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--- |
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license: cc-by-4.0 |
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language: |
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- en |
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size_categories: |
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- 100K<n<1M |
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pretty_name: Causal3D |
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tags: |
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- Causality |
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- Computer_Vision |
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dataset_info: |
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- config_name: hypothetical_scenes_Hypothetic_v2_linear |
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- config_name: real_scenes_Real_parabola_multi_view |
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- config_name: real_scenes_Real_spring_v3_256P |
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- config_name: real_scenes_Water_flow_scene_render |
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--- |
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# 🧠 Causal3D: A Benchmark for Visual Causal Reasoning |
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**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)**. |
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## 📚 Usage |
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#### 🔹 Option 1: Load from Hugging Face |
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You can easily load a specific scene using the Hugging Face `datasets` library: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset( |
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"LLDDSS/Causal3D", |
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name="real_scenes_Real_Parabola", |
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download_mode="force_redownload", # Optional: force re-download |
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trust_remote_code=True # Required for custom dataset loading |
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) |
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print(dataset) |
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``` |
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#### 🔹 Option 2: Download via [**Kaggle**](https://www.kaggle.com/datasets/dsliu0011/causal3d-image-dataset) + Croissant |
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``` |
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import mlcroissant as mlc |
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import pandas as pd |
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# Load the dataset metadata from Kaggle |
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croissant_dataset = mlc.Dataset( |
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"https://www.kaggle.com/datasets/dsliu0011/causal3d-image-dataset/croissant/download" |
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) |
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# List available record sets |
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record_sets = croissant_dataset.metadata.record_sets |
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print(record_sets) |
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# Load records from the first record set |
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df = pd.DataFrame(croissant_dataset.records(record_set=record_sets[0].uuid)) |
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print(df.head()) |
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``` |
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--- |
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## 📌 Overview |
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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: |
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- **19 curated 3D-scene datasets** simulating diverse real-world causal phenomena. |
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- Paired **tabular causal graphs** and **image observations** across multiple views and backgrounds. |
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- Benchmarks for evaluating models in both **structured** (tabular) and **unstructured** (image) modalities. |
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--- |
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## 🧩 Dataset Structure |
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Each sub-dataset (scene) contains: |
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- `images/`: Rendered images under different camera views and backgrounds. |
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- `tabular.csv`: Instance-level annotations including object attributes in causal graph. |
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## 🖼️ Visual Previews |
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Below are example images from different Causal3D scenes: |
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<table> |
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<tr> |
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<td align="center"> |
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<img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/parabola.png" width="250"/><br/>parabola |
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</td> |
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<td align="center"> |
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<img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/convex.png" width="250"/><br/>convex |
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</td> |
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</tr> |
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<tr> |
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<td align="center"> |
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<img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/magnetic.png" width="200"/><br/>magnetic |
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</td> |
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<td align="center"> |
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<img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/pendulum.png" width="200"/><br/>pendulum |
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</td> |
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<td align="center"> |
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<img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/reflection.png" width="200"/><br/>reflection |
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</td> |
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</tr> |
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<tr> |
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<td align="center"> |
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<img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/seesaw.png" width="200"/><br/>seesaw |
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</td> |
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<td align="center"> |
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<img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/spring.png" width="200"/><br/>spring |
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</td> |
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<td align="center"> |
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<img src="https://huggingface.co/datasets/LLDDSS/Causal3D/resolve/main/preview/water_flow.png" width="200"/><br/>water_flow |
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</td> |
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</tr> |
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</table> |
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<!-- - `causal_graph.json`: Ground-truth causal structure (as adjacency matrix or graph). |
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- `view_info.json`: Camera/viewpoint metadata. |
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- `split.json`: Recommended train/val/test splits for benchmarking. --> |
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--- |
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## 🎯 Evaluation Tasks |
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Causal3D supports a range of causal reasoning tasks, including: |
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- **Causal discovery** from image sequences or tables |
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- **Intervention prediction** under modified object states or backgrounds |
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- **Counterfactual reasoning** across views |
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- **VLM-based causal inference** given multimodal prompts |
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--- |
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## 📊 Benchmark Results |
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We evaluate a diverse set of methods: |
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- **Classical causal discovery**: PC, GES, NOTEARS |
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- **Causal representation learning**: CausalVAE, ICM-based encoders |
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- **Vision-Language and Large Language Models**: GPT-4V, Claude-3.5, Gemini-1.5 |
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**Key Findings**: |
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- As causal structures grow more complex, **model performance drops significantly** without strong prior assumptions. |
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- A noticeable performance gap exists between models trained on structured data and those applied directly to visual inputs. |
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--- |
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<!-- ## 🔍 Example Use Case |
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```python |
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from causal3d import load_scene_data |
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scene = "SpringPendulum" |
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data = load_scene_data(scene, split="train") |
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images = data["images"] |
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metadata = data["table"] |
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graph = data["causal_graph"] --> |
