Human Pose Forecasting
31 papers with code • 4 benchmarks • 4 datasets
Human pose forecasting is the task of detecting and predicting future human poses.
( Image credit: EgoPose )
Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality.
In this paper, we propose a simple feed-forward deep network for motion prediction, which takes into account both temporal smoothness and spatial dependencies among human body joints.
We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models.
The proposed method is generic and principled as it can be used for transforming any spatio-temporal graph through employing a certain set of well defined steps.
In this paper, we propose a novel sampling strategy for sampling very diverse results from an imbalanced multimodal distribution learned by a deep generative model.
First we explicitly model the high level structure of active objects in the scene---humans---and use a VAE to model the possible future movements of humans in the pose space.
For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence.
We consider the problem of image-to-video translation, where an input image is translated into an output video containing motions of a single object.