What Happens if ...

By L. Brazil, J. Curtis, Rachel Grayer, R. Henry. 2011

21 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation (2018, background): “Prediction has been an active area of research in computer vision, including prediction of future images [9, 10, 11], motion [12], and even physical events [13, 14, 15].”
Unsupervised Learning for Physical Interaction through Video Prediction (2016, background): “Learning physics: Several works have explicitly addressed prediction of physical interactions, including predicting ball motion [4], block falling [1], the effects of forces [16, 15], future human interactions [7], and future car trajectories [21].”
Learning to Perform Physics Experiments via Deep Reinforcement Learning (2016, neutral): “This is the approach taken by Mottaghi et al. (2016), who generate an “interactable” data set of scenes, which they use to generate a static data set of image and force pairs, along with the ground truth trajectory of a target object in response to the application of the indicated force.”
SE3-nets: Learning rigid body motion using deep neural networks (2016, methodology): “Physics prediction: Our model is related to recent work in deep learning for physics based prediction, such as predicting the stability of a tower of blocks from images [19, 20], predicting the motion of a billiards ball under forces [10] and predicting the dynamics of objects in images [23, 24].”
IntPhys: A Benchmark for Visual Intuitive Physics Reasoning (2019, background): “…which are not strictly speaking about intuitive physics, one can distinguish the Visual Newtonian Dynamics (VIND) dataset which includes more than 6000 videos with bounding boxes on key objects across frames, and annotated with a 3D plane which woud most closely ﬁt the object trajectory [31].”
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation (2020, background): “Taken together, TDW is substantially more full-featured for supporting future development in rapidly-expanding research areas such as learning scene dynamics for physical reasoning [52, 51] and model-predictive planning and control [4, 32, 16, 5, 10, 2, 39, 15, 35].”
Interaction Networks for Learning about Objects, Relations and Physics (2016, background): “Mottaghi et al. [21] trained CNNs to predict the 3D trajectory of an object after an external impulse is applied.”