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Found 12 papers, 5 papers with code
Motion Question Answering via Modular Motion Programs
In order to build artificial intelligence systems that can perceive and reason with human behavior in the real world, we must first design models that conduct complex spatio-temporal reasoning over motion sequences.
CIRCLE: Capture In Rich Contextual Environments
Leveraging our dataset, the model learns to use ego-centric scene information to achieve nontrivial reaching tasks in the context of complex 3D scenes.
Scene Synthesis from Human Motion
Large-scale capture of human motion with diverse, complex scenes, while immensely useful, is often considered prohibitively costly.
Ego-Body Pose Estimation via Ego-Head Pose Estimation
In addition, collecting large-scale, high-quality datasets with paired egocentric videos and 3D human motions requires accurate motion capture devices, which often limit the variety of scenes in the videos to lab-like environments.
GIMO: Gaze-Informed Human Motion Prediction in Context
We perform an extensive study of the benefits of leveraging the eye gaze for ego-centric human motion prediction with various state-of-the-art architectures.
DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points
To make the local features aware of the global context and improve their matching accuracy, we introduce DenseGAP, a new solution for efficient Dense correspondence learning with a Graph-structured neural network conditioned on Anchor Points.
Task-Generic Hierarchical Human Motion Prior using VAEs
We demonstrate the effectiveness of our hierarchical motion variational autoencoder in a variety of tasks including video-based human pose estimation, motion completion from partial observations, and motion synthesis from sparse key-frames.
Ranked #4 on
Motion Synthesis
on LaFAN1
Dynamic Facial Asset and Rig Generation from a Single Scan
We also model the joint distribution between identities and expressions, enabling the inference of the full set of personalized blendshapes with dynamic appearances from a single neutral input scan.
Learning to Generate Diverse Dance Motions with Transformer
We also introduce new evaluation metrics for the quality of synthesized dance motions, and demonstrate that our system can outperform state-of-the-art methods.
VirtualHome: Simulating Household Activities via Programs
We then implement the most common atomic (inter)actions in the Unity3D game engine, and use our programs to "drive" an artificial agent to execute tasks in a simulated household environment.
Differentiable Compositional Kernel Learning for Gaussian Processes
The NKN architecture is based on the composition rules for kernels, so that each unit of the network corresponds to a valid kernel.
Learning to Act Properly: Predicting and Explaining Affordances from Images
We address the problem of affordance reasoning in diverse scenes that appear in the real world.
