motion retargeting
18 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in motion retargeting
MultiSenseBadminton
Most implemented papers
C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning
The motion retargeting learning is performed using refined data in a latent space by the cyclic and filtering paths of our method.
Learning Character-Agnostic Motion for Motion Retargeting in 2D
ChrisWu1997/2D-Motion-Retargeting
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In order to achieve our goal, we learn to extract, directly from a video, a high-level latent motion representation, which is invariant to the skeleton geometry and the camera view.
Task-Oriented Hand Motion Retargeting for Dexterous Manipulation Imitation
In this work, we capture the hand information by using a state-of-the-art hand pose estimator.
Skeleton-Aware Networks for Deep Motion Retargeting
DeepMotionEditing/deep-motion-editing
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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.
Learning Skeletal Articulations with Neural Blend Shapes
PeizhuoLi/neural-blend-shapes
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Furthermore, we propose neural blend shapes--a set of corrective pose-dependent shapes which improve the deformation quality in the joint regions in order to address the notorious artifacts resulting from standard rigging and skinning.
JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting
rmokady/JOKR
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To alleviate this problem, we introduce JOKR - a JOint Keypoint Representation that captures the motion common to both the source and target videos, without requiring any object prior or data collection.
AgileGAN: stylizing portraits by inversion-consistent transfer learning
While substantial progress has been made in automated stylization, generating high quality stylistic portraits is still a challenge, and even the recent popular Toonify suffers from several artifacts when used on real input images.
DexMV: Imitation Learning for Dexterous Manipulation from Human Videos
While significant progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipulation.
Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations
jianglongye/cgf
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We will first convert the large-scale human-object interaction trajectories to robot demonstrations via motion retargeting, and then use these demonstrations to train CGF.
ViA: View-invariant Skeleton Action Representation Learning via Motion Retargeting
YangDi666/UNIK
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Current self-supervised approaches for skeleton action representation learning often focus on constrained scenarios, where videos and skeleton data are recorded in laboratory settings.
