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Found 24 papers, 8 papers with code
WalkTheDog: Cross-Morphology Motion Alignment via Phase Manifolds
We present a new approach for understanding the periodicity structure and semantics of motion datasets, independently of the morphology and skeletal structure of characters.
Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wild
To the best of our knowledge, the Nymeria dataset is the world largest in-the-wild collection of human motion with natural and diverse activities; first of its kind to provide synchronized and localized multi-device multimodal egocentric data; and the world largest dataset with motion-language descriptions.
DivaTrack: Diverse Bodies and Motions from Acceleration-Enhanced Three-Point Trackers
We then condition the otherwise ambiguous lower-body pose with the predictions of foot contact and upper-body pose in a two-stage model.
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.
AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation
Among existing approaches for avatar stylization, direct optimization methods can produce excellent results for arbitrary styles but they are unpleasantly slow.
Learning to Transfer In-Hand Manipulations Using a Greedy Shape Curriculum
In-hand object manipulation is challenging to simulate due to complex contact dynamics, non-repetitive finger gaits, and the need to indirectly control unactuated objects.
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.
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.
Decision Making for Hierarchical Multi-label Classification with Multidimensional Local Precision Rate
We show that classification decisions made by simply sorting objects across classes in descending order of their true mLPRs can, in theory, ensure the class hierarchy and lead to the maximization of CATCH, an objective function we introduce that is related to the area under a hit curve.
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.
From Intervention to Domain Transportation: A Novel Perspective to Optimize Recommendation
The interventional nature of recommendation has attracted increasing attention in recent years.
Towards Robust Off-policy Learning for Runtime Uncertainty
Off-policy learning plays a pivotal role in optimizing and evaluating policies prior to the online deployment.
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training
Notably, on CMU Panoptic Studio, we are able to reduce the turn-around time by 60% and annotation cost by 80% when compared to the conventional annotation process.
Identity-Disentangled Neural Deformation Model for Dynamic Meshes
When applied to dynamically deforming shapes such as the human hands, however, they would need to preserve temporal coherence of the deformation as well as the intrinsic identity of the subject.
Understanding the role of importance weighting for deep learning
The recent paper by Byrd & Lipton (2019), based on empirical observations, raises a major concern on the impact of importance weighting for the over-parameterized deep learning models.
Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels
Learning latent representations of registered meshes is useful for many 3D tasks.
Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks
Low-dimensional embeddings of knowledge graphs and behavior graphs have proved remarkably powerful in varieties of tasks, from predicting unobserved edges between entities to content recommendation.
Disentangling Pose from Appearance in Monochrome Hand Images
Hand pose estimation from the monocular 2D image is challenging due to the variation in lighting, appearance, and background.
HierLPR: Decision making in hierarchical multi-label classification with local precision rates
In this article we propose a novel ranking algorithm, referred to as HierLPR, for the multi-label classification problem when the candidate labels follow a known hierarchical structure.
Learning Warped Guidance for Blind Face Restoration
For better recovery of fine facial details, we modify the problem setting by taking both the degraded observation and a high-quality guided image of the same identity as input to our guided face restoration network (GFRNet).
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Going Deeper in Spiking Neural Networks: VGG and Residual Architectures
Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware.
