1.1k citations · 2.3k across the 13 of their papers we have counts for
16 papers
Human Body Measurement Estimation with Adversarial Augmentation
Nataniel Ruiz, Miriam Bellver, Timo Bolkart +4
We present a Body Measurement network (BMnet) for estimating 3D anthropomorphic measurements of the human body shape from silhouette images. Training of BMnet is performed on data…
Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing
Donglai Xiang, Timur Bagautdinov, Tuur Stuyck +10
Despite recent progress in developing animatable full-body avatars, realistic modeling of clothing - one of the core aspects of human self-expression - remains an open challenge. S…
AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling
Ziqian Bai, Timur Bagautdinov, Javier Romero +3
Neural fields such as implicit surfaces have recently enabled avatar modeling from raw scans without explicit temporal correspondences. In this work, we exploit autoregressive mode…
Embodied Hands: Modeling and Capturing Hands and Bodies Together
Javier Romero, Dimitrios Tzionas, Michael J. Black
Humans move their hands and bodies together to communicate and solve tasks. Capturing and replicating such coordinated activity is critical for virtual characters that behave reali…
Learning Realistic Human Reposing using Cyclic Self-Supervision with 3D Shape, Pose, and Appearance Consistency
Soubhik Sanyal, Alex Vorobiov, Timo Bolkart +5
Synthesizing images of a person in novel poses from a single image is a highly ambiguous task. Most existing approaches require paired training images; i.e. images of the same pers…
SMPLpix: Neural Avatars from 3D Human Models
Sergey Prokudin, Michael J. Black, Javier Romero
Recent advances in deep generative models have led to an unprecedented level of realism for synthetically generated images of humans. However, one of the remaining fundamental limi…
Learning Multi-Human Optical Flow
Anurag Ranjan, David T. Hoffmann, Dimitrios Tzionas +3
The optical flow of humans is well known to be useful for the analysis of human action. Recent optical flow methods focus on training deep networks to approach the problem. However…
FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second
David Smith, Matthew Loper, Xiaochen Hu +2
Current methods for body shape estimation either lack detail or require many images. They are usually architecturally complex and computationally expensive. We propose FACSIMILE (F…
Efficient Learning on Point Clouds with Basis Point Sets
Sergey Prokudin, Christoph Lassner, Javier Romero
With the increased availability of 3D scanning technology, point clouds are moving into the focus of computer vision as a rich representation of everyday scenes. However, they are…
Learning Human Optical Flow
Anurag Ranjan, Javier Romero, Michael J. Black
The optical flow of humans is well known to be useful for the analysis of human action. Given this, we devise an optical flow algorithm specifically for human motion and show that…
On human motion prediction using recurrent neural networks
Julieta Martinez, Michael J. Black, Javier Romero
Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and moti…
A simple yet effective baseline for 3d human pose estimation
Julieta Martinez, Rayat Hossain, Javier Romero +1
Following the success of deep convolutional networks, state-of-the-art methods for 3d human pose estimation have focused on deep end-to-end systems that predict 3d joint locations…
Unite the People: Closing the Loop Between 3D and 2D Human Representations
Christoph Lassner, Javier Romero, Martin Kiefel +3
3D models provide a common ground for different representations of human bodies. In turn, robust 2D estimation has proven to be a powerful tool to obtain 3D fits "in-the- wild". Ho…
Learning from Synthetic Humans
Gül Varol, Javier Romero, Xavier Martin +4
Estimating human pose, shape, and motion from images and videos are fundamental challenges with many applications. Recent advances in 2D human pose estimation use large amounts of…
Coupling Adaptive Batch Sizes with Learning Rates
Lukas Balles, Javier Romero, Philipp Hennig
Mini-batch stochastic gradient descent and variants thereof have become standard for large-scale empirical risk minimization like the training of neural networks. These methods are…
Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
Federica Bogo, Angjoo Kanazawa, Christoph Lassner +3
We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and show…