Research map / Multi-agent systems and games / Human-AI and goal-conditioned
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TDW-generated images instead of SceneNet images for pretraining | ResNet-50 visual representation · Fine-grained image classification (mean) | accuracy: 0.40 -> 0.76 | Helped |
| Use TDW-generated images instead of AI2-THOR images for pretraining | ResNet-50 visual representation · Fine-grained image classification (mean) | accuracy: 0.63 -> 0.76 | Helped |
| Train on TDW audio instead of Sound-20K audio | VGG-16 material classifier · Real-world material recognition | accuracy: 0.34 -> 0.66 | Helped |
| Add audio features to vision-only features | 2-layer MLP classifier · Material classification | accuracy: 0.72 -> 0.96 | Helped |
| Add audio features to vision-only features | 2-layer MLP classifier · Mass classification | accuracy: 0.42 -> 0.83 | Helped |
| Replace HRN with DRHRN | Physical dynamics prediction · Lift, Slide, Collide, Stack, and Cloth | global and local position MSE | Helped |
| Remove collision detection from HRN (ablation) | HRN physical dynamics predictor · Physical prediction | prediction accuracy | Hurt |
Closest papers by meaning
- 4DWorldBench: A Comprehensive Evaluation Framework for 3D/4D World Generation Models (CVPR 2026 · similarity 0.88)
- PhysGen3D: Crafting a Miniature Interactive World from a Single Image (CVPR 2025 · similarity 0.87)
- HoloScene: Simulation‑Ready Interactive 3D Worlds from a Single Video (NeurIPS 2025 · similarity 0.87)
- SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning (NeurIPS 2026 · similarity 0.87)
- Learning Interactive Real-World Simulators (ICLR 2024 · similarity 0.87)
- PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding (ICLR 2025 · similarity 0.86)
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