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20192022
most citedMonocular Real-Time Volumetric Performance Capture

4 citations · 12 across the 5 of their papers we have counts for

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7 papers · 1 filter

cs.CV20223 cited

Control-NeRF: Editable Feature Volumes for Scene Rendering and Manipulation

Verica Lazova, Vladimir Guzov, Kyle Olszewski +2

We present a novel method for performing flexible, 3D-aware image content manipulation while enabling high-quality novel view synthesis. While NeRF-based approaches are effective f…

cs.CV2022

Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning

Ligong Han, Jian Ren, Hsin-Ying Lee +5

Most methods for conditional video synthesis use a single modality as the condition. This comes with major limitations. For example, it is problematic for a model conditioned on an…

cs.CV20213 cited

Flow Guided Transformable Bottleneck Networks for Motion Retargeting

Jian Ren, Menglei Chai, Oliver J. Woodford +2

Human motion retargeting aims to transfer the motion of one person in a "driving" video or set of images to another person. Existing efforts leverage a long training video from eac…

cs.CV2021

A Good Image Generator Is What You Need for High-Resolution Video Synthesis

Yu Tian, Jian Ren, Menglei Chai +4

Image and video synthesis are closely related areas aiming at generating content from noise. While rapid progress has been demonstrated in improving image-based models to handle la…

cs.CV20204 cited

Monocular Real-Time Volumetric Performance Capture

Ruilong Li, Yuliang Xiu, Shunsuke Saito +3

We present the first approach to volumetric performance capture and novel-view rendering at real-time speed from monocular video, eliminating the need for expensive multi-view syst…

cs.CV20202 cited

Intuitive, Interactive Beard and Hair Synthesis with Generative Models

Kyle Olszewski, Duygu Ceylan, Jun Xing +4

We present an interactive approach to synthesizing realistic variations in facial hair in images, ranging from subtle edits to existing hair to the addition of complex and challeng…