Pith. sign in

REVIEW 2 cited by

Layout-your-3D: Controllable and Precise 3D Generation with 2D Blueprint

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 2410.15391 v2 pith:PYX5XZL5 submitted 2024-10-20 cs.CV

classification cs.CV
keywords layout-your-3dgenerationassetsblueprintcompositionalcontrollablelayoutlayouts
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We present Layout-Your-3D, a framework that allows controllable and compositional 3D generation from text prompts. Existing text-to-3D methods often struggle to generate assets with plausible object interactions or require tedious optimization processes. To address these challenges, our approach leverages 2D layouts as a blueprint to facilitate precise and plausible control over 3D generation. Starting with a 2D layout provided by a user or generated from a text description, we first create a coarse 3D scene using a carefully designed initialization process based on efficient reconstruction models. To enforce coherent global 3D layouts and enhance the quality of instance appearances, we propose a collision-aware layout optimization process followed by instance-wise refinement. Experimental results demonstrate that Layout-Your-3D yields more reasonable and visually appealing compositional 3D assets while significantly reducing the time required for each prompt. Additionally, Layout-Your-3D can be easily applicable to downstream tasks, such as 3D editing and object insertion. Our project page is available at:https://colezwhy.github.io/layoutyour3d/

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. TabletopGen: Tabletop Scene Generation and Interactive Simulation for Robotic Manipulation

    cs.CV 2025-12 conditional novelty 6.0 of 10

    A training-free pipeline generates instance-level, physically interactive 3D tabletop scenes from text or one image, with a differentiable rotation optimizer and top-view spatial alignment for collision-free layouts.

  2. DreamScene: 3D Gaussian-based End-to-end Text-to-3D Scene Generation

    cs.CV 2025-07 conditional novelty 4.0 of 10

    A pipeline that generates editable 3D scenes from natural language by combining LLM-based layout planning, multi-timestep diffusion distillation, and staged camera sampling.

Pith tools