ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis (2109.05488v2)

Abstract: Estimating the articulated 3D hand-object pose from a single RGB image is a highly ambiguous and challenging problem, requiring large-scale datasets that contain diverse hand poses, object types, and camera viewpoints. Most real-world datasets lack these diversities. In contrast, data synthesis can easily ensure those diversities separately. However, constructing both valid and diverse hand-object interactions and efficiently learning from the vast synthetic data is still challenging. To address the above issues, we propose ArtiBoost, a lightweight online data enhancement method. ArtiBoost can cover diverse hand-object poses and camera viewpoints through sampling in a Composited hand-object Configuration and Viewpoint space (CCV-space) and can adaptively enrich the current hard-discernable items by loss-feedback and sample re-weighting. ArtiBoost alternatively performs data exploration and synthesis within a learning pipeline, and those synthetic data are blended into real-world source data for training. We apply ArtiBoost on a simple learning baseline network and witness the performance boost on several hand-object benchmarks. Our models and code are available at https://github.com/lixiny/ArtiBoost.

• The paper introduces an online framework that integrates CCV-space mapping and adaptive synthetic data generation for improved 3D hand-object pose estimation.
• It employs a novel grasp synthesis method with contact constraints to overcome the diversity limitations of real-world datasets.
• Extensive evaluation on HO3D and DexYCB demonstrates significant performance gains in both hand pose estimation and object pose metrics.

ArtiBoost: Enhancing Hand-Object Pose Estimation with Online Data Synthesis

The paper "ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis" presents a novel approach to tackling the challenges inherent in estimating articulated 3D hand-object poses from single RGB images. This problem is encumbered by the requirement for large-scale datasets featuring diverse hand poses, object types, and camera viewpoints. Real-world datasets often lack sufficient diversity, making data synthesis an attractive alternative. However, synthesizing valid and diverse hand-object interactions and efficiently utilizing synthetic data for training presents its own set of challenges.

Key Contributions

The proposed method, ArtiBoost, is designed as an online data enhancement framework that operates within a learning pipeline to boost the performance of Hand-Object Pose Estimation (HOPE) tasks. Its key features include:

1. Composited Configuration Viewpoint (CCV) Space: The paper introduces a CCV-space, a three-dimensional discrete space that systematically maps hand-object configurations and camera viewpoints.
2. Adaptive Synthetic Data Generation: ArtiBoost uses a novel grasp synthesis method, incorporating contact constraints to simulate hand-object interactions, thereby addressing the diversity deficit in available real-world datasets.
3. Iterative Learning with Online Feedback: By continuously integrating synthetic data into the training cycle, ArtiBoost adjusts its sampling strategy based on the difficulty of discerning data samples, informed by training loss feedback and sample re-weighting. This dynamic methodology enhances the model’s ability to generalize across a range of pose distributions.
4. Compatibility with Baseline Networks: ArtiBoost is demonstrated to be agnostic to the underlying model architecture, effectively boosting both classification-based and regression-based pose estimation models.

Evaluation and Results

When evaluated on prominent benchmarks such as HO3D and DexYCB, the models integrated with ArtiBoost exhibited superior performance compared to previous state-of-the-art methods. The efficacy of ArtiBoost was validated through comparative experiments and ablation studies that explored the significance of individually addressing grasp synthesis strategies and re-weighting strategies.

Notably, ArtiBoost enhanced hand pose estimation (MPJPE) and object pose estimation metrics (MPCPE and MSSD) significantly, particularly under conditions of augmented datasets that combined minimal real-world data with synthetic data. This empirical evidence supports the claim that diversity in training samples is critical to the success of pose estimation models.

Implications and Future Directions

ArtiBoost’s approach to judiciously sampled synthetic data may influence future research in synthetic data utilization, particularly in fields requiring high-dimensional data diversity. Given its model-agnostic design, ArtiBoost can potentially be adapted to a variety of pose estimation tasks beyond hand-object configurations, thus encouraging more research into adaptable data synthesis methodologies.

Furthermore, the dependency of pose prediction accuracy on effective grasp synthesis accentuates the need for continued exploration in this area. Future efforts might focus on integrating more sophisticated grasp dynamics and refining inter-object interaction models.

By addressing key roadblocks in data diversity and synthesis efficiency through a streamlined feedback mechanism, ArtiBoost sets a precedence for innovative strategies in AI-driven pose estimation tasks. It invites further exploration into its potential adaptations and optimizations to cater to the unique demands of advanced articulated body modeling.