In this work, we develop intuitive controls for editing the style of 3D objects. Our framework,
Text2Mesh, stylizes a 3D mesh by predicting color and local geometric details which conform …

In unsupervised domain mapping, the learner is given two unmatched datasets $ A $ and $
B $. The goal is to learn a mapping $ G_ {AB} $ that translates a sample in $ A $ to the …

We wish to automatically predict the" speediness" of moving objects in videos-whether they
move faster, at, or slower than their" natural" speed. The core component in our approach is …

We consider the task of generating diverse and realistic videos guided by natural audio
samples from a wide variety of semantic classes. For this task, the videos are required to be …

Given a single image $ x $ from domain $ A $ and a set of images from domain $ B $, our
task is to generate the analogous of $ x $ in $ B $. We argue that this task could be a key AI …

Anomaly detection, the task of identifying unusual samples in data, often relies on a large
set of training samples. In this work, we consider the setting of few-shot anomaly detection in …

Recent work has shown that convolutional neural network classifiers overly rely on texture at
the expense of shape cues. We make a similar but different distinction between shape and …

We consider the task of generating diverse and novel videos from a single video sample.
Recently, new hierarchical patch-GAN based approaches were proposed for generating …

We tackle the task of learning dynamic 3D semantic radiance fields given a single monocular
video as input. Our learned semantic radiance field captures per-point semantics as well …

Recent advances in machine learning have shown that Reinforcement Learning from
Human Feedback (RLHF) can improve machine learning models and align them with human …