Image-to-3D generation faces inherent semantic ambiguity under occlusion, where partial observation alone is often insufficient to determine object category. In this work, we formalize text-driven amodal 3D generation, where text prompts steer the completion of unseen regions while strictly preserving input observation. Crucially, we identify that these objectives demand distinct control granularities: rigid control for the observation versus relaxed structural control for the prompt. To this end, we propose RelaxFlow, a training-free dual-branch framework that decouples control granularity via a Multi-Prior Consensus Module and a Relaxation Mechanism. Theoretically, we prove that our relaxation is equivalent to applying a low-pass filter on the generative vector field, which suppresses high-frequency instance details to isolate geometric structure that accommodates the observation. To facilitate evaluation, we introduce two diagnostic benchmarks, ExtremeOcc-3D and AmbiSem-3D. Extensive experiments demonstrate that RelaxFlow successfully steers the generation of unseen regions to match the prompt intent without compromising visual fidelity.
Figures & tables
Figure 1: The case for multiple plausible amodal 3D interpretations under occlusion. Feedforward image-to-3D model ( e.g . , SAM3D) collapses to a single overfitted bed-like shape, while our RelaxFlow resolves the ambiguity via text-driven amodal generation , allowing users to inject explicit textual intent and steer the generation toward alternative semantically consistent amodal 3D shapes.
Figure 2: Conceptual illustration of low-pass relaxation. The background depicts a conceptual compatibility landscape over latent states, where high density corresponds to low “energy”. Star markers indicate a spurious trapped mode and the intended target mode. Throughout, we use corridor to denote a tube of latent states traced by integral curves of a conditioned velocity field while satisfying a constraint. Smoothing the prior-conditioned guidance thickens the semantic corridor and steers trajectories toward the intended mode while remaining compatible with the observation corridor.
Figure 3: RelaxFlow pipeline overview . An observation-driven branch preserves visible evidence, while the semantic guidance is injected through the intent prompt via multi-prior consensus and low-pass relaxation. Dual branches are fused via velocity blending to resolve occlusion-induced ambiguity.
Figure 4: Qualitative Comparisons. Top: Comparisons on AmbiSem-3D examples. Each contains two different intent prompts for the same observation. Our method preserves observation fidelity while enabling prompt-controlled completion. Bottom: The case in ExtremeOcc-3D with an intent prompt “bed”. Under extreme occlusion, baselines either overfit the visible region or yield implausible shapes, whereas ours follows the category prior while maintaining visible evidence.
Category
Method
CLIP img↑
CLIP txt↑
FID ↓
LPIPS ↓
Point-FID ↓
Non-Occlusion Aware
TRELLIS
0.78
23.14
122.68
0.83
141.48
Ours (w/ TRELLIS)
0.80
24.09
100.75
0.80
97.79
Occlusion- Aware
Amodal2D+SAM3D
0.76
21.59
94.38
0.56
127.27
Amodal3R
0.77
22.29
118.49
0.60
129.46
SAM3D
0.84
24.08
50.73
0.54
100.38
Ours (w/ SAM3D)
0.87
27.26
39.44
0.51
81.11
Table 1: Quantitative comparison on ExtremeOcc-3D. Methods are grouped by occlusion awareness. Our method improves over both backbones across all metrics.
Method
CLIP Score
User Study
CLIP img↑
CLIP txt↑
Alignment ↑
3D Fidelity ↑
Overall Pref. ↑
SAM3D
0.85
26.29
4.84%
13.59%
9.22%
TRELLIS (multi-view)
0.80
26.59
3.75%
8.28%
6.02%
SDXL + TRELLIS
0.81
26.76
6.09%
8.91%
7.50%
SDXL + SAM3D
0.79
26.71
11.41%
6.09%
8.75%
Ours
0.87
27.23
73.91%
63.13%
68.52%
Table 2: Quantitative comparison on AmbiSem-3D. Left: automatic metrics (CLIP similarities). Right: user study results ( n=32 ). Our method achieves the best scores on both evaluations.
Figure 5: Visual ablation results. Top: effect of key components. Bottom: effect of varying the number of prior images N .
Figure 6: Ablation studies on ExtremeOcc-3D with SAM3D backbone. (a) Component ablation and hyperparameter sensitivity. Removing low-pass relaxation or visibility mask degrades performance; extreme ρ or σ values also hurt. “Prior from generation” uses Z-Image-generated priors instead of retrieved ones. (b) Effect of prior count N : moderate N improves consensus, but too many priors introduce conflicts.
Appendix figures & tables14 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 7: Visualization of all cases in AmbiSem-3D.
Figure 8: More examples of cases in ExtremeOcc-3D.
Method
CLIP img↑
CLIP txt↑
FID ↓
LPIPS ↓
Point-FID ↓
Wan2.2-TI2V + TRELLIS
0.78
22.98
107.49
0.85
110.3
MV-Adapter + TRELLIS
0.80
22.70
148.66
0.81
238.3
TRELLIS (text+image)
0.82
26.36
116.51
0.79
137.7
Nano Banana Pro + 3D
0.83
27.49
77.68
0.77
88.6
Ours
0.87
27.26
39.44
0.51
81.1
Appendix
Table 3: Comparison with other baseline families on ExtremeOcc-3D. These baselines cover video generation, multi-view generation, direct text+image conditioning, and commercial 2D editing pipelines.
Method
CLIP img↑
CLIP txt↑
Wan2.2-TI2V + TRELLIS
0.80
26.71
MV-Adapter + TRELLIS
0.81
25.54
TRELLIS (text+image)
0.81
27.24
Nano Banana Pro + 3D
0.81
27.41
Ours
0.87
27.23
Appendix
Table 4: Comparison with other baseline families on AmbiSem-3D. Because AmbiSem-3D has multiple valid completions and no unique 3D ground truth, we report CLIP-based observation and text alignment.
Figure 9: Qualitative comparison with the TRELLIS (text+image) fusion baseline. This baseline often produces implausible geometry because the objects generated by its text- and image-conditioned branches are not visually consistent, making spatial alignment difficult. In contrast, our method injects text conditioning as low-pass semantic guidance in the visual latent space, yielding more coherent and geometrically consistent results.
Figure 10: Qualitative comparison with Wan2.2-TI2V + TRELLIS and MV-Adapter + TRELLIS. For Wan2.2-TI2V, we use the enhanced prompt: “A clean studio turnaround video of intent prompt, centered in frame, isolated from the background, with the camera orbiting smoothly around the object to reveal multiple viewpoints. The object stays fixed while only the camera moves. Consistent geometry, consistent appearance, product-shot lighting, plain neutral background, the full object remains visible in every frame, no extra objects, no scene changes, no cuts, no zoom.” Despite this careful prompting, the video-generation pipeline often fails to produce 3D-consistent frames, leading to distorted or blurred 3D reconstructions. The multi-view generation pipeline also struggles in this setting, often failing to preserve the observation evidence while following the text prompt, which results in distorted outputs. In contrast, our method operates during 3D generation and produces results that are both geometrically consistent and faithful to the input observation and text intent.
Figure 11: Qualitative comparison with Nano Banana Pro + 3D. As a non-3D-aware commercial 2D editing model, Nano Banana Pro can produce edits with implausible geometry, back-view inconsistency, and attribute leakage. In contrast, our method achieves text-guided amodal 3D generation that remains consistent with both the text intent and the observation evidence.
Method
CLIP img↑
CLIP txt↑
SAM3D
0.839
25.6
Ours
0.845
26.9
Appendix
Table 5: Extended 100-case AmbiSem-3D evaluation.
Backbone
Method
Time (s/sample)
Allocated (GB)
Reserved (GB)
SAM3D
Base
15.59
18.07
21.85
Ours
38.40
18.60
22.60
Overhead
2.46 ×
+2.9%
+3.4%
TRELLIS
Base
12.81
5.57
5.60
Ours
23.31
6.02
6.07
Overhead
1.82 ×
+8.0%
+8.4%
Appendix
Table 6: Runtime and memory overhead. Memory is peak GPU memory in GB. RelaxFlow adds extra branch evaluations, increasing runtime, while peak memory grows modestly. The current implementation prioritizes memory over runtime; batched branch inference and feature caching may further reduce wall-clock overhead.
Figure 12: More qualitative results. TRELLIS and SAM3D remain overfitted to the observed geometry, producing either a shield or a TIE fighter regardless of intent, whereas our method generates either structure as specified by the intent prompt.
Figure 13: More qualitative results.
Figure 14: More qualitative results.
Figure 15: More qualitative results on ExtremeOcc-3D.
Figure 16: Failure cases under conflicting conditions. When the observation and prior disagree, no evidence-consistent solution exists, and our method may interpolate between them as a soft concept fusion due to asymmetric guidance.
Current 3D generation methods lack explicit local control: geometric adherence is often defined by a global control strength, and appearance cannot be specified locally. We present SpaceFlow, a training-free pipeline for locally controllable 3D generation from text descriptions and a collection of geometric primitives. Each primitive serves as a proxy for an object part and is assigned a local control level, enabling users to specify whether regions should strictly follow the input shape or allow generative completion. During structure generation, we enforce these spatial constraints within the generative flow process. For appearance synthesis, the generated structure is segmented and matched to the primitives. Each generated part is conditioned only on its assigned text or image cue, thereby limiting cross-part leakage. Regional geometry metrics demonstrate that SpaceFlow preserves the specified geometry in high-control regions and enables plausible shape variation in low-control areas. A user study further indicates that the resulting balance between geometric fidelity and generative freedom remains competitive in overall quality. When evaluating appearance on fixed geometry, text-conditioned routing achieves state-of-the-art prompt faithfulness and color/material accuracy. Qualitative results additionally show localized routing of image cues. The project page is available at SpaceFlow3D.github.io.
Neil De La Fuente, Joan Lafuente, Mukhammadali Sayfiddinov +5
Generating complete 3D objects under partial occlusions (i.e., amodal scenarios) is a practically important yet challenging problem, as large portions of object geometry are unobserved in real-world scenarios. Existing approaches either operate directly in 3D, which ensures geometric consistency but often lacks generative expressiveness, or rely on 2D amodal completion, which provides strong appearance priors but does not guarantee reliable 3D structure. This raises a key question: how can we achieve both generative plausibility and geometric coherence in amodal 3D modeling? To answer this question, we introduce GENA3D (GENarative Amodal 3D), a framework that integrates learned 2D generative priors with explicit 3D geometric reasoning within a conditional 3D generation paradigm. The 2D priors enable the model to plausibly infer diverse occluded content, while the 3D representation enforces multi-view consistency and spatial validity. Our design incorporates a novel View-Wise Cross-Attention for multi-view alignment and a Stereo-Conditioned Cross-Attention to anchor generative predictions in 3D relationships. By combining generative imagination with structural constraints, GENA3D generates complete and coherent 3D objects from limited observations without sacrificing geometric fidelity. Experiments demonstrate that our method outperforms existing approaches in both synthetic and real-world amodal scenarios, highlighting the effectiveness of bridging 2D priors and 3D coherence in generating plausible and geometrically consistent 3D structures in complex environments.
We identify occlusion reasoning as a fundamental yet overlooked aspect for 3D layout-conditioned generation. It is essential for synthesizing partially occluded objects with depth-consistent geometry and scale. While existing methods can generate realistic scenes that follow input layouts, they often fail to model precise inter-object occlusions. We propose SeeThrough3D, a model for 3D layout conditioned generation that explicitly models occlusions. We introduce an occlusion-aware 3D scene representation (OSCR), where objects are depicted as translucent 3D boxes placed within a virtual environment and rendered from desired camera viewpoint. The transparency encodes hidden object regions, enabling the model to reason about occlusions, while the rendered viewpoint provides explicit camera control during generation. We condition a pretrained flow based text-to-image image generation model by introducing a set of visual tokens derived from our rendered 3D representation. Furthermore, we apply masked self-attention to accurately bind each object bounding box to its corresponding textual description, enabling accurate generation of multiple objects without object attribute mixing. To train the model, we construct a synthetic dataset with diverse multi-object scenes with strong inter-object occlusions. SeeThrough3D generalizes effectively to unseen object categories and enables precise 3D layout control with realistic occlusions and consistent camera control.