activity
20172022
most citedDecomposing Motion and Content for Natural Video Sequence Prediction

416 citations · 531 across the 7 of their papers we have counts for

collaborators

14 papers

cs.CV202280 cited

Phenaki: Variable Length Video Generation From Open Domain Textual Description

Ruben Villegas, Mohammad Babaeizadeh, Pieter-Jan Kindermans +6

We present Phenaki, a model capable of realistic video synthesis, given a sequence of textual prompts. Generating videos from text is particularly challenging due to the computatio…

cs.CV2022

RiCS: A 2D Self-Occlusion Map for Harmonizing Volumetric Objects

Yunseok Jang, Ruben Villegas, Jimei Yang +3

There have been remarkable successes in computer vision with deep learning. While such breakthroughs show robust performance, there have still been many challenges in learning in-d…

cs.CV2021

Contact-Aware Retargeting of Skinned Motion

Ruben Villegas, Duygu Ceylan, Aaron Hertzmann +2

This paper introduces a motion retargeting method that preserves self-contacts and prevents interpenetration. Self-contacts, such as when hands touch each other or the torso or the…

cs.CV2021

Stochastic Scene-Aware Motion Prediction

Mohamed Hassan, Duygu Ceylan, Ruben Villegas +4

A long-standing goal in computer vision is to capture, model, and realistically synthesize human behavior. Specifically, by learning from data, our goal is to enable virtual humans…

cs.CV202112 cited

Single-image Full-body Human Relighting

Manuel Lagunas, Xin Sun, Jimei Yang +5

We present a single-image data-driven method to automatically relight images with full-body humans in them. Our framework is based on a realistic scene decomposition leveraging pre…

cs.CV20213 cited

Task-Generic Hierarchical Human Motion Prior using VAEs

Jiaman Li, Ruben Villegas, Duygu Ceylan +4

A deep generative model that describes human motions can benefit a wide range of fundamental computer vision and graphics tasks, such as providing robustness to video-based human p…