most citedGenerating Videos with Scene Dynamics

849 citations · 2.1k across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV20163 cited

Following Gaze Across Views

Adrià Recasens, Carl Vondrick, Aditya Khosla +1

Following the gaze of people inside videos is an important signal for understanding people and their actions. In this paper, we present an approach for following gaze across views…

cs.CV20163 cited

Who is Mistaken?

Benjamin Eysenbach, Carl Vondrick, Antonio Torralba

Recognizing when people have false beliefs is crucial for understanding their actions. We introduce the novel problem of identifying when people in abstract scenes have incorrect b…

cs.AI2016166 cited

A Compositional Object-Based Approach to Learning Physical Dynamics

Michael B. Chang, Tomer Ullman, Antonio Torralba +1

We present the Neural Physics Engine (NPE), a framework for learning simulators of intuitive physics that naturally generalize across variable object count and different scene conf…

cs.CV2016233 cited

SoundNet: Learning Sound Representations from Unlabeled Video

Yusuf Aytar, Carl Vondrick, Antonio Torralba

We learn rich natural sound representations by capitalizing on large amounts of unlabeled sound data collected in the wild. We leverage the natural synchronization between vision a…

cs.CV2016849 cited

Generating Videos with Scene Dynamics

Carl Vondrick, Hamed Pirsiavash, Antonio Torralba

We capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. action classification) and video generation tas…

cs.CV2016175 cited

Places: An Image Database for Deep Scene Understanding

Bolei Zhou, Aditya Khosla, Agata Lapedriza +2

The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification at tasks such as object and scene…