![]() The figure below shows how the condition random field combine different shots and person tracking to improve the result on top of the neural network. It combines visual information (image, optical flow, and object detection) and acoustic information (audio) to make the prediction. The model to the right is our neural architecture for obtaining all the unaires. ![]() We proposed a 4-stream neural architecture for obtaining the unaries, and a Condition Random Field to exploit the relationship between shots. This clip doesn't contain scenes that do not have as many effects as in the previous example.Īnd we can still see that the model can properly ignore the parts where human don't perceive as a strong effect. Here is another clip taken from the movie Iron Man III. We can see that the 2 proposed methods make good qualitative prediction. It is a really difficult task given the clip contains multiple effects happening to different characters. The task is to detect the effects happening to the characters in the frame. GT is the label made by human annotators U is the prediction made by neural network CRF is the result from Conditional Random Field. Here is one example clip taken from the movie Thor II: The Dark World. Your browser does not support the video tag. Now You Shake Me: Towards Automatic 4D Cinema Yuhao Zhou, Makarand Tapaswi, and Sanja Fidler We propose effect detection and classification as two tasks, and present results along with ablation studies on our dataset, paving the way towards 4D cinema in everyone's homes. Our model further exploits correlations of effects between different characters in the clip as well as across movie threads. We propose a Conditional Random Field model atop a neural network that brings together visual and audio information, as well as semantics in the form of person tracks. We collect a new dataset referred to as the Movie4D dataset which annotates over 9K effects in 63 movies. These include effects such as physical interactions, water splashing, light, and shaking, and are grounded to either a character in the scene or the camera. We are interested in enabling automatic 4D cinema by parsing physical and special effects from untrimmed movies.
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