtained by running SuperDec (Fedele et al., 2025). During training, we use the same reconstruction loss of the original Trellis model.
Linear
Linear
Paak Gieb.
Grrb.
MHCA-SQ
MHCA
MHCA
FFN
FFN
xN
xN
Linear
Linear
Feature
Feature
Output
Output
Figure Comparison hetween Flow Transformer from the original Trellis (left) and the one from SPICE-E-T (right), adapted to enable spatial control via superquadries.
18


Reviews:
[{"review_id": "EeQdSF0Xtt", "reviewer": "Reviewer_wmdV", "summary": "This paper introduces SPACECONTROL, a novel training-free, test-time method for adding explicit spatial control to pre-trained 3D generative models. Addressing the limitations of ambiguous text prompts and cumbersome image editing, SPACECONTROL enables users to guide 3D asset generation using diverse geometric inputs, including primitives and meshes. The method works by encoding the user-specified geometry into the latent space of a powerful pre-trained model and using this latent code to initiate the denoising process from an intermediate timestep. A key contribution is a controllable parameter that allows users to fluidly trade off between geometric faithfulness and output realism. Extensive quantitative evaluations and user studies demonstrate that the method outperforms existing training-based and optimization-based baselines in geometric fidelity while preserving high visual quality. The authors also present an interactive user interface for practical creative workflows8888.", "questions": "1: **Auto optimization of $\\tau_0$.** The paper states that the adherence parameter $\\tau_0$ must be \"selected manually\" and that this is a limitation. Could the authors elaborate on the challenges of automating this selection? Have they explored any methods to predict an optimal or suggested $\\tau_0$ based on the properties of the input control geometry (e.g., its complexity) or its relation to the text prompt? Clarification on whether this is a fundamental, subjective trade-off or a solvable engineering problem would be valuable.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1: The proposed SPACECONTROL is a training-free method that injects explicit spatial control into a powerful pre-trained 3D generative model (Trellis) purely at test-time, which is efficient.\n\n2: The framework provides intuitive geometric control using diverse inputs, from coarse primitives to detailed meshes, and introduces a single parameter that allows users to flexibly trade off between geometric faithfulness and output realism.\n\n3: Extensive experiments show the method significantly outperforms both training-based (Spice-E, SPICE-E-T) and guidance-based (Coin3D) baselines in geometric alignment (measured by Chamfer Distance) while preserving high visual quality (measured by FID and P-FID).\n\n4: This paper also presents a practical interactive user interface that enables online editing of superquadrics for their real-time conversion into detailed, textured 3D assets, demonstrating its high utility for creative workflows.", "weaknesses": "1: **Reliance on Manual Parameter Tuning.** The crucial trade-off between realism and faithfulness is governed by the $\\tau_0$ parameter, which the paper states must be \"selected manually\"2. While Table 2 and Figure 3 show the effect of varying $\\tau_0$, the optimal value appears to be object-dependent. This manual, per-instance tuning requirement undermines the method's practicality and hinders its use in automated generation pipelines.\n\n2: **Lack of Part-Based Control.** The parameter $\\tau_0$ is applied globally, \"enforcing a uniform adherence level across the entire object\"4. This is a significant limitation, as it prevents more nuanced, part-aware editing. For example, a user cannot simultaneously enforce high geometric fidelity for one part (e.g., the legs of a chair) while allowing high realism and creative variation for another (e.g., the backrest).\n\n3: **Potential Information Bottleneck from Voxelization.** The method requires all spatial control inputs, including \"detailed meshes,\" to be first voxelized into a $64 \\times 64 \\times 64$ grid before being passed to the encoder. This coarse, low-resolution representation may act as an information bottleneck, losing the very \"fine-grained\" details the user wishes to control. The paper does not analyze the impact of this $64^3$ resolution limit on the final geometric faithfulness.\n\n4: **Limited Analysis of Failure Cases.** The paper presents many successful qualitative results (e.g., Figures 1, 4, 7, 8) but lacks a dedicated analysis of failure cases. For instance, what happens when the text prompt and spatial control are semantically contradictory (e.g., text=\"a car\", spatial control=a boat)? Or when the control geometry is highly complex or topologically different from the objects in the pre-trained model's prior?", "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": "lgSPmJZUSI", "reviewer": "Reviewer_W86Y", "summary": "The paper proposes a method to add explicit spatial conditioning to a pretrained 3D generative model (Trellis) at test time, without retraining. Users provide geometric inputs, coarse superquadrics, which enables explicit control.\n\nIt performs latent-space interpolation between the control geometry’s embedding and random noise, then uses the pretrained structure-flow and appearance-flow models to denoise and decode the final 3D asset. A scalar parameter governs the trade-off between faithfulness to the spatial control and visual realism.\n\nExperiments demonstrate that this training-free strategy yields stronger geometric fidelity than baselines while maintaining comparable realism scores (slightly reduced). The work also presents a browser-based interface for interactive shape manipulation through superquadrics.", "questions": "1. How many superquadircs would it need to precisely represent the elephant trunk shown in Fig. 3? I understand the current is one and rely on sacrificing controllability to generate this curve trunk, but what would the approximate amount of superquadrics if we want an accurate control?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "**Training-free controllability.**\nThe whole system is designed without model training or surgery, which preserves the strong 3D generative prior in Trellis [1], and the approach introduced seems to be also applicable to other / future 3D generation under the Trellis framework.\n\n**Good controllability.**\nWith superquadrics, this method enables explicit control with only a few primitives, which is user-friendly and accurate on most shapes. It also introduces a tunable adherence parameter that enables test-time adaptation according to actual usage.\n\n**Empirical quality.**\nThis method consistently improves geometric alignment relative to strong baselines and Trellis itself.\n\n```\n[1] Trellis: Structured 3D Latents for Scalable and Versatile 3D Generation.\n```", "weaknesses": "**Implied control limitation on curvy shapes.**\nThe 3D geometry representation, superquadrics, is often suitable for objects primarily containing convex shapes - cubes, spheres, cylinders, etc., while for concave shapes, it requires multiple superquadrics to compose. In pure 3D reconstruction domain, this does not impose a problem, but in terms of controllability, where simplicity or the number of the underlying primitives is more critical, superquadrics may suffer.\n\n**Limited conceptual novelty.**\nThe latent-interpolation mechanism is directly analogous to image-domain test-time editing (e.g., SDEdit [1]) and does not introduce fundamentally new generative principles.\n\n**Architecture dependency.**\nThe approach assumes access to a pretrained model with paired latent encoder and flow decoder (Trellis [2]), which means its generality to other 3D generative architectures is uncertain.\n\n**Realism and appearance degradation.**\nGeometrc metrics (Chamfer distance) is improved greatly, but this seems to be at the expense of realism (CLIP-I, FID) when compared to Trellis in Tab. 1. This means the proposed method is still a trade-off, just like the previous methods.\n\n\n```\n[1] SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations.\n[2] Trellis: Structured 3D Latents for Scalable and Versatile 3D Generation.\n```", "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": "HujubtelIQ", "reviewer": "Reviewer_VKm8", "summary": "SpaceControl is a training-free, test-time method that introduces explicit spatial control into the pre-trained 3D generative model Trellis. It achieves precise geometric control by encoding user-provided geometric inputs—such as superquadrics or meshes—into the model’s latent space and guiding the denoising process accordingly. The method supports adjustable control strength via a parameter τ₀, enabling users to trade off between geometric fidelity and visual realism. It outperforms both training-based and optimization-based baselines across multiple datasets and includes an interactive interface for real-time editing and generation.", "questions": "1. Generalizability to other 3D generative models: The method is tightly integrated with Trellis’s two-stage geometry–appearance pipeline. Can SPACECONTROL be adapted to other 3D generative architectures? and if so, how does its performance compare in terms of faithfulness and realism?\n2. Extension to richer conditioning modalities: Currently, spatial control relies on 3D primitives (e.g., superquadrics) combined with text or optional image prompts. Could the framework naturally incorporate additional conditioning signals—such as depth maps, sketches, or multi-view images—to enable more expressive and intuitive user control?\n3. Necessity and timing of geometric guidance: The geometric prior is injected only at the start of the structure generation stage (i.e., after initial noise). Given that Trellis already disentangles geometry and appearance, why not introduce spatial guidance even earlier (e.g., at ) or jointly optimize both stages with geometric constraints? Is the current design a limitation of the rectified flow formulation or a deliberate trade-off?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The motivation is clear and practical. Allowing users to directly manipulate geometry using 3D primitives, aligns well with real-world design workflows.\n2. The paper is well-structured, with a logical flow and clear figures.\n3. The performance is good and includes a user study and interactive interface to demonstrate practical utility.", "weaknesses": "1. Limited technical novelty. The core idea of injecting geometric guidance before the appearance generation stage appears to be a natural extension of Trellis’s inherent two-stage design (coarse geometry ->  fine appearance). This raises the question of whether the controllability stems more from Trellis itself than from a genuinely novel contribution.\n2. Potential generalizability problem beyond two-stage architectures. The method is tightly coupled to Trellis’s specific disentangled geometry-appearance pipeline. It is unclear how SPACECONTROL would adapt to end-to-end 3D generative models or single-stage frameworks, limiting its broader applicability.\n3. Inadequate baselines. The comparison primarily includes methods published before or during 2024, missing recent advances in controllable 3D generation (e.g., 2025 works cited in the introduction). This weakens the claim of state-of-the-art performance and raises concerns about the completeness of the evaluation.\n4. Missing References. The idea of 3D-reference-based 3D generation is similar to ThemeStation [siggraph 2024] and Phidias [iclr 2025], but only coin3d is compared.", "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": "mHNYmFDadF", "reviewer": "Reviewer_Lb61", "summary": "SpaceControl is a training-free method that introduces explicit spatial control into 3D asset generation by conditioning a pre-trained generative model (Trellis) on user-provided 3D geometry, such as superquadrics or meshes. Unlike existing approaches that require fine-tuning or optimization, SpaceControl injects geometric guidance directly into the latent space during inference using a method inspired by SDEdit. This allows users to intuitively control the shape of generated assets while preserving the realism and generalization of the base model. The method supports multi-modal inputs (text, image, and geometry) and offers a tunable parameter to balance faithfulness to the input geometry with output realism. Extensive experiments and a user study show that SpaceControl outperforms both training-based and guidance-based baselines in terms of geometric alignment and visual quality.", "questions": "1. Limited Methodological Innovation Relative to Spice-E\n2. Narrow Experimental Scope and Limited Category Diversity\n3. Manual Hyperparameter Tuning and Global Control\n4. Limited Comparison to Other Training-Free 3D Methods", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Novel Training-Free Pipeline for 3D Spatial Control\nSpaceControl introduces a new inference-time guidance mechanism that enables spatial control without any fine-tuning. This is a significant departure from existing methods like Spice-E, which require task-specific training, or optimization-based methods like Coin3D, which are slow and computationally heavy.\n\n\nGenerality and Flexibility: Because it does not modify the model weights, SpaceControl can be applied to any pre-trained Trellis model and supports a wide range of geometric inputs—from simple primitives to detailed meshes—without retraining.\n\n2. Tunable Faithfulness–Realism Trade-off\nA key contribution is the introduction of the control strength parameter τ₀, which allows users to smoothly interpolate between:\n\nHigh Faithfulness: When τ₀ is large, the output closely adheres to the input geometry.\n\nHigh Realism: When τ₀ is small, the output aligns more with the data distribution of the base model, often resulting in more natural-looking assets.\n\n3. Multi-Modal Conditioning and Practical Usability\nSpaceControl supports text, image, and geometry conditioning in a unified framework: Text guides the semantic content. Image influences appearance and style. Geometry controls the 3D structure.", "weaknesses": "1. Limited Methodological Innovation Relative to Spice-E\n\nWhile the authors position SpaceControl as a novel training-free alternative, the core idea is conceptually similar to Spice-E, which also conditions generation on geometric primitives. The main difference lies in the implementation: Spice-E uses fine-tuning, while SpaceControl uses inference-time guidance.\n\nHeavy Reliance on Trellis: The method is built directly on Trellis and does not introduce a new generative framework. Its success is largely dependent on the capabilities of the underlying model, which limits the perceived novelty.\n\n2. Narrow Experimental Scope and Limited Category Diversity\n\nThe experiments are conducted on a limited set of object categories—primarily chairs and tables from ShapeNet, and toys from Toys4K. This raises questions about the method’s scalability and generalization to more complex or diverse shapes.\n\nLack of Complex Categories: There is no evaluation on categories with intricate geometry or high structural variability (e.g., humans, animals, vehicles, or scene-level generation).\n\n3. Manual Hyperparameter Tuning and Global Control\nThe control strength τ₀ is a global, manually tuned parameter that applies uniformly to the entire object. This can be a limitation in practice:\n\nNo Part-Level Control: Users cannot specify that certain parts of the object should strictly follow the geometry while others can vary freely. This limits fine-grained creative control.\n\nPer-Instance Tuning Required: The optimal τ₀ may vary across objects, requiring users to manually experiment for each generation, which hinders fully automated pipelines.\n\n4. Limited Comparison to Other Training-Free 3D Methods\n\nThe paper compares only to Coin3D among training-free methods. Other relevant inference-time 3D guidance approaches—such as those based on score distillation sampling (SDS) or prompt-based editing in 3D—are not discussed or evaluated, leaving the reader uncertain about how SpaceControl fits into the broader landscape of training-free 3D control.", "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)