Pith. sign in

REVIEW 4 cited by

Build-A-Scene: Interactive 3D Layout Control for Diffusion-Based Image Generation

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2408.14819 v1 pith:GTDENWTL submitted 2024-08-27 cs.CV

classification cs.CV
keywords layoutcontrolgenerationapproachinteractiveobjectobjectsapproaches
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We propose a diffusion-based approach for Text-to-Image (T2I) generation with interactive 3D layout control. Layout control has been widely studied to alleviate the shortcomings of T2I diffusion models in understanding objects' placement and relationships from text descriptions. Nevertheless, existing approaches for layout control are limited to 2D layouts, require the user to provide a static layout beforehand, and fail to preserve generated images under layout changes. This makes these approaches unsuitable for applications that require 3D object-wise control and iterative refinements, e.g., interior design and complex scene generation. To this end, we leverage the recent advancements in depth-conditioned T2I models and propose a novel approach for interactive 3D layout control. We replace the traditional 2D boxes used in layout control with 3D boxes. Furthermore, we revamp the T2I task as a multi-stage generation process, where at each stage, the user can insert, change, and move an object in 3D while preserving objects from earlier stages. We achieve this through our proposed Dynamic Self-Attention (DSA) module and the consistent 3D object translation strategy. Experiments show that our approach can generate complicated scenes based on 3D layouts, boosting the object generation success rate over the standard depth-conditioned T2I methods by 2x. Moreover, it outperforms other methods in comparison in preserving objects under layout changes. Project Page: \url{https://abdo-eldesokey.github.io/build-a-scene/}

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Canvas3D: Empowering Precise Spatial Control for Image Generation with Constraints from a 3D Virtual Canvas

    cs.HC 2025-08 conditional novelty 6.0 of 10

    Canvas3D lets users arrange objects in a 3D canvas generated from a text prompt, then feeds depth, skeleton, and lighting constraints to diffusion models to produce images that match the layout.

  2. Controllable 3D Placement of Objects with Scene-Aware Diffusion Models

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Projecting a color-coded 3D bounding box into a ControlNet conditioning map gives diffusion inpainting models precise control over vehicle orientation and placement in driving scenes.

  3. SSEditor: Controllable Mask-to-Scene Generation with Diffusion Model

    cs.CV 2024-11 conditional novelty 5.0 of 10

    SSEditor generates controllable 3D semantic urban scenes from mask conditions using a triplane autoencoder and a mask-conditional diffusion model, avoiding multi-step resampling.

  4. CoT-lized Diffusion: Let's Reinforce T2I Generation Step-by-step

    cs.CV 2025-07 conditional novelty 4.0 of 10

    CoT-Diff couples a multimodal LLM's step-by-step 3D layout reasoning into the diffusion denoising loop, claiming large gains in spatial alignment for text-to-image generation.

Pith tools