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Found 6 papers, 2 papers with code
SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data
Our system incorporates a convolutional-based autoencoder that synthesizes high-quality continuous human poses by learning the human motion manifold from motion capture data.
Motion-R3: Fast and Accurate Motion Annotation via Representation-based Representativeness Ranking
In this paper, we follow a data-centric philosophy and propose a novel motion annotation method based on the inherent representativeness of motion data in a given dataset.
Pose Representations for Deep Skeletal Animation
Data-driven character animation techniques rely on the existence of a properly established model of motion, capable of describing its rich context.
Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structure
In this work, we present a music-driven motion synthesis framework that generates long-term sequences of human motions which are synchronized with the input beats, and jointly form a global structure that respects a specific dance genre.
MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency
We introduce MotioNet, a deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video. While previous methods rely on either rigging or inverse kinematics (IK) to associate a consistent skeleton with temporally coherent joint rotations, our method is the first data-driven approach that directly outputs a kinematic skeleton, which is a complete, commonly used, motion representation.
Deep motifs and motion signatures
In this paper we introduce motion motifs and motion signatures that are a succinct but descriptive representation of motion sequences.
