88 citations · 291 across the 10 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…
Learning to Infer Implicit Surfaces without 3D Supervision
Shichen Liu, Shunsuke Saito, Weikai Chen +1
Recent advances in 3D deep learning have shown that it is possible to train highly effective deep models for 3D shape generation, directly from 2D images. This is particularly inte…