The ability to predict, anticipate and reason about future outcomes is a key component of
intelligent decision-making systems. In light of the success of deep learning in computer …

This paper addresses a novel task of anticipating 3D human-object interactions (HOIs). Most
existing research on HOI synthesis lacks comprehensive whole-body interactions with …

Leveraging physical knowledge described by partial differential equations (PDEs) is an
appealing way to improve unsupervised video forecasting models. Since physics is too …

Our goal is to synthesize 3D human motions given textual inputs describing simultaneous
actions, for examplewaving hand'whilewalking'at the same time. We refer to generating such …

We present a method to learn compositional multi-object dynamics models from image
observations based on implicit object encoders, Neural Radiance Fields (NeRFs), and …

We propose to forecast future hand-object interactions given an egocentric video. Instead of
predicting action labels or pixels, we directly predict the hand motion trajectory and the …

We present a method for learning to generate unbounded flythrough videos of natural
scenes starting from a single view. This capability is learned from a collection of single …

Understanding dynamics from visual observations is a challenging problem that requires
disentangling individual objects from the scene and learning their interactions. While recent …

A video prediction model that generalizes to diverse scenes would enable intelligent agents
such as robots to perform a variety of tasks via planning with the model. However, while …

Our work addresses long-term motion context issues for predicting future frames. To predict
the future precisely, it is required to capture which long-term motion context (eg, walking or …