| | --- |
| | license: cc-by-4.0 |
| | language: |
| | - en |
| | size_categories: |
| | - 100K<n<1M |
| | pretty_name: Causal3D |
| | tags: |
| | - Causality |
| | - Computer_Vision |
| | dataset_info: |
| | - config_name: hypothetical_scenes_Hypothetic_v2_linear |
| | features: |
| | - name: image |
| | dtype: image |
| | - name: file_name |
| | dtype: string |
| | - name: metadata |
| | dtype: string |
| | splits: |
| | - name: train |
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| | - name: metadata |
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| | - name: train |
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| | num_examples: 10000 |
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| | - config_name: hypothetical_scenes_Hypothetic_v3_fully_connected_linear |
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| | - config_name: hypothetical_scenes_Hypothetic_v4_nonlinear_v |
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| | - name: image |
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| | - config_name: hypothetical_scenes_Hypothetic_v5_linear |
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| | - name: metadata |
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| | - config_name: hypothetical_scenes_Hypothetic_v5_linear_full_connected |
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| | - config_name: real_scenes_Real_Parabola |
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| | - name: image |
| | dtype: image |
| | - name: file_name |
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| | - name: metadata |
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| | - name: train |
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| | - config_name: real_scenes_Real_magnet_v3 |
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| | - name: metadata |
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| | - name: train |
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| | - config_name: real_scenes_Real_magnet_v3_5 |
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| | - name: image |
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| | - config_name: real_scenes_Real_spring_v3_256P |
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| | - config_name: real_scenes_Water_flow_scene_render |
| | features: |
| | - name: image |
| | dtype: image |
| | - name: file_name |
| | dtype: string |
| | - name: metadata |
| | dtype: string |
| | splits: |
| | - name: train |
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| | num_examples: 10000 |
| | download_size: 1813070 |
| | dataset_size: 3533718.0 |
| | - config_name: real_scenes_convex_len_render_images |
| | features: |
| | - name: image |
| | dtype: image |
| | - name: file_name |
| | dtype: string |
| | - name: metadata |
| | dtype: string |
| | splits: |
| | - name: train |
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| | num_examples: 1078 |
| | download_size: 106436 |
| | dataset_size: 161948.95 |
| | - config_name: real_scenes_real_pendulum |
| | features: |
| | - name: image |
| | dtype: image |
| | - name: file_name |
| | dtype: string |
| | - name: metadata |
| | dtype: string |
| | splits: |
| | - name: train |
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| | dataset_size: 2884667.13 |
| | - config_name: real_scenes_rendered_magnetic_128 |
| | features: |
| | - name: image |
| | dtype: image |
| | - name: file_name |
| | dtype: string |
| | - name: metadata |
| | dtype: string |
| | splits: |
| | - name: train |
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| | num_examples: 8350 |
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| | dataset_size: 2290040.5 |
| | - config_name: real_scenes_rendered_reflection_128P |
| | features: |
| | - name: image |
| | dtype: image |
| | - name: file_name |
| | dtype: string |
| | - name: metadata |
| | dtype: string |
| | splits: |
| | - name: train |
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| | num_examples: 9995 |
| | download_size: 1665779 |
| | dataset_size: 2723942.65 |
| | - config_name: real_scenes_seesaw_scene_128P |
| | features: |
| | - name: image |
| | dtype: image |
| | - name: file_name |
| | dtype: string |
| | - name: metadata |
| | dtype: string |
| | splits: |
| | - name: train |
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| | num_examples: 10000 |
| | download_size: 1257167 |
| | dataset_size: 2234514.0 |
| | - config_name: real_scenes_spring_scene_128P |
| | features: |
| | - name: image |
| | dtype: image |
| | - name: file_name |
| | dtype: string |
| | - name: metadata |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 2506086.0 |
| | num_examples: 10000 |
| | download_size: 951360 |
| | dataset_size: 2506086.0 |
| | configs: |
| | - config_name: hypothetical_scenes_Hypothetic_v2_linear |
| | data_files: |
| | - split: train |
| | path: hypothetical_scenes_Hypothetic_v2_linear/train-* |
| | - config_name: hypothetical_scenes_Hypothetic_v2_nonlinear |
| | data_files: |
| | - split: train |
| | path: hypothetical_scenes_Hypothetic_v2_nonlinear/train-* |
| | - config_name: hypothetical_scenes_Hypothetic_v3_fully_connected_linear |
| | data_files: |
| | - split: train |
| | path: hypothetical_scenes_Hypothetic_v3_fully_connected_linear/train-* |
| | - config_name: hypothetical_scenes_Hypothetic_v4_linear_full_connected |
| | data_files: |
| | - split: train |
| | path: hypothetical_scenes_Hypothetic_v4_linear_full_connected/train-* |
| | - config_name: hypothetical_scenes_Hypothetic_v4_linear_v |
| | data_files: |
| | - split: train |
| | path: hypothetical_scenes_Hypothetic_v4_linear_v/train-* |
| | - config_name: hypothetical_scenes_Hypothetic_v4_nonlinear_v |
| | data_files: |
| | - split: train |
| | path: hypothetical_scenes_Hypothetic_v4_nonlinear_v/train-* |
| | - config_name: hypothetical_scenes_Hypothetic_v5_linear |
| | data_files: |
| | - split: train |
| | path: hypothetical_scenes_Hypothetic_v5_linear/train-* |
| | - config_name: hypothetical_scenes_Hypothetic_v5_linear_full_connected |
| | data_files: |
| | - split: train |
| | path: hypothetical_scenes_Hypothetic_v5_linear_full_connected/train-* |
| | - config_name: hypothetical_scenes_rendered_h3_linear_128P |
| | data_files: |
| | - split: train |
| | path: hypothetical_scenes_rendered_h3_linear_128P/train-* |
| | - config_name: hypothetical_scenes_rendered_h3_nonlinear_128P |
| | data_files: |
| | - split: train |
| | path: hypothetical_scenes_rendered_h3_nonlinear_128P/train-* |
| | - config_name: hypothetical_scenes_rendered_h5_nonlinear |
| | data_files: |
| | - split: train |
| | path: hypothetical_scenes_rendered_h5_nonlinear/train-* |
| | - config_name: real_scenes_Real_Parabola |
| | data_files: |
| | - split: train |
| | path: real_scenes_Real_Parabola/train-* |
| | - config_name: real_scenes_Real_magnet_v3 |
| | data_files: |
| | - split: train |
| | path: real_scenes_Real_magnet_v3/train-* |
| | default: true |
| | - config_name: real_scenes_Real_magnet_v3_5 |
| | data_files: |
| | - split: train |
| | path: real_scenes_Real_magnet_v3_5/train-* |
| | - config_name: real_scenes_Real_spring_v3_256P |
| | data_files: |
| | - split: train |
| | path: real_scenes_Real_spring_v3_256P/train-* |
| | - config_name: real_scenes_Water_flow_scene_render |
| | data_files: |
| | - split: train |
| | path: real_scenes_Water_flow_scene_render/train-* |
| | - config_name: real_scenes_convex_len_render_images |
| | data_files: |
| | - split: train |
| | path: real_scenes_convex_len_render_images/train-* |
| | - config_name: real_scenes_real_pendulum |
| | data_files: |
| | - split: train |
| | path: real_scenes_real_pendulum/train-* |
| | - config_name: real_scenes_rendered_magnetic_128 |
| | data_files: |
| | - split: train |
| | path: real_scenes_rendered_magnetic_128/train-* |
| | - config_name: real_scenes_rendered_reflection_128P |
| | data_files: |
| | - split: train |
| | path: real_scenes_rendered_reflection_128P/train-* |
| | - config_name: real_scenes_seesaw_scene_128P |
| | data_files: |
| | - split: train |
| | path: real_scenes_seesaw_scene_128P/train-* |
| | - config_name: real_scenes_spring_scene_128P |
| | data_files: |
| | - split: train |
| | path: real_scenes_spring_scene_128P/train-* |
| | --- |
| | # 🧠 Causal3D: A Benchmark for Visual Causal Reasoning |
| |
|
| | **Causal3D** is a comprehensive benchmark designed to evaluate models’ abilities to uncover *latent causal relations* from structured and visual data. This dataset integrates **3D-rendered scenes** with **tabular causal annotations**, providing a unified testbed for advancing *causal discovery*, *causal representation learning*, and *causal reasoning* with **vision-language models (VLMs)** and **large language models (LLMs)**. |
| |
|
| | --- |
| |
|
| | ## 📌 Overview |
| |
|
| | While recent progress in AI and computer vision has been remarkable, there remains a major gap in evaluating causal reasoning over complex visual inputs. **Causal3D** bridges this gap by providing: |
| |
|
| | - **19 curated 3D-scene datasets** simulating diverse real-world causal phenomena. |
| | - Paired **tabular causal graphs** and **image observations** across multiple views and backgrounds. |
| | - Benchmarks for evaluating models in both **structured** (tabular) and **unstructured** (image) modalities. |
| |
|
| | --- |
| |
|
| | ## 🧩 Dataset Structure |
| |
|
| | Each sub-dataset (scene) contains: |
| |
|
| |
|
| | - `images/`: Rendered images under different camera views and backgrounds. |
| | - `tabular.csv`: Instance-level annotations including object attributes in causal graph. |
| |
|
| |
|
| | ## 🖼️ Visual Previews |
| |
|
| | Below are example images from different Causal3D scenes: |
| |
|
| | <table> |
| | <tr> |
| | <td align="center"> |
| | <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/parabola.png" width="250"/><br/>parabola |
| | </td> |
| | <td align="center"> |
| | <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/convex.png" width="250"/><br/>convex |
| | </td> |
| | </tr> |
| | <tr> |
| | <td align="center"> |
| | <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/magnetic.png" width="200"/><br/>magnetic |
| | </td> |
| | <td align="center"> |
| | <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/pendulum.png" width="200"/><br/>pendulum |
| | </td> |
| | <td align="center"> |
| | <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/reflection.png" width="200"/><br/>reflection |
| | </td> |
| | </tr> |
| | <tr> |
| | <td align="center"> |
| | <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/seesaw.png" width="200"/><br/>seesaw |
| | </td> |
| | <td align="center"> |
| | <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/spring.png" width="200"/><br/>spring |
| | </td> |
| | <td align="center"> |
| | <img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/water_flow.png" width="200"/><br/>water_flow |
| | </td> |
| | </tr> |
| | </table> |
| | |
| | <!-- - `causal_graph.json`: Ground-truth causal structure (as adjacency matrix or graph). |
| | - `view_info.json`: Camera/viewpoint metadata. |
| | - `split.json`: Recommended train/val/test splits for benchmarking. --> |
| |
|
| | --- |
| |
|
| | ## 🎯 Evaluation Tasks |
| |
|
| | Causal3D supports a range of causal reasoning tasks, including: |
| |
|
| | - **Causal discovery** from image sequences or tables |
| | - **Intervention prediction** under modified object states or backgrounds |
| | - **Counterfactual reasoning** across views |
| | - **VLM-based causal inference** given multimodal prompts |
| |
|
| | --- |
| |
|
| | ## 📊 Benchmark Results |
| |
|
| | We evaluate a diverse set of methods: |
| |
|
| | - **Classical causal discovery**: PC, GES, NOTEARS |
| | - **Causal representation learning**: CausalVAE, ICM-based encoders |
| | - **Vision-Language and Large Language Models**: GPT-4V, Claude-3.5, Gemini-1.5 |
| |
|
| | **Key Findings**: |
| |
|
| | - As causal structures grow more complex, **model performance drops significantly** without strong prior assumptions. |
| | - A noticeable performance gap exists between models trained on structured data and those applied directly to visual inputs. |
| |
|
| | --- |
| |
|
| |
|
| |
|
| | <!-- ## 🔍 Example Use Case |
| |
|
| | ```python |
| | from causal3d import load_scene_data |
| | |
| | scene = "SpringPendulum" |
| | data = load_scene_data(scene, split="train") |
| | images = data["images"] |
| | metadata = data["table"] |
| | graph = data["causal_graph"] --> |
