In this paper, we present a novel data-driven framework for motion style transfer, which learns from an unpaired collection of motions with style labels, and enables transferring motion styles not observed during training.
In other words, our operators form the building blocks of a new deep motion processing framework that embeds the motion into a common latent space, shared by a collection of homeomorphic skeletons.
Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality.
In this paper, we formalize the music-conditioned dance generation as a sequence-to-sequence learning problem and devise a novel seq2seq architecture to efficiently process long sequences of music features and capture the fine-grained correspondence between music and dance.
Our approach is the first humanoid control method that successfully learns from a large-scale human motion dataset (Human3. 6M) and generates diverse long-term motions.
Our model, which we call HP-GAN, learns a probability density function of future human poses conditioned on previous poses.
Synthesizing 3D human motion plays an important role in many graphics applications as well as understanding human activity.