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Found 10 papers, 5 papers with code
Generative Proxemics: A Prior for 3D Social Interaction from Images
To address this, we present a novel approach that learns a prior over the 3D proxemics two people in close social interaction and demonstrate its use for single-view 3D reconstruction.
Synthesizing Physical Character-Scene Interactions
These scene interactions are learned using an adversarial discriminator that evaluates the realism of a motion within the context of a scene.
Stochastic Scene-Aware Motion Prediction
A long-standing goal in computer vision is to capture, model, and realistically synthesize human behavior.
GIF: Generative Interpretable Faces
Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model.
Grasping Field: Learning Implicit Representations for Human Grasps
Specifically, our generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud.
From Variational to Deterministic Autoencoders
Variational Autoencoders (VAEs) provide a theoretically-backed and popular framework for deep generative models.
End-to-end Learning for Graph Decomposition
The new optimization problem can be viewed as a Conditional Random Field (CRF) in which the random variables are associated with the binary edge labels of the initial graph and the hard constraints are introduced in the CRF as high-order potentials.
Unsupervised Learning of Multi-Frame Optical Flow with Occlusions
In this paper, we propose a framework for unsupervised learning of optical flow and occlusions over multiple frames.
Detailed, accurate, human shape estimation from clothed 3D scan sequences
We address the problem of estimating human pose and body shape from 3D scans over time.
Deep representation learning for human motion prediction and classification
To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units.
