Compositional Human Pose Regression
Regression based methods are not performing as well as detection based methods for human pose estimation. A central problem is that the structural information in the pose is not well exploited in the previous regression methods. In this work, we propose a structure-aware regression approach. It adopts a reparameterized pose representation using bones instead of joints. It exploits the joint connection structure to define a compositional loss function that encodes the long range interactions in the pose. It is simple, effective, and general for both 2D and 3D pose estimation in a unified setting. Comprehensive evaluation validates the effectiveness of our approach. It significantly advances the state-of-the-art on Human3.6M and is competitive with state-of-the-art results on MPII.
Code (1)
Tasks
3D Human Pose Estimation3D Pose EstimationPose EstimationregressionSimilar Papers 제목 키워드 기반
Probing the Compositionality of Intuitive Functions
How do people learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is accomplished by harnessing compositionality: complex structure is decomposed into …
regressionPrimal path algorithm for compositional data analysis
Compositional data have two unique characteristics compared to typical multivariate data: the observed values are nonnegative and their summand is exactly one. To reflect these characteristics, a specific regularized reg…
General ClassificationregressionOn the inability of Gaussian process regression to optimally learn compositional functions
We rigorously prove that deep Gaussian process priors can outperform Gaussian process priors if the target function has a compositional structure. To this end, we study information-theoretic lower bounds for posterior co…
regressionMulti-Step Regression Learning for Compositional Distributional Semantics
Resolving Lexical Ambiguity in Tensor Regression Models of Meaning
This paper provides a method for improving tensor-based compositional distributional models of meaning by the addition of an explicit disambiguation step prior to composition. In contrast with previous research where thi…
regression