title: ComposeAnything: Composite Object Priors for Text-to-Image Generation
summary: Generating images from text involving complex and novel object arrangements
  remains a significant challenge for current text-to-image (T2I) models.
  Although prior layout-based methods improve object arrangements using spatial
  constraints with 2D layouts, they often struggle to capture 3D positioning and
  sacrifice quality and coherence. In this work, we introduce ComposeAnything, a
  novel framework for improving compositional image generation without retraining
  existing T2I models. Our approach first leverages the chain-of-thought
  reasoning abilities of LLMs to produce 2.5D semantic layouts from text,
  consisting of 2D object bounding boxes enriched with depth information and
  detailed captions. Based on this layout, we generate a spatial and depth aware
  coarse composite of objects that captures the intended composition, serving as
  a strong and interpretable prior that replaces stochastic noise initialization
  in diffusion-based T2I models. This prior guides the denoising process through
  object prior reinforcement and spatial-controlled denoising, enabling seamless
  generation of compositional objects and coherent backgrounds, while allowing
  refinement of inaccurate priors. ComposeAnything outperforms state-of-the-art
  methods on the T2I-CompBench and NSR-1K benchmarks for prompts with 2D/3D
  spatial arrangements, high object counts, and surreal compositions. Human
  evaluations further demonstrate that our model generates high-quality images
  with compositions that faithfully reflect the text.
paper_authors._id: 683ebe67140f76a0a5485d51
paper_authors.name: Zeeshan Khan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T09:39:29.585Z
paper_authors._id: 668e6b47f59574a8ec2ae078
paper_authors.avatarUrl: /avatars/1cbc80ee4fb4a832783bd3dbee032d6e.svg
paper_authors.fullname: Zeeshan Khan
paper_authors.type: user
paper_authors.user: zk95
paper_authors._id: 683ebe67140f76a0a5485d52
paper_authors.name: Shizhe Chen
paper_authors._id: 683ebe67140f76a0a5485d53
paper_authors.name: Cordelia Schmid
paper_title: ComposeAnything: Composite Object Priors for Text-to-Image Generation
paper_summary: Generating images from text involving complex and novel object arrangements
  remains a significant challenge for current text-to-image (T2I) models.
  Although prior layout-based methods improve object arrangements using spatial
  constraints with 2D layouts, they often struggle to capture 3D positioning and
  sacrifice quality and coherence. In this work, we introduce ComposeAnything, a
  novel framework for improving compositional image generation without retraining
  existing T2I models. Our approach first leverages the chain-of-thought
  reasoning abilities of LLMs to produce 2.5D semantic layouts from text,
  consisting of 2D object bounding boxes enriched with depth information and
  detailed captions. Based on this layout, we generate a spatial and depth aware
  coarse composite of objects that captures the intended composition, serving as
  a strong and interpretable prior that replaces stochastic noise initialization
  in diffusion-based T2I models. This prior guides the denoising process through
  object prior reinforcement and spatial-controlled denoising, enabling seamless
  generation of compositional objects and coherent backgrounds, while allowing
  refinement of inaccurate priors. ComposeAnything outperforms state-of-the-art
  methods on the T2I-CompBench and NSR-1K benchmarks for prompts with 2D/3D
  spatial arrangements, high object counts, and surreal compositions. Human
  evaluations further demonstrate that our model generates high-quality images
  with compositions that faithfully reflect the text.
paper_ai_summary: ComposeAnything improves text-to-image generation by using LLMs for 2.5D semantic layouts, enhancing object placement and coherence in diffusion-based models.
paper_ai_keywords: LLMs chain-of-thought reasoning 2.5D semantic layouts object bounding boxes depth information spatial and depth aware coarse composite denoising process object prior reinforcement spatial-controlled denoising diffusion-based T2I models T2I-CompBench NSR-1K benchmarks