--- language: - en license: cc-by-4.0 pretty_name: BlenderBench tags: - 3d - blender - code-generation - vision - multimodal - benchmark dataset_info: features: - name: instance_id dtype: string - name: task_description dtype: string - name: start_render dtype: image - name: goal_render dtype: image - name: start_code dtype: string - name: goal_code dtype: string - name: blend_file_path dtype: string - name: blend_file_size_mb dtype: float64 splits: - name: train num_bytes: 18000000000 num_examples: 27 download_size: 18000000000 dataset_size: 18000000000 --- # BlenderBench Dataset

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## Dataset Description **BlenderBench** is a comprehensive benchmark dataset for evaluating models on 3D scene editing tasks in Blender. The dataset challenges agents to understand visual differences between initial and target scenes, then generate appropriate Blender Python code to transform the initial scene to match the target. ### Key Features - **27 instances** across 3 difficulty levels - **Multi-modal**: Combines visual understanding with code generation - **Realistic 3D scenes**: Using Blender Studio assets - **Progressive difficulty**: From simple camera adjustments to complex scene manipulations - **Rich annotations**: Each instance includes task descriptions, start/goal code, and rendered images ### Task Categories The dataset covers three main difficulty levels: 1. **Level 1** (9 instances): Camera adjustments 2. **Level 2** (9 instances): Multi-step editing 3. **Level 3** (9 instances): Compositional editing (level 1 + level 2) ## Dataset Structure Each instance in the dataset contains: ``` instance/ ├── blender_file.blend # Blender scene file ├── start.py # Initial scene configuration code ├── goal.py # Target scene configuration code ├── task.txt # Natural language task description └── renders/ ├── start/ │ └── render1.png # Rendered image of initial scene (512x512) └── goal/ └── render1.png # Rendered image of target scene (512x512) ``` ### Data Fields When loaded using `datasets.load_dataset()`, each example contains: - `instance_id` (string): Unique identifier (e.g., "level1/camera1") - `level` (string): Difficulty level ("level1", "level2", or "level3") - `instance_name` (string): Instance name within the level - `task_description` (string): Natural language description of the task - `start_code` (string): Python code for the initial scene setup - `goal_code` (string): Python code for the target scene configuration - `start_render` (image): Rendered image of the initial scene (512x512 PNG) - `goal_render` (image): Rendered image of the target scene (512x512 PNG) - `blend_file_path` (string): Relative path to the .blend file - `blend_file_size_mb` (float): Size of the .blend file in MB ### Data Splits The dataset provides a single training split containing all instances. You can filter by difficulty level using the dataset configurations: - `all`: All instances (default) - `level1`: Only level 1 instances - `level2`: Only level 2 instances - `level3`: Only level 3 instances ## Usage ### Installation ```bash pip install datasets huggingface_hub ``` ### Loading the Dataset ```python from datasets import load_dataset # Load all instances dataset = load_dataset("DietCoke4671/BlenderBench") # Load only level 1 instances dataset = load_dataset("DietCoke4671/BlenderBench", "level1") # Access an example example = dataset["train"][0] print(f"Task: {example['task_description']}") print(f"Level: {example['level']}") # Display images example['start_render'].show() # Initial scene example['goal_render'].show() # Target scene # Access code print(f"Start code:\n{example['start_code']}") print(f"Goal code:\n{example['goal_code']}") ``` ### Using with Blender To actually render and evaluate generated code, you'll need Blender installed: ```python import subprocess from pathlib import Path def render_with_blender(blend_file, code, output_dir): """ Execute Blender code and render the result. Args: blend_file: Path to .blend file code: Python code to execute in Blender output_dir: Directory to save rendered output """ # Save code to temporary file code_file = Path(output_dir) / "temp_code.py" with open(code_file, 'w') as f: f.write(code) # Run Blender cmd = [ "blender", "--background", str(blend_file), "--python", str(code_file), "--render-output", str(output_dir / "render.png"), "--render-frame", "1" ] subprocess.run(cmd) # Example usage example = dataset["train"][0] render_with_blender( blend_file=f"path/to/{example['blend_file_path']}", code=example['start_code'], output_dir="output/" ) ``` ### Example: Building an AI Agent ```python from datasets import load_dataset import openai # Load dataset dataset = load_dataset("DietCoke4671/BlenderBench", "level1") def solve_blender_task(example): """ Use an AI agent to solve a BlenderBench task. """ # Prepare prompt with task description and images prompt = f""" You are an expert Blender Python programmer. Your task is to: Task: {example['task_description']} Given the initial scene code and rendered image, generate Blender Python code to transform the scene to match the target image. Initial code: {example['start_code']} Generate the modified code that will produce the target scene. """ # Call your AI model (e.g., GPT-4 with vision) response = openai.ChatCompletion.create( model="gpt-4-vision-preview", messages=[ { "role": "user", "content": [ {"type": "text", "text": prompt}, { "type": "image_url", "image_url": {"url": encode_image(example['start_render'])} }, { "type": "image_url", "image_url": {"url": encode_image(example['goal_render'])} } ] } ] ) return response.choices[0].message.content # Solve all level 1 tasks for example in dataset["train"]: solution = solve_blender_task(example) print(f"Solution for {example['instance_id']}:") print(solution) ```