activity
20162020
most citedLearning to Infer Implicit Surfaces without 3D Supervision

88 citations · 460 across the 15 of their papers we have counts for

collaborators

15 papers

cs.GR2020

Dynamic Facial Asset and Rig Generation from a Single Scan

Jiaman Li, Zhengfei Kuang, Yajie Zhao +3

The creation of high-fidelity computer-generated (CG) characters used in film and gaming requires intensive manual labor and a comprehensive set of facial assets to be captured wit…

cs.CV2020★ 74 cited

Learning to Generate Diverse Dance Motions with Transformer

Jiaman Li, Yihang Yin, Hang Chu +4

With the ongoing pandemic, virtual concerts and live events using digitized performances of musicians are getting traction on massive multiplayer online worlds. However, well chore…

cs.CV2020★ 4 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.CV2020★ 47 cited

Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels

Yi Zhou, Chenglei Wu, Zimo Li +5

Learning latent representations of registered meshes is useful for many 3D tasks. Techniques have recently shifted to neural mesh autoencoders. Although they demonstrate higher pre…

cs.CV2020★ 20 cited

Generative Tweening: Long-term Inbetweening of 3D Human Motions

Yi Zhou, Jingwan Lu, Connelly Barnes +3

The ability to generate complex and realistic human body animations at scale, while following specific artistic constraints, has been a fundamental goal for the game and animation…

cs.CV2020★ 2 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…