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Found 7 papers, 5 papers with code
Pose-to-Motion: Cross-Domain Motion Retargeting with Pose Prior
Our experiments show that our method effectively combines the motion features of the source character with the pose features of the target character, and performs robustly with small or noisy pose data sets, ranging from a few artist-created poses to noisy poses estimated directly from images.
Example-based Motion Synthesis via Generative Motion Matching
At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches.
MoDi: Unconditional Motion Synthesis from Diverse Data
In this work, we present MoDi -- a generative model trained in an unsupervised setting from an extremely diverse, unstructured and unlabeled dataset.
GANimator: Neural Motion Synthesis from a Single Sequence
We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence.
Learning Skeletal Articulations with Neural Blend Shapes
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.
Skeleton-Aware Networks for Deep Motion Retargeting
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.
Saliency based Semi-supervised Learning for Orbiting Satellite Tracking
The trajectory and boundary of an orbiting satellite are fundamental information for on-orbit repairing and manipulation by space robots.
