Search Results for author:
Found 7 papers, 5 papers with code
SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows
Inverse Kinematics (IK) systems are often rigid with respect to their input character, thus requiring user intervention to be adapted to new skeletons.
Motion Inbetweening via Deep $Δ$-Interpolator
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 in the delta mode using the spherical linear interpolator as a baseline.
Ranked #1 on
Motion Synthesis
on LaFAN1
ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics
Our work focuses on the development of a learnable neural representation of human pose for advanced AI assisted animation tooling.
Robust Motion In-betweening
To quantitatively evaluate performance on transitions and generalizations to longer time horizons, we present well-defined in-betweening benchmarks on a subset of the widely used Human3. 6M dataset and on LaFAN1, a novel high quality motion capture dataset that is more appropriate for transition generation.
Ranked #3 on
Motion Synthesis
on LaFAN1
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning
Finally, we analyze the effects of our proposed methods on the policies that our agents learn and show that our methods successfully enforce the qualities that we propose as proxies for coordinated behaviors.
Recurrent Transition Networks for Character Locomotion
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 locomotion movements, where the number of transitions grows exponentially with the number of states.
Recurrent Semi-supervised Classification and Constrained Adversarial Generation with Motion Capture Data
We find that using such constraints allow to stabilize the training of recurrent adversarial architectures for animation generation.
