259 citations · 259 across the 2 of their papers we have counts for
6 papers
SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows
Vikram Voleti, Boris N. Oreshkin, Florent Bocquelet +3
Inverse Kinematics (IK) systems are often rigid with respect to their input character, thus requiring user intervention to be adapted to new skeletons. In this paper we aim at crea…
Motion Inbetweening via Deep -Interpolator
Boris N. Oreshkin, Antonios Valkanas, Félix G. Harvey +3
We show that the task of synthesizing human motion conditioned on a set of key frames can be solved more accurately and effectively if a deep learning based interpolator operates i…
ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics
Boris N. Oreshkin, Florent Bocquelet, Félix G. Harvey +2
Our work focuses on the development of a learnable neural representation of human pose for advanced AI assisted animation tooling. Specifically, we tackle the problem of constructi…
Robust Motion In-betweening
Félix G. Harvey, Mike Yurick, Derek Nowrouzezahrai +1
In this work we present a novel, robust transition generation technique that can serve as a new tool for 3D animators, based on adversarial recurrent neural networks. The system sy…
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning
Julien Roy, Paul Barde, Félix G. Harvey +2
In multi-agent reinforcement learning, discovering successful collective behaviors is challenging as it requires exploring a joint action space that grows exponentially with the nu…
Recurrent Transition Networks for Character Locomotion
Félix G. Harvey, Christopher Pal
Manually authoring transition animations for a complete locomotion system can be a tedious and time-consuming task, especially for large games that allow complex and constrained lo…