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Found 9 papers, 4 papers with code
Towards Diverse and Natural Scene-aware 3D Human Motion Synthesis
The ability to synthesize long-term human motion sequences in real-world scenes can facilitate numerous applications.
Scene-aware Generative Network for Human Motion Synthesis
We revisit human motion synthesis, a task useful in various real world applications, in this paper.
A Unified 3D Human Motion Synthesis Model via Conditional Variational Auto-Encoder
Notably, by considering this problem as a conditional generation process, we estimate a parametric distribution of the missing regions based on the input conditions, from which to sample and synthesize the full motion series.
Positive-Congruent Training: Towards Regression-Free Model Updates
Reducing inconsistencies in the behavior of different versions of an AI system can be as important in practice as reducing its overall error.
Motion Guided 3D Pose Estimation from Videos
We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose.
Ranked #19 on
3D Human Pose Estimation
on Human3.6M
Convolutional Sequence Generation for Skeleton-Based Action Synthesis
It captures the temporal structure at multiple scales through the GP prior and the temporal convolutions; and establishes the spatial connection between the latent vectors and the skeleton graphs via a novel graph refining scheme.
Ranked #2 on
Human action generation
on NTU RGB+D
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Dynamics of human body skeletons convey significant information for human action recognition.
Unconstrained Fashion Landmark Detection via Hierarchical Recurrent Transformer Networks
This work addresses unconstrained fashion landmark detection, where clothing bounding boxes are not provided in both training and test.
Fashion Landmark Detection in the Wild
Fashion landmark is also compared to clothing bounding boxes and human joints in two applications, fashion attribute prediction and clothes retrieval, showing that fashion landmark is a more discriminative representation to understand fashion images.
