15 citations · 30 across the 8 of their papers we have counts for
11 papers
Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wild
Dafei Qin, Jun Saito, Noam Aigerman +2
We propose an end-to-end deep-learning approach for automatic rigging and retargeting of 3D models of human faces in the wild. Our approach, called Neural Face Rigging (NFR), holds…
Skeleton-free Pose Transfer for Stylized 3D Characters
Zhouyingcheng Liao, Jimei Yang, Jun Saito +2
We present the first method that automatically transfers poses between stylized 3D characters without skeletal rigging. In contrast to previous attempts to learn pose transformatio…
Audio-driven Neural Gesture Reenactment with Video Motion Graphs
Yang Zhou, Jimei Yang, Dingzeyu Li +3
Human speech is often accompanied by body gestures including arm and hand gestures. We present a method that reenacts a high-quality video with gestures matching a target speech au…
NeMF: Neural Motion Fields for Kinematic Animation
Chengan He, Jun Saito, James Zachary +2
We present an implicit neural representation to learn the spatio-temporal space of kinematic motions. Unlike previous work that represents motion as discrete sequential samples, we…
Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes
Noam Aigerman, Kunal Gupta, Vladimir G. Kim +3
This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogen…
Joint Audio-Text Model for Expressive Speech-Driven 3D Facial Animation
Yingruo Fan, Zhaojiang Lin, Jun Saito +2
Speech-driven 3D facial animation with accurate lip synchronization has been widely studied. However, synthesizing realistic motions for the entire face during speech has rarely be…
FaceFormer: Speech-Driven 3D Facial Animation with Transformers
Yingruo Fan, Zhaojiang Lin, Jun Saito +2
Speech-driven 3D facial animation is challenging due to the complex geometry of human faces and the limited availability of 3D audio-visual data. Prior works typically focus on lea…
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…
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…
Neural Puppet: Generative Layered Cartoon Characters
Omid Poursaeed, Vladimir G. Kim, Eli Shechtman +2
We propose a learning based method for generating new animations of a cartoon character given a few example images. Our method is designed to learn from a traditionally animated se…
Minimally Supervised Learning of Affective Events Using Discourse Relations
Jun Saito, Yugo Murawaki, Sadao Kurohashi
Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly becau…