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Get Started Free →Generate publication-quality AI illustrations for academic papers using Gemini image generation. Creates architecture diagrams, method illustrations with Claude-supervised iterative refinement loop. Use when user says "生成图表", "画架构图", "AI绘图", "paper illustration", "generate diagram", or needs visual figures for papers.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | 414% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 118% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 285% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 304% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 257% | 0% |
Generate publication-quality illustrations using a multi-stage workflow with Claude as the STRICT supervisor/reviewer.
┌──────────────────────────────────────────────────────────────────────────┐
│ MULTI-STAGE ITERATIVE WORKFLOW │
├──────────────────────────────────────────────────────────────────────────┤
│ │
│ User Request │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Claude │ ◄─── Step 1: Parse request, create initial prompt │
│ │ (Planner) │ │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Gemini │ ◄─── Step 2: Optimize layout description │
│ │ (gemini-3-pro)│ - Refine component positioning │
│ │ Layout │ - Optimize spacing and grouping │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Gemini │ ◄─── Step 3: CVPR/NeurIPS style verification │
│ │ (gemini-3-pro)│ - Check color palette compliance │
│ │ Style │ - Verify arrow and font standards │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Paperbanana │ ◄─── Step 4: Render final image │
│ │ (gemini-3- │ - High-quality image generation │
│ │ pro-image) │ - Internal codename: Nano Banana Pro │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Claude │ ◄─── Step 5: STRICT visual review + SCORE (1-10) │
│ │ (Reviewer) │ - Verify EVERY arrow direction │
│ │ STRICT! │ - Verify EVERY block content │
│ └──────┬──────┘ - Verify aesthetics & visual appeal │
│ │ │
│ ▼ │
│ Score ≥ 9? ──YES──► Accept & Output │
│ │ │
│ NO │
│ │ │
│ ▼ │
│ Generate SPECIFIC improvement feedback ──► Loop back to Step 2 │
│ │
└──────────────────────────────────────────────────────────────────────────┘gemini-3-pro-image-preview — Paperbanana (Nano Banana Pro) for image renderinggemini-3-pro-preview — Gemini for layout optimization and style checkingfigures/ai_generated/ — Output directoryGEMINI_API_KEY — Environment variableWhat "CVPR Style" Actually Means:
目标:既不保守也不花哨,找到平衡点
| Figure Type | Quality | Examples | |-------------|---------|----------| | Architecture diagrams | Excellent | Model architecture, pipeline, encoder-decoder | | Method illustrations | Excellent | Conceptual diagrams, algorithm flowcharts | | Conceptual figures | Good | Comparison diagrams, taxonomy trees |
Not for: Statistical plots (use /paper-figure), photo-realistic images
bash# Check API key if [ -z "$GEMINI_API_KEY" ]; then echo "ERROR: GEMINI_API_KEY not set" echo "Get your key from: https://aistudio.google.com/app/apikey" echo "Set it: export GEMINI_API_KEY='your-key'" exit 1 fi # Create output directory mkdir -p figures/ai_generated
CRITICAL: Claude must first analyze the user's request and create a detailed prompt.
Parse the input: $ARGUMENTS
Claude's task:
Prompt Template for Claude to generate:
Create a PROFESSIONAL, VISUALLY APPEALING publication-quality academic diagram following CVPR/ICLR/NeurIPS standards.
## Visual Style: 科研风格 (Academic Professional Style)
### 目标:平衡 — 既不保守也不花哨
#### DO (应该有):
- **Subtle gradients** — 同色系淡雅渐变(如 #2563EB → #3B82F6),不是多色炫彩
- **Rounded corners** — 圆角矩形(6-10px),现代感
- **Clear visual hierarchy** — 通过大小、深浅区分层次
- **Internal structure** — 大模块内显示子组件结构
- **Consistent color coding** — 统一的3-4色方案
- **Professional polish** — 精致但不夸张
#### DON'T (不要有):
- ❌ Rainbow/multi-color gradients (彩虹渐变)
- ❌ Heavy drop shadows (重阴影)
- ❌ 3D effects / perspective (3D效果)
- ❌ Glowing effects (发光效果)
- ❌ Excessive decorative icons (过多装饰图标)
- ❌ Plain boring rectangles (完全平淡的方块)
#### 理想效果:
像顶会论文中精心设计的架构图 — 专业、清晰、有适度的视觉吸引力
## Figure Type
[Architecture Diagram / Pipeline / Comparison / etc.]
## Components to Include (BE SPECIFIC ABOUT CONTENT)
1. [Component 1]:
- Label: "[exact text]"
- Sub-label: "[smaller text below]"
- Position: [left/center/right, top/middle/bottom]
- Style: [border color, fill, internal structure]
2. [Component 2]: ...
## Layout
- Direction: [left-to-right / top-to-bottom]
- Spacing: [tight / normal / loose]
- Grouping: [how components should be grouped]
## Connections (BE EXPLICIT ABOUT DIRECTION)
EXACT arrow specifications:
1. [Component A] → [Component B]: Arrow goes FROM A TO B, label it "[data type]"
2. [Component C] → [Component D]: Arrow goes FROM C TO D, label it "[data type]"
...
VERIFY: Each arrow must point to the CORRECT target!
## Style Requirements (CVPR/ICLR/NeurIPS Standard)
### Visual Style
- Color palette: Professional academic colors
- Inputs: Green (#10B981)
- Encoders: Blue (#2563EB)
- Fusion modules: Purple (#7C3AED)
- Outputs: Orange (#EA580C)
- Font: Sans-serif (Arial/Helvetica), minimum 14pt, bold for labels
- Background: Clean white, no patterns
- Blocks: Rounded rectangles (8-12px radius), subtle gradient fill, colored border (2-3px)
- Subtle shadows for depth effect
- Print-friendly (must work in grayscale)
### CRITICAL: Arrow & Data Flow Requirements
1. **ALL arrows must be VERY THICK** - minimum 5-6px stroke width
2. **ALL arrows must have CLEAR arrowheads** - large, visible triangular heads
3. **ALL arrows must be BLACK or DARK GRAY** - not colored
4. **Label EVERY arrow** with what data flows through it
5. **VERIFY arrow direction** - each arrow MUST point to the correct target
6. **No ambiguous connections** - every arrow should have a clear source and destination
### Logic Clarity Requirements
1. **Data flow must be immediately obvious** - viewer should understand the pipeline in 5 seconds
2. **No crossing arrows** - reorganize layout to avoid arrow crossings
3. **Consistent direction** - maintain left-to-right or top-to-bottom flow throughout
4. **Group related components** - use subtle background boxes or spacing to group modules
5. **Clear hierarchy** - main components larger, sub-components smaller
## Additional Requirements
[Any specific requirements from user]Claude sends the initial prompt to Gemini (gemini-3-pro) for layout optimization.
bash#!/bin/bash # Step 2: Optimize layout using Gemini gemini-3-pro # This step refines component positioning and spacing set -e OUTPUT_DIR="figures/ai_generated" mkdir -p "$OUTPUT_DIR" API_KEY="${GEMINI_API_KEY}" URL="https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-preview:generateContent?key=$API_KEY" # The initial prompt from Claude INITIAL_PROMPT='[Claude fills in the detailed prompt here]' # Layout optimization request LAYOUT_REQUEST="You are an expert in academic figure layout design for CVPR/NeurIPS papers. Analyze this figure request and provide an OPTIMIZED LAYOUT DESCRIPTION: $INITIAL_PROMPT Provide: 1. **Optimized Component Positions**: Exact positions (left/center/right, top/middle/bottom) for each component 2. **Spacing Recommendations**: Specific spacing between components 3. **Grouping Strategy**: Which components should be visually grouped together 4. **Arrow Routing**: Optimal paths for arrows to avoid crossings 5. **Visual Hierarchy**: Size recommendations for main vs sub-components Output a DETAILED layout specification that will be used for rendering." # Build JSON payload python3 << PYTHON import json payload = { "contents": [{"parts": [{"text": '''$LAYOUT_REQUEST'''}]}] } with open("/tmp/gemini_layout_request.json", "w") as f: json.dump(payload, f, indent=2) print("Layout request created") PYTHON # Call Gemini gemini-3-pro-preview for layout optimization (DIRECT connection, no proxy) RESPONSE=$(curl -s --max-time 90 \ -X POST "$URL" \ -H 'Content-Type: application/json' \ -d @/tmp/gemini_layout_request.json) # Extract layout description LAYOUT_DESCRIPTION=$(echo "$RESPONSE" | python3 -c " import sys, json data = json.load(sys.stdin) try: print(data['candidates'][0]['content']['parts'][0]['text']) except: print('Error extracting layout') ") echo "=== Layout Optimization Complete ===" echo "$LAYOUT_DESCRIPTION" echo "$LAYOUT_DESCRIPTION" > "$OUTPUT_DIR/layout_description.txt"
Claude sends the optimized layout to Gemini for CVPR/NeurIPS style verification.
bash#!/bin/bash # Step 3: Verify and enhance style compliance using Gemini gemini-3-pro API_KEY="${GEMINI_API_KEY}" URL="https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-preview:generateContent?key=$API_KEY" # Read layout from previous step LAYOUT=$(cat figures/ai_generated/layout_description.txt) # Style verification request STYLE_REQUEST="You are a CVPR/NeurIPS paper figure reviewer specializing in visual standards. Review and ENHANCE this figure specification for top-tier conference compliance: $LAYOUT Ensure compliance with: 1. **Color Palette**: Use professional academic colors (green for inputs, blue for encoders, purple for fusion, orange for outputs) 2. **Arrow Standards**: Thick (5-6px), black/dark gray, clear arrowheads, all labeled 3. **Font Standards**: Sans-serif, minimum 14pt, readable in print 4. **Visual Appeal (科研风格)**: - ✅ Subtle same-color gradients, rounded corners (6-10px), internal structure visible - ❌ NO heavy shadows, NO glowing effects, NO rainbow gradients Output an ENHANCED figure specification with explicit style instructions for rendering." # Build JSON payload python3 << PYTHON import json payload = { "contents": [{"parts": [{"text": '''$STYLE_REQUEST'''}]}] } with open("/tmp/gemini_style_request.json", "w") as f: json.dump(payload, f, indent=2) print("Style request created") PYTHON # Call Gemini gemini-3-pro-preview for style verification (DIRECT connection, no proxy) RESPONSE=$(curl -s --max-time 90 \ -X POST "$URL" \ -H 'Content-Type: application/json' \ -d @/tmp/gemini_style_request.json) # Extract style-enhanced specification STYLE_SPEC=$(echo "$RESPONSE" | python3 -c " import sys, json data = json.load(sys.stdin) try: print(data['candidates'][0]['content']['parts'][0]['text']) except: print('Error extracting style spec') ") echo "=== Style Verification Complete ===" echo "$STYLE_SPEC" echo "$STYLE_SPEC" > "figures/ai_generated/style_spec.txt"
Claude sends the optimized, style-verified specification to Paperbanana for rendering.
bash#!/bin/bash # Step 4: Render image using Paperbanana (gemini-3-pro-image-preview) # Internal codename: Nano Banana Pro # Use DIRECT connection (no proxy) - proxy causes SSL errors set -e OUTPUT_DIR="figures/ai_generated" mkdir -p "$OUTPUT_DIR" API_KEY="${GEMINI_API_KEY}" URL="https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent?key=$API_KEY" # Read the style-enhanced specification from previous step STYLE_SPEC=$(cat figures/ai_generated/style_spec.txt) # Add rendering instructions RENDER_PROMPT="Render a publication-quality academic diagram based on this specification: $STYLE_SPEC RENDERING REQUIREMENTS: - Output a clean, professional diagram suitable for CVPR/NeurIPS submission - Use vector-quality rendering with sharp edges and clear text - Ensure all elements are properly aligned and spaced - The diagram should be immediately understandable at a glance" # Build JSON payload using Python for proper escaping python3 << PYTHON import json payload = { "contents": [{"parts": [{"text": '''$RENDER_PROMPT'''}]}], "generationConfig": {"responseModalities": ["TEXT", "IMAGE"]} } with open("/tmp/gemini_request.json", "w") as f: json.dump(payload, f, indent=2) print("JSON payload created") PYTHON # Call Paperbanana API WITHOUT proxy (direct connection works better) RESPONSE=$(curl -s --max-time 180 \ -X POST "$URL" \ -H 'Content-Type: application/json' \ -d @/tmp/gemini_request.json) # Check for error if echo "$RESPONSE" | grep -q '"error"'; then echo "API Error:" echo "$RESPONSE" | python3 -m json.tool 2>/dev/null || echo "$RESPONSE" exit 1 fi # Extract and save image echo "$RESPONSE" | python3 << 'PYTHON' import sys, json, base64 from pathlib import Path output_dir = Path("figures/ai_generated") data = json.load(sys.stdin) try: parts = data['candidates'][0]['content']['parts'] iteration = 1 # Claude increments this each iteration for part in parts: if 'text' in part: print(f"\n[Paperbanana]: {part['text'][:200]}...") elif 'inlineData' in part: img_data = base64.b64decode(part['inlineData']['data']) img_path = output_dir / f"figure_v{iteration}.png" with open(img_path, "wb") as f: f.write(img_data) print(f"\n✅ Image saved: {img_path}") print(f" Size: {len(img_data)/1024:.1f} KB") except Exception as e: print(f"Parse error: {e}") print(f"Raw response: {str(data)[:500]}") PYTHON
Claude MUST read the generated image and perform a STRICT review:
STRICT Review Template:
markdown## Claude's STRICT Review of Figure v{N} ### What I See [Describe the generated image in DETAIL - every block, every arrow] ### Strengths - [Strength 1] - [Strength 2] ### ═══════════════════════════════════════════════════════════════ ### STRICT VERIFICATION CHECKLIST (ALL must pass for score ≥ 9) ### ═══════════════════════════════════════════════════════════════ #### A. Arrow Correctness Verification (CRITICAL - any failure = score ≤ 6) Check EACH arrow: - [ ] Arrow 1: [Source] → [Target] — Does it point to the CORRECT target? - [ ] Arrow 2: [Source] → [Target] — Does it point to the CORRECT target? - [ ] Arrow 3: [Source] → [Target] — Does it point to the CORRECT target? - [ ] Arrow 4: [Source] → [Target] — Does it point to the CORRECT target? - [ ] Arrow 5: [Source] → [Target] — Does it point to the CORRECT target? - [ ] Arrow 6: [Source] → [Target] — Does it point to the CORRECT target? #### B. Block Content Verification (any failure = score ≤ 7) Check EACH block: - [ ] Block 1 "[Name]": Has correct label? Has sub-label? Content correct? - [ ] Block 2 "[Name]": Has correct label? Has sub-label? Content correct? - [ ] Block 3 "[Name]": Has correct label? Has sub-label? Content correct? - [ ] Block 4 "[Name]": Has correct label? Has sub-label? Content correct? - [ ] Block 5 "[Name]": Has correct label? Has sub-label? Content correct? - [ ] Block 6 "[Name]": Has correct label? Has sub-label? Content correct? - [ ] Block 7 "[Name]": Has correct label? Has sub-label? Content correct? #### C. Arrow Visibility (any failure = score ≤ 7) - [ ] ALL arrows are THICK (≥5px visible stroke) - [ ] ALL arrows have CLEAR arrowheads (large triangular heads) - [ ] ALL arrows are BLACK or DARK GRAY (not light colors) - [ ] NO arrows are too thin or invisible #### D. Arrow Labels (any failure = score ≤ 7) - [ ] EVERY arrow has a text label - [ ] Labels are readable (not too small) - [ ] Labels correctly describe the data flowing #### E. Visual Appeal (科研风格 - Balanced Academic Style) (any failure = score ≤ 8) - [ ] **有适度视觉吸引力** — 有subtle渐变或圆角,但不夸张 - [ ] **不是平淡方块** — 有一定设计感 - [ ] **不过度装饰** — 没有重阴影、发光效果、彩虹配色 - [ ] **专业学术风格** — 像CVPR论文中的图表,不是PPT模板 - [ ] **Internal structure visible** — 大模块内部显示子组件结构 - [ ] **Color palette: 3-4种协调色** — 不是彩虹,也不是纯黑白 #### E2. Visual Appeal - RED FLAGS (immediate score ≤ 7 if found) - [ ] **NO heavy drop shadows** (重阴影 = too flashy) - [ ] **NO glowing effects** (发光效果 = too flashy) - [ ] **NO rainbow gradients** (彩虹渐变 = unprofessional) - [ ] **NO excessive decorative icons** (过多装饰图标 = distracting) #### F. Layout & Flow (any failure = score ≤ 7) - [ ] Clean horizontal left-to-right flow - [ ] No arrow crossings - [ ] Data flow traceable in 5 seconds - [ ] Balanced spacing (not cramped, not sparse) #### G. Style Compliance - [ ] CVPR/NeurIPS professional style - [ ] Color palette appropriate (not rainbow) - [ ] Font readable - [ ] Print-friendly (grayscale test) ### ═══════════════════════════════════════════════════════════════ ### Issues Found (BE SPECIFIC) 1. [Issue 1]: [EXACTLY what is wrong] → [How to fix] 2. [Issue 2]: [EXACTLY what is wrong] → [How to fix] 3. [Issue 3]: [EXACTLY what is wrong] → [How to fix] ### Score: X/10 ### STRICT Score Breakdown Guide: - **10**: Perfect. No issues. Publication-ready masterpiece. 视觉风格完美平衡。 - **9**: Excellent. Minor issues that don't affect understanding. 可以直接使用。 - **8**: Good but has noticeable issues. 视觉上太平淡或太花哨都需要改进。 - **7**: Usable but has clear problems. 箭头或内容有问题。 - **6**: Has arrow direction errors (箭头指向错误) OR missing major components. - **1-5**: Major issues. Unacceptable. ### Visual Style Scoring (视觉风格评分): - **太花哨 (Too flashy)**: 重阴影、发光效果、彩虹配色 → score ≤ 7 - **太平淡 (Too plain)**: 纯黑白方块、无任何视觉设计 → score ≤ 8 - **恰到好处 (Balanced)**: 适度渐变、圆角、清晰层次 → score 9-10 ### Verdict [ ] ACCEPT (score ≥ 9 AND all critical checks pass) [ ] REFINE (score < 9 OR any critical check fails) **If REFINE: List the EXACT issues that must be fixed**
IF score >= 9 AND all critical checks pass:
→ Accept figure, generate LaTeX snippet, DONE
ELSE IF iteration < MAX_ITERATIONS:
→ Generate SPECIFIC improvement prompt based on EXACT issues
→ Go to Step 2 (Gemini Layout) with refined prompt
ELSE:
→ Max iterations reached, show best version
→ Ask user if they want to continue or acceptClaude generates TARGETED improvement prompt with EXACT issues:
Refine this academic diagram. This is iteration {N}.
## ═══════════════════════════════════════════════════════════════
## CRITICAL: Fix These EXACT Issues (from previous review)
## ═══════════════════════════════════════════════════════════════
### Arrow Direction Errors (MUST FIX):
1. [EXACT issue]: Arrow from [A] to [B] is pointing to wrong target. It should point to [C] instead.
2. [EXACT issue]: ...
### Missing Arrow Labels (MUST FIX):
1. Arrow from [A] to [B] is missing label "[data type]"
2. ...
### Block Content Issues (MUST FIX):
1. Block "[Name]" has wrong label. Should be "[correct label]"
2. ...
### Visual Appeal Issues (SHOULD FIX):
1. Blocks are too plain. Add [gradients/shadows/internal structure]
2. ...
## Keep These Good Elements:
- [What to preserve from previous version]
## Generate the improved figure with ALL issues fixed.When figure is accepted (score ≥ 9):
latex% === AI-Generated Figure === \begin{figure*}[t] \centering \includegraphics[width=0.95\textwidth]{figures/ai_generated/figure_final.png} \caption{[Caption based on user's original request].} \label{fig:[label]} \end{figure*}
figures/ai_generated/
├── layout_description.txt # Step 2: Gemini layout optimization output
├── style_spec.txt # Step 3: Gemini style verification output
├── figure_v1.png # Iteration 1 (Paperbanana render)
├── figure_v2.png # Iteration 2
├── figure_v3.png # Iteration 3
├── figure_final.png # Accepted version (copy of best, score ≥ 9)
├── latex_include.tex # LaTeX snippet
└── review_log.json # All review scores and STRICT feedback| Stage | Model | Purpose | |-------|-------|---------| | Step 1 | Claude | Parse request, create initial prompt | | Step 2 | gemini-3-pro | Layout optimization (positioning, spacing, grouping) | | Step 3 | gemini-3-pro | CVPR/NeurIPS style verification | | Step 4 | gemini-3-pro-image-preview (Paperbanana) | High-quality image rendering | | Step 5 | Claude | STRICT visual review and scoring |
Other measured skills in the registry, with their headline benchmark lift.