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A Database and Evaluation Methodology for Optical Flow
TLDR
This paper proposes a new set of benchmarks and evaluation methods for the next generation of optical flow algorithms and analyzes the results obtained to date to draw a large number of conclusions.
A Naturalistic Open Source Movie for Optical Flow Evaluation
TLDR
A new optical flow data set derived from the open source 3D animated short film Sintel is introduced, which has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects.
SMPL: a skinned multi-person linear model
TLDR
The Skinned Multi-Person Linear model (SMPL) is a skinned vertex-based model that accurately represents a wide variety of body shapes in natural human poses that is compatible with existing graphics pipelines and iscompatible with existing rendering engines.
End-to-End Recovery of Human Shape and Pose
TLDR
This work introduces an adversary trained to tell whether human body shape and pose parameters are real or not using a large database of 3D human meshes, and produces a richer and more useful mesh representation that is parameterized by shape and 3D joint angles.
Towards Understanding Action Recognition
TLDR
It is found that high-level pose features greatly outperform low/mid level features, in particular, pose over time is critical, but current pose estimation algorithms are not yet reliable enough to provide this information.
Secrets of optical flow estimation and their principles
TLDR
It is discovered that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques, and while median filtering of intermediate flow fields during optimization is a key to recent performance gains, it leads to higher energy solutions.
Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
TLDR
The first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image is described, showing superior pose accuracy with respect to the state of the art.
The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields
TLDR
A framework based on robust estimation is presented that addresses violations of the brightness constancy and spatial smoothness assumptions caused by multiple motions of optical flow, and is applied to standard formulations of the optical flow problem thus reducing their sensitivity to violations of their underlying assumptions.
HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion
TLDR
A baseline algorithm for 3D articulated tracking that uses a relatively standard Bayesian framework with optimization in the form of Sequential Importance Resampling and Annealed Particle Filtering is described, and a variety of likelihood functions, prior models of human motion and the effects of algorithm parameters are explored.
On Human Motion Prediction Using Recurrent Neural Networks
TLDR
It is shown that, surprisingly, state of the art performance can be achieved by a simple baseline that does not attempt to model motion at all, and a simple and scalable RNN architecture is proposed that obtains state-of-the-art performance on human motion prediction.
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