Learning to Estimate 3D Human Pose and Shape from a Single Color Image (1805.04092v1)

Abstract: This work addresses the problem of estimating the full body 3D human pose and shape from a single color image. This is a task where iterative optimization-based solutions have typically prevailed, while Convolutional Networks (ConvNets) have suffered because of the lack of training data and their low resolution 3D predictions. Our work aims to bridge this gap and proposes an efficient and effective direct prediction method based on ConvNets. Central part to our approach is the incorporation of a parametric statistical body shape model (SMPL) within our end-to-end framework. This allows us to get very detailed 3D mesh results, while requiring estimation only of a small number of parameters, making it friendly for direct network prediction. Interestingly, we demonstrate that these parameters can be predicted reliably only from 2D keypoints and masks. These are typical outputs of generic 2D human analysis ConvNets, allowing us to relax the massive requirement that images with 3D shape ground truth are available for training. Simultaneously, by maintaining differentiability, at training time we generate the 3D mesh from the estimated parameters and optimize explicitly for the surface using a 3D per-vertex loss. Finally, a differentiable renderer is employed to project the 3D mesh to the image, which enables further refinement of the network, by optimizing for the consistency of the projection with 2D annotations (i.e., 2D keypoints or masks). The proposed approach outperforms previous baselines on this task and offers an attractive solution for direct prediction of 3D shape from a single color image.

• The paper introduces an end-to-end ConvNet framework that integrates the SMPL model for direct 3D human pose and shape prediction from 2D data.
• The approach leverages 2D keypoints, silhouettes, and differentiable rendering to bypass the need for extensive 3D ground truth annotations.
• Empirical results demonstrate lower mean per-vertex error and faster inference, significantly outperforming traditional iterative optimization methods.

Estimating 3D Human Pose and Shape from a Single Image: A Technical Summary

The paper "Learning to Estimate 3D Human Pose and Shape from a Single Color Image" by Pavlakos et al. presents an innovative approach for estimating the full body 3D human pose and shape from a single color image using Convolutional Networks (ConvNets). This research is significant given the historical reliance on iterative optimization-based methods for this task, which were typically constrained by the need for 3D ground truth data and computational inefficiency.

Methodology

The paper proposes an end-to-end framework integrating a statistical body shape model, SMPL, within a ConvNet architecture. The core of this framework involves leveraging SMPL to ensure efficient and effective direct predictions. SMPL allows the model to generate high-quality 3D meshes by estimating a manageable number of parameters, thus making the task suitable for ConvNet prediction.

Key Features of the Approach:

1. Parametric Model Integration: The use of the SMPL model enables detailed 3D mesh predictions from a small set of parameters, enhancing the feasibility of direct 3D predictions from 2D data.
2. Use of 2D Annotations: The framework predicts 3D parameters from 2D keypoints and silhouettes. This utilization circumvents the necessity for extensive 3D-labeled training datasets, relying instead on abundant 2D pose datasets.
3. Differentiable Renderer: Incorporates a differentiable renderer to project 3D mesh onto the image plane, allowing optimization based on 2D annotations, thereby refining the network.

Empirical Evaluation

The proposed approach outperformed existing methods on multiple datasets, including UP-3D and SURREAL, as well as specific benchmarks like Human3.6M. Quantitative results demonstrate superior mean per-vertex error compared to previous direct prediction and iterative optimization techniques, with notable efficiency gains—running at approximately 50ms per image on a GPU.

Implications and Future Directions

The inclusion of SMPL and the adoption of a differentiable renderable model push the boundaries of what is achievable with ConvNets in human pose estimation. This work not only advances the performance of direct prediction models but also offers a path to accelerating and improving optimization-based approaches by offering robust initializations.

Potential Future Work:

• Robustness in Diverse Conditions: Enhancing model robustness under varying imaging conditions and poses could further improve real-world applicability.
• Integration with Other AI Models: Exploring hybrid models that synergize with other deep learning architectures for multi-modal human understanding.
• Dataset Expansion: Increasing the dataset diversity, particularly for unseen poses and shapes, could enhance the model’s generalization.

In summary, the research demonstrates an advanced, efficient methodology for estimating 3D human pose and shape, paving the way for more scalable and effective applications in computer vision and related domains.