[Paper] PosterCopilot: Toward Layout Reasoning and Controllable Editing for Professional Graphic Design

Source: arXiv - 2512.04082v1

Overview

PosterCopilot tackles a long‑standing pain point for designers: turning high‑level ideas into pixel‑perfect, aesthetically balanced graphics without tedious manual tweaking. By marrying large multimodal models (LMMs) with a novel training pipeline and a layer‑aware editing workflow, the authors deliver a system that can reason about layout geometry, respect visual realism, and respond to iterative, element‑specific edits—capabilities that bring AI‑assisted design a step closer to professional studio tools.

Key Contributions

• Three‑stage progressive training that endows an LMM with (1) geometric precision, (2) visual‑reality alignment, and (3) aesthetic judgment.
• Perturbed Supervised Fine‑Tuning (PSFT): introduces controlled layout noise during supervised learning to teach the model to recover accurate positions.
• Reinforcement Learning for Visual‑Reality Alignment (RL‑VRA): uses a realism discriminator to reward layouts that look plausible when rendered.
• Reinforcement Learning from Aesthetic Feedback (RL‑AF): incorporates a learned aesthetic scorer to steer designs toward higher visual quality.
• Layer‑controllable, iterative editing workflow that couples the trained LMM with generative diffusion models, enabling precise modifications of individual design elements while preserving overall composition.
• Comprehensive evaluation showing superior geometric accuracy and aesthetic scores compared with prior LMM‑based design assistants.

Methodology

1. Base Model – The authors start with a pre‑trained large multimodal transformer (e.g., CLIP‑based) that can ingest textual prompts and visual context.
2. Stage 1: Perturbed Supervised Fine‑Tuning
   • Training data: pairs of design briefs and ground‑truth poster layouts.
   • Random perturbations (shifts, scaling, rotation) are applied to element coordinates before feeding them to the model.
   • The loss penalizes deviation from the original layout, teaching the model to “undo” noise and thus learn robust geometric reasoning.
3. Stage 2: RL‑VRA
   • A realism discriminator (trained on real vs. synthetic renderings) provides a reward signal.
   • The LMM generates candidate layouts; the discriminator scores how realistic the rendered composition looks; policy gradients update the LMM to maximize this reward.
4. Stage 3: RL‑AF
   • An aesthetic predictor (trained on human‑rated designs) supplies a second reward.
   • The model is fine‑tuned to increase aesthetic scores while still satisfying realism constraints.
5. Iterative Editing Pipeline
   • The trained LMM proposes a full‑poster layout given a prompt.
   • Designers can select any layer (e.g., a logo, text block) and issue a follow‑up instruction (“move logo 20 px right”).
   • The system re‑generates only the targeted layer via a diffusion model, then re‑assembles the poster, preserving global alignment thanks to the LMM’s layout backbone.

Results & Findings

• Geometric Accuracy: PosterCopilot reduced average element‑position error by ~38 % relative to baseline LMM assistants, measured against expert‑crafted ground truth.
• Aesthetic Quality: In a blind user study (N = 120), designs from PosterCopilot received higher mean aesthetic ratings (4.3/5) than competing methods (3.6/5).
• Controllability: The layer‑specific editing interface achieved a 92 % success rate for precise user commands (e.g., “resize subtitle to 24 pt”) while maintaining overall visual coherence.
• Efficiency: End‑to‑end generation + one round of editing averaged 3.2 seconds per poster on a single RTX 4090, comparable to manual layout tools for simple compositions.

Practical Implications

• Rapid Prototyping: Marketing teams can generate near‑final poster drafts from a brief and then fine‑tune individual elements without re‑creating the whole design.
• Design System Integration: Because the workflow respects layer boundaries, PosterCopilot can be plugged into existing design platforms (Figma, Adobe XD) as a “smart assistant” that suggests layout adjustments or auto‑fills placeholders.
• Localization & A/B Testing: Brands can automatically re‑position or resize elements for different languages or market variants while guaranteeing that the overall aesthetic stays on brand.
• Education & Onboarding: Junior designers can experiment with AI‑driven suggestions, learning layout principles through the model’s feedback loop.

Limitations & Future Work

• Domain Scope: The training data focuses on poster‑style graphics; performance on complex UI mockups or multi‑page layouts remains untested.
• Aesthetic Subjectivity: The aesthetic scorer, while effective, reflects the preferences of the training crowd and may not capture niche brand identities without further fine‑tuning.
• Real‑World Rendering Gaps: The realism discriminator works on rasterized previews; subtle print‑specific issues (color gamut, bleed) are not yet modeled.
• Future Directions: Extending the pipeline to multi‑modal outputs (e.g., animated ads), incorporating user‑specific style embeddings, and tightening the loop with high‑fidelity print simulation are highlighted as next steps.

Authors

• Jiazhe Wei
• Ken Li
• Tianyu Lao
• Haofan Wang
• Liang Wang
• Caifeng Shan
• Chenyang Si

Paper Information

• arXiv ID: 2512.04082v1
• Categories: cs.CV
• Published: December 3, 2025
• PDF: Download PDF