arxiv_cv 90% Match Research Paper AI researchers in generative models,Developers of text-to-image systems,Artists and designers using AI tools 2 weeks ago

DOS: Directional Object Separation in Text Embeddings for Multi-Object Image Generation

generative-ai › diffusion-models

📄 Abstract

Abstract: Recent progress in text-to-image (T2I) generative models has led to significant improvements in generating high-quality images aligned with text prompts. However, these models still struggle with prompts involving multiple objects, often resulting in object neglect or object mixing. Through extensive studies, we identify four problematic scenarios, Similar Shapes, Similar Textures, Dissimilar Background Biases, and Many Objects, where inter-object relationships frequently lead to such failures. Motivated by two key observations about CLIP embeddings, we propose DOS (Directional Object Separation), a method that modifies three types of CLIP text embeddings before passing them into text-to-image models. Experimental results show that DOS consistently improves the success rate of multi-object image generation and reduces object mixing. In human evaluations, DOS significantly outperforms four competing methods, receiving 26.24%-43.04% more votes across four benchmarks. These results highlight DOS as a practical and effective solution for improving multi-object image generation.

Authors (5)

Dongnam Byun

Jungwon Park

Jumgmin Ko

Changin Choi

Wonjong Rhee

Submitted

October 16, 2025

arXiv Category

cs.CV

arXiv PDF

Key Contributions

DOS (Directional Object Separation) is proposed to improve multi-object image generation by modifying CLIP text embeddings before they are fed into text-to-image models. It addresses common failures like object neglect and mixing by better separating directional cues for individual objects, significantly improving generation success rates.

Business Value

Enables the creation of more accurate and controllable visual content from text descriptions, valuable for graphic design, advertising, and personalized content generation.

Paper Metadata

Innovation Type

Prompt/Embedding Engineering

Deployment Feasibility

High. It's a modification applied to existing text-to-image pipelines, requiring minimal additional infrastructure beyond the base model.

Limitations Addressed

Struggles of text-to-image models with prompts involving multiple objects,Object neglect and object mixing issues,Difficulty in capturing complex inter-object relationships

Performance Gains

Consistently improves multi-object image generation success rate and reduces object mixing. Receives 26.24%-43.04% more votes in human evaluations compared to four competing methods.

Technical Tags

text-to-image generationmulti-object generationCLIP embeddingsdirectional object separation (DOS)object neglectobject mixingprompt engineeringgenerative modelsdiffusion modelsinter-object relationships

Research Topics

Generative AIText-to-Image SynthesisComputer VisionNatural Language ProcessingDeep Learning

Methods & Architectures

Directional Object Separation (DOS)CLIP text embedding modificationDiffusion ModelsGenerative Adversarial Networks (GANs) Diffusion ModelsCLIP

Applications & Tasks

Content Creation Art Generation Design Marketing Generating images with multiple objects accuratelyPreventing object neglect and mixingImproving adherence to complex text promptsHandling inter-object relationships Text-to-Image GenerationMulti-Object Image Synthesis

Datasets & Benchmarks

Benchmarks

Human evaluation across four benchmarks

Success rate of multi-object generationReduction in object mixingHuman preference scores

Related Fields

Generative AIComputer VisionNatural Language ProcessingDeep Learning

Keywords

text-to-imagegenerative modelsdiffusion modelsmulti-object generationCLIPembeddingsobject neglectobject mixingprompt engineeringAI artimage synthesisDOS

Academic Context

#Generative AI#Text-to-Image Synthesis#Computer Vision#Natural Language Processing#Deep Learning

Technology Stack

Frameworks & Libraries

PyTorchTensorFlow

Programming Languages

Python

ML Infrastructure

GPU computing

Commercial Potential

Potential Products

Improved text-to-image generation APIsPlugins for creative softwareSpecialized content generation tools

Target Industries

MediaAdvertisingGamingDesignTechnology

Use Case Examples

Generating an image of 'a red ball next to a blue cube on a green table'Creating scenes with multiple characters and specific interactionsGenerating marketing visuals with precise object placement

Competitive Edge

Offers a significant improvement in handling complex multi-object prompts for text-to-image models, addressing a key limitation of current state-of-the-art systems.

Market Opportunity

Massive and rapidly growing market for AI-powered image generation.

Revenue Models

API accesslicensing of the technique.

Resource Requirements

Compute Needs

Requires access to pre-trained text-to-image models and CLIP; training/fine-tuning might require GPUs.

Data Requirements

Large-scale image-text datasets for training/fine-tuning generative models.

Deployment Constraints

Computational cost of the underlying text-to-image model,Effectiveness might vary depending on the specific T2I architecture

Scalability

Scalability depends on the underlying text-to-image model.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years

Patent Potential

Moderate, related to the DOS technique for modifying embeddings.

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