58 papers with code • 6 benchmarks • 8 datasets
These leaderboards are used to track progress in Motion Synthesis
Most implemented papers
On human motion prediction using recurrent neural networks
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.
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
We further explore a number of methods for integrating multiple clips into the learning process to develop multi-skilled agents capable of performing a rich repertoire of diverse skills.
HP-GAN: Probabilistic 3D human motion prediction via GAN
Our model, which we call HP-GAN, learns a probability density function of future human poses conditioned on previous poses.
MoGlow: Probabilistic and controllable motion synthesis using normalising flows
Data-driven modelling and synthesis of motion is an active research area with applications that include animation, games, and social robotics.
Multi-View Motion Synthesis via Applying Rotated Dual-Pixel Blur Kernels
In this work, we follow the trend of rendering the NIMAT effect by introducing a modification on the blur synthesis procedure in portrait mode.
MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement
To improve upon existing models, we propose a generic audio-driven facial animation approach that achieves highly realistic motion synthesis results for the entire face.
Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis
We present a real-time method for synthesizing highly complex human motions using a novel training regime we call the auto-conditioned Recurrent Neural Network (acRNN).
A Neural Temporal Model for Human Motion Prediction
We propose novel neural temporal models for predicting and synthesizing human motion, achieving state-of-the-art in modeling long-term motion trajectories while being competitive with prior work in short-term prediction and requiring significantly less computation.
Dancing to Music
In the analysis phase, we decompose a dance into a series of basic dance units, through which the model learns how to move.
CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion
Motion synthesis in a dynamic environment has been a long-standing problem for character animation.