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Found 11 papers, 4 papers with code
Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wild
We propose an end-to-end deep-learning approach for automatic rigging and retargeting of 3D models of human faces in the wild.
Skeleton-free Pose Transfer for Stylized 3D Characters
We present the first method that automatically transfers poses between stylized 3D characters without skeletal rigging.
Audio-driven Neural Gesture Reenactment with Video Motion Graphs
We present a method that reenacts a high-quality video with gestures matching a target speech audio.
NeMF: Neural Motion Fields for Kinematic Animation
We present an implicit neural representation to learn the spatio-temporal space of kinematic motions.
Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes
This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogeneous collections of meshes that do not share a triangulation, as well as producing highly detail-preserving maps whose accuracy exceeds current state of the art.
FaceFormer: Speech-Driven 3D Facial Animation with Transformers
Speech-driven 3D facial animation is challenging due to the complex geometry of human faces and the limited availability of 3D audio-visual data.
Ranked #1 on
3D Face Animation
on VOCASET
Joint Audio-Text Model for Expressive Speech-Driven 3D Facial Animation
The existing datasets are collected to cover as many different phonemes as possible instead of sentences, thus limiting the capability of the audio-based model to learn more diverse contexts.
Contact-Aware Retargeting of Skinned Motion
Self-contacts, such as when hands touch each other or the torso or the head, are important attributes of human body language and dynamics, yet existing methods do not model or preserve these contacts.
Stochastic Scene-Aware Motion Prediction
A long-standing goal in computer vision is to capture, model, and realistically synthesize human behavior.
Neural Puppet: Generative Layered Cartoon Characters
We capture these subtle changes by applying an image translation network to refine the mesh rendering, providing an end-to-end model to generate new animations of a character with high visual quality.
Minimally Supervised Learning of Affective Events Using Discourse Relations
Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words.
