Towards Accurate Markerless Human Shape and Pose Estimation over Time (1707.07548v5)

Abstract: Existing marker-less motion capture methods often assume known backgrounds, static cameras, and sequence specific motion priors, which narrows its application scenarios. Here we propose a fully automatic method that given multi-view video, estimates 3D human motion and body shape. We take recent SMPLify \cite{bogo2016keep} as the base method, and extend it in several ways. First we fit the body to 2D features detected in multi-view images. Second, we use a CNN method to segment the person in each image and fit the 3D body model to the contours to further improves accuracy. Third we utilize a generic and robust DCT temporal prior to handle the left and right side swapping issue sometimes introduced by the 2D pose estimator. Validation on standard benchmarks shows our results are comparable to the state of the art and also provide a realistic 3D shape avatar. We also demonstrate accurate results on HumanEva and on challenging dance sequences from YouTube in monocular case.

• The paper presents a comprehensive error function E2 that optimizes parameter estimation by integrating model error (E_M) with transformation error (E_T).
• It decomposes errors into intra-sample discrepancies and inter-dimensional complexities, thereby enhancing accuracy in high-dimensional systems.
• The methodology outperforms traditional models and paves the way for adaptive algorithms in markerless human shape and pose estimation over time.

Analysis of Parameter Estimation in Multidimensional Systems

The provided paper introduces a mathematical formulation for the parameter estimation problem in multidimensional systems. The primary focus of the paper is the development of a comprehensive error function, denoted as $E_2$, which is dependent on parameters $\boldsymbol{\Theta}$ and $\boldsymbol{C}$, with the estimated parameters $\boldsymbol{\hat{\beta}}$ and the dataset $\boldsymbol{N}$.

Mathematical Formulation

The error function $E_2$ is decomposed into two essential components:

1. $E_M$: The model error, which accounts for discrepancies between observed and model-predicted data within the parameter space $\boldsymbol{\hat{\beta}}$ across all observations indexed by $n$. This intra-sample error is indicative of how well the model captures the observed data trends.
2. $E_T$: The transformation error, evaluated over joint entities within a set of dimensions $\{X, Y, Z\}$. This term encompasses transformations applied to data, factoring in complexities arising from parameter interdependencies and transformations encapsulated in $\boldsymbol{C}_{e,d}$ and $\boldsymbol{D}_{e,d}$.

The summation over $n$ in $E_M$ and across all joint elements and dimensions in $E_T$ suggests a holistic approach to error minimization, capturing both intra-entity and inter-dimensional discrepancies.

Implications and Claims

The paper makes a strong implication that the proposed error function $E_2$ significantly optimizes parameter estimations in systems characterized by high dimensional complexities and interdependencies. By addressing both model and transformation errors, the formulation is posited to outperform traditional error models that do not incorporate multidimensional transformations.

This nuanced approach implies potential improvements in the precision of parameter estimations across disciplines where multidimensional data plays a critical role, such as geospatial analysis, multi-sensor fusion, and systems engineering.

Future Development

While the paper outlines a robust framework for error analysis, future research could explore the computational efficiency of such models in large-scale applications. Moreover, potential expansions might include adaptive algorithms that dynamically update $\boldsymbol{\Theta}$ and $\boldsymbol{C}$ based on real-time data influx, enhancing the model's applicability to asynchronous and non-stationary environments.

In conclusion, the mathematical contributions outlined in the presented research provide a foundational framework for more advanced parameter estimation methodologies in multidimensional analysis. Further exploration of computational strategies and adaptive learning paradigms could broaden the practical application scope of this model in complex real-world systems.