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Found 12 papers, 4 papers with code
MOCHA: Real-Time Motion Characterization via Context Matching
Central to our framework is the Neural Context Matcher, which generates a motion feature for the target character with the most similar context to the input motion feature.
DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics
We introduce DROP, a novel framework for modeling Dynamics Responses of humans using generative mOtion prior and Projective dynamics.
Physics-based Motion Retargeting from Sparse Inputs
We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies.
QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensors
Most existing methods for motion tracking avoid environment interaction apart from foot-floor contact due to their complex dynamics and hard constraints.
Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions
We present a novel generative model, called Bidirectional GaitNet, that learns the relationship between human anatomy and its gait.
PMP: Learning to Physically Interact with Environments using Part-wise Motion Priors
The proposed PMP allows us to assemble multiple part skills to animate a character, creating a diverse set of motions with different combinations of existing data.
PhaseMP: Robust 3D Pose Estimation via Phase-conditioned Human Motion Prior
We present a novel motion prior, called PhaseMP, modeling a probability distribution on pose transitions conditioned by a frequency domain feature extracted from a periodic autoencoder.
Breaking The Limits of Text-conditioned 3D Motion Synthesis with Elaborative Descriptions
Given its wide applications, there is increasing focus on generating 3D human motions from textual descriptions.
Leveraging Demonstrations with Latent Space Priors
Starting with a learned joint latent space, we separately train a generative model of demonstration sequences and an accompanying low-level policy.
QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars
Real-time tracking of human body motion is crucial for interactive and immersive experiences in AR/VR.
Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generation
Real-time human motion reconstruction from a sparse set of (e. g. six) wearable IMUs provides a non-intrusive and economic approach to motion capture.
Conditional Motion In-betweening
Motion in-betweening (MIB) is a process of generating intermediate skeletal movement between the given start and target poses while preserving the naturalness of the motion, such as periodic footstep motion while walking.
