Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation (1808.05942v1)

Abstract: Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models. Mapping from the 2D image space to the prediction space is difficult: perspective ambiguities make the loss function noisy and training data is scarce. In this paper, we propose a novel approach (Neural Body Fitting (NBF)). It integrates a statistical body model within a CNN, leveraging reliable bottom-up semantic body part segmentation and robust top-down body model constraints. NBF is fully differentiable and can be trained using 2D and 3D annotations. In detailed experiments, we analyze how the components of our model affect performance, especially the use of part segmentations as an explicit intermediate representation, and present a robust, efficiently trainable framework for 3D human pose estimation from 2D images with competitive results on standard benchmarks. Code will be made available at http://github.com/mohomran/neural_body_fitting

• The paper introduces Neural Body Fitting, a hybrid framework that combines CNN-based semantic segmentation with an embedded SMPL model to predict realistic 3D human pose and shape.
• The method leverages multiple loss functions and minimal 3D data, achieving accurate reconstruction while reducing reliance on extensive 3D annotations.
• Experimental results show that optimized supervision and detailed part segmentation significantly enhance 3D inference accuracy, enabling practical real-world applications.

Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation

This paper introduces Neural Body Fitting (NBF), an innovative framework integrating deep learning with statistical model-based approaches to enhance human pose and shape estimation from single 2D images. The primary challenge addressed is the high-dimensional problem of predicting 3D body structures amidst inherent perspective ambiguities and limited annotated data. The authors propose an end-to-end differentiable system that effectively leverages both 2D and 3D annotations, delivering competitive outcomes on standard benchmarks.

Methodology Overview

NBF bridges the traditional model-based methods with recent advancements in deep learning through a hybrid CNN architecture. The approach autonomously predicts 3D human model parameters by integrating a semantic body part segmentation process. Key highlights include:

• Semantic Segmentation: Utilizing a color-coded map, human body segments are classified, providing an explicit intermediate representation that contributes to reducing the dimensionality and improving 3D inference.
• Integration with SMPL: The Statistical Model of Human Pose and Shape (SMPL) is embedded within the CNN to produce realistic 3D meshes. This includes considerations of body part orientations and anthropomorphic constraints, making the model valuable for applications like character animation and biomechanics.
• Loss Functions: The model employs multiple loss functions, including 2D projection space losses and 3D parameter space losses, accommodating various supervisory data types.

Experimental Findings

The authors conduct exhaustive experiments evaluating the impact of their design choices. Key results indicate:

• Efficiency of Part Segmentation: A 12-part segmentation offers a robust balance by abstracting irrelevant image information while retaining detailed pose information. The experiments suggest that segmentation quality strongly impacts 3D reconstruction accuracy.
• Minimal 3D Data Requirement: The system demonstrates that competitive performance can be achieved with a small fraction of 3D annotated data, utilizing the UP-3D dataset effectively. This finding spotlights the potential for cost-efficient data acquisition.
• Impact of Supervision: Variations in supervision strategies reveal that incorporating some 3D annotations significantly boosts accuracy without fully relying on them.

Implications and Future Directions

The research proposes a scalable methodology that adjusts to limited 3D data availability, underlining the practicality of the framework for real-world deployment. The findings extend theoretical implications, suggesting that intermediate representations and hybrid architectures can substantially enhance model generalization and training efficiency.

Future prospects might include exploring more complex scenes involving multiple articulated bodies and occlusions. Broader applications within virtual reality or advanced biomechanics could be explored, with further optimization for diverse environments and realities.

In conclusion, NBF provides a comprehensive system integrating the robustness of statistical models with the adaptive learning capabilities of CNNs, marking a notable contribution to the field of human pose estimation. The insights obtained from its evaluation endorse its efficacy and adaptability, meeting the demands of modern computational assistance in 3D human modeling and associated applications.