MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimation (2007.07227v2)

Abstract: Heatmap representations have formed the basis of human pose estimation systems for many years, and their extension to 3D has been a fruitful line of recent research. This includes 2.5D volumetric heatmaps, whose X and Y axes correspond to image space and Z to metric depth around the subject. To obtain metric-scale predictions, 2.5D methods need a separate post-processing step to resolve scale ambiguity. Further, they cannot localize body joints outside the image boundaries, leading to incomplete estimates for truncated images. To address these limitations, we propose metric-scale truncation-robust (MeTRo) volumetric heatmaps, whose dimensions are all defined in metric 3D space, instead of being aligned with image space. This reinterpretation of heatmap dimensions allows us to directly estimate complete, metric-scale poses without test-time knowledge of distance or relying on anthropometric heuristics, such as bone lengths. To further demonstrate the utility our representation, we present a differentiable combination of our 3D metric-scale heatmaps with 2D image-space ones to estimate absolute 3D pose (our MeTRAbs architecture). We find that supervision via absolute pose loss is crucial for accurate non-root-relative localization. Using a ResNet-50 backbone without further learned layers, we obtain state-of-the-art results on Human3.6M, MPI-INF-3DHP and MuPoTS-3D. Our code will be made publicly available to facilitate further research.

• The paper presents MeTRAbs, a novel approach that eliminates heuristic dependencies by directly estimating absolute 3D poses using truncation-robust heatmaps.
• The method employs a fully-convolutional network to fuse 2D and 3D heatmaps, achieving state-of-the-art MPJPE scores on key benchmarks.
• The approach enhances real-world applicability in fields like augmented reality and human-computer interaction by effectively managing scale ambiguity and image truncation.

Overview of MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimation

This paper discusses the proposal of a novel method, MeTRAbs (Metric-Scale Truncation-Robust Heatmaps), targeting the inherent challenges in absolute 3D human pose estimation. The research pivots around addressing key limitations of existing methodologies, particularly focusing on the problems of scale ambiguity and image truncation in monocular 3D pose estimation tasks.

Key Contributions and Methodology

1. Metric-Scale Truncation-Robust (MeTRo) Heatmaps:
   • Unlike traditional 2.5D heatmaps, which require post-processing to obtain metric-scale predictions, MeTRo heatmaps define all dimensions in metric space. This approach allows for direct estimation of complete metric-scale poses without needing distance information during testing.
   • The framework employs a fully-convolutional network to estimate these heatmaps, enabling it to predict body joints even outside image boundaries, thereby handling truncated images effectively.
2. Differentiable Absolute Pose Estimation:
   • MeTRAbs combines 3D metric-scale heatmaps with 2D image-space heatmaps, enabling the estimation of absolute 3D poses. The approach leverages a differentiable mechanism to combine and supervise both outputs—improving localization accuracy for non-root-relative joints.
   • An essential aspect of this method is the end-to-end backpropagation of the absolute pose loss, fostering better distance estimation compared to prior approaches.
3. Realization of Metric-Scale Predictions without Heuristic Dependencies:
   • The MeTRo representation negates the need for anthropometric heuristics like bone-length assumptions or focal length data at test time, simplifying the overall pipeline significantly.
4. State-of-the-Art Performance:
   • The methodology attains top performance metrics on prominent benchmarks including Human3.6M, MPI-INF-3DHP, and MuPoTS-3D. Noteworthy is the network's achievement in the 2020 ECCV 3D Poses in the Wild Challenge.

Numerical Results and Implications

• The MeTRo framework shows significant improvements in computational efficiency and prediction accuracy. Specifically, the approach achieves state-of-the-art MPJPE scores on widely recognized datasets, underscoring its effective handling of scale and truncation issues.
• The experiments demonstrate MeTRAbs’ robustness across both indoor and outdoor environments, suggesting its utility in diverse, real-world applications—ranging from augmented reality to human-computer interaction.

Future Directions

• The paper hints at possible future research trajectories, including learning scale cues from large-scale outdoor datasets and evaluating performance on data sets featuring more divergent human heights.
• Furthermore, continued refinement could enhance the ability of MeTRo to predict accurate poses in more complex, crowded scenes with significant interactions among multiple subjects.

In summary, the paper introduces a fundamentally solid approach towards enhancing the robustness of monocular 3D human pose estimation. MeTRAbs capitalizes on structural advantages of heatmap representations, tackling the critical challenges of absolute pose estimation efficiently. Through rigorous experiments and transparent processes, the research paves the way for practical advancements in human pose estimation technology.