Research map / 3D vision / 3D generation
Inferring Forces and Learning Human Utilities From Videos
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use physical quantities φp(G) alone, without spatial or temporal features (ablation) | Human utility ranking for sitting affordance · Static 3D scenes | pose naturalness | Mixed |
| Add spatial relations φs(G) to physical quantities φp(G) (ablation) | Human utility ranking for sitting affordance · Static 3D scenes | pose naturalness | Helped |
| Add temporal plan-cost features φt(G) to physical and spatial features (ablation) | Human utility ranking for sitting affordance · Static 3D scenes | pose naturalness | Helped |
Closest papers by meaning
- PhysPT: Physics-aware Pretrained Transformer for Estimating Human Dynamics from Monocular Videos (CVPR 2024 · similarity 0.86)
- PhyMotion: Structured 3D Motion Reward for Physics-Grounded Human Video Generation (NeurIPS 2026 · similarity 0.86)
- VidBot: Learning Generalizable 3D Actions from In-the-Wild 2D Human Videos for Zero-Shot Robotic Manipulation (CVPR 2025 · similarity 0.85)
- Vision-Guided Action: Enhancing 3D Human Motion Prediction with Gaze-informed Affordance in 3D Scenes (CVPR 2025 · similarity 0.85)
- InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactions (CVPR 2025 · similarity 0.85)
- IAAO: Interactive Affordance Learning for Articulated Objects in 3D Environments (CVPR 2025 · similarity 0.85)
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