We present a method for simultaneously estimating 3D human pose and body shape from a sparse set of wide-baseline camera views. We train a symmetric convolutional autoencoder with a dual loss that enforces learning of a latent representation that encodes skeletal joint positions, and at the same time learns a deep representation of volumetric body…Read More
Tag: mocap
Human Motion Modeling using DVGANs
We present a novel generative model for human motion modeling using Generative Adversarial Networks (GANs). We formulate the GAN discriminator using dense validation at each time-scale and perturb the discriminator input to make it translation invariant. Our model is capable of motion generation and completion. We show through our evaluations the resiliency to noise, generalization…Read More
PoseTrack
It’s always fun to see machine learning challenges posed as well as the competing solutions, but even more so when they’re relevant to VFX. PoseTrack is a new large-scale benchmark for human pose estimation and tracking in video. We provide a publicly available training and validation set as well as an evaluation server for benchmarking…Read More
Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation
A great example of how domain-specific knowledge can help design network architecture, in this case helping them make the jump from supervised learning (where training data may be difficult or time-consuming to acquire) to unsupervised learning (where training data is often plentiful). Modern 3D human pose estimation techniques rely on deep networks, which require large…Read More
Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation
Depth map and UV map from a single image! “It has been recently shown that neural networks can recover the geometric structure of a face from a single given image. A common denominator of most existing face geometry reconstruction methods is the restriction of the solution space to some low-dimensional subspace. While such a model…Read More