# To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction

arXiv: https://arxiv.org/abs/1604.00066 · HTML: https://mlsanity.com/paper/1604.00066/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2016.

## Built on by (most cited first, 10 of 10)

- [Physion: Evaluating Physical Prediction from Vision in Humans and Machines](https://mlsanity.com/paper/2106.08261/) (2021)
- [O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning](https://mlsanity.com/paper/2106.15087/) (2021)
- [A Survey on Machine Learning Approaches for Modelling Intuitive Physics](https://mlsanity.com/paper/2202.06481/) (2022)
- [Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes](https://mlsanity.com/paper/2305.11772/) (2023)
- [3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes](https://mlsanity.com/paper/2304.11470/) (2023)
- [IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes](https://mlsanity.com/paper/2112.05298/) (2021)
- [PIP: Physical Interaction Prediction via Mental Simulation with Span Selection](https://mlsanity.com/paper/2109.04683/) (2021)
- [Object and Relation Centric Representations for Push Effect Prediction](https://mlsanity.com/paper/2102.02100/) (2021)
- [6-DoF Stability Field via Diffusion Models](https://mlsanity.com/paper/2310.17649/) (2023)
- [PhysicsGen: Can Generative Models Learn from Images to Predict Complex Physical Relations?](https://mlsanity.com/paper/2503.05333/) (2025)

## Abstract (arXiv)

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel object and their configurations. Developmental psychology has shown that such skills are acquired by infants from observations at a very early stage. In this paper, we contrast a more traditional approach of taking a model-based route with explicit 3D representations and physical simulation by an end-to-end approach that directly predicts stability and related quantities from appearance. We ask the question if and to what extent and quality such a skill can directly be acquired in a data-driven way bypassing the need for an explicit simulation. We present a learning-based approach based on simulated data that predicts stability of towers comprised of wooden blocks under different conditions and quantities related to the potential fall of the towers. The evaluation is carried out on synthetic data and compared to human judgments on the same stimuli.