Human Dynamics
18 papers with code • 0 benchmarks • 1 datasets
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Latest papers with no code
PhysPT: Physics-aware Pretrained Transformer for Estimating Human Dynamics from Monocular Videos
PhysPT exploits a Transformer encoder-decoder backbone to effectively learn human dynamics in a self-supervised manner.
TexVocab: Texture Vocabulary-conditioned Human Avatars
To adequately utilize the available image evidence in multi-view video-based avatar modeling, we propose TexVocab, a novel avatar representation that constructs a texture vocabulary and associates body poses with texture maps for animation.
EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion Generation
We introduce Efficient Motion Diffusion Model (EMDM) for fast and high-quality human motion generation.
A Grammatical Compositional Model for Video Action Detection
Analysis of human actions in videos demands understanding complex human dynamics, as well as the interaction between actors and context.
Envisioning a Next Generation Extended Reality Conferencing System with Efficient Photorealistic Human Rendering
Efficient photorealistic rendering of human 3D dynamics is the core of immersive meetings.
Learning Human Dynamics in Autonomous Driving Scenarios
In this work, we propose a holistic framework for learning physically plausible human dynamics from real driving scenarios, narrowing the gap between real and simulated human behavior in safety-critical applications.
Time Series Clustering for Human Behavior Pattern Mining
Human behavior modeling deals with learning and understanding behavior patterns inherent in humans' daily routines.
Can Action be Imitated? Learn to Reconstruct and Transfer Human Dynamics from Videos
To achieve this, a novel Mesh-based Video Action Imitation (M-VAI) method is proposed by us.
Systemic formalisation of Cyber-Physical-Social System (CPSS): A systematic literature review
The concept of CPSS has been around for over a decade and it has gained increasing attention over the past few years.
Weakly-supervised Learning of Human Dynamics
This paper proposes a weakly-supervised learning framework for dynamics estimation from human motion.