Are Pose Estimators Ready for the Open World? STAGE: Synthetic Data Generation Toolkit for Auditing 3D Human Pose Estimators (2408.16536v1)

Abstract: The estimation of 3D human poses from images has progressed tremendously over the last few years as measured on standard benchmarks. However, performance in the open world remains underexplored, as current benchmarks cannot capture its full extent. Especially in safety-critical systems, it is crucial that 3D pose estimators are audited before deployment, and their sensitivity towards single factors or attributes occurring in the operational domain is thoroughly examined. Nevertheless, we currently lack a benchmark that would enable such fine-grained analysis. We thus present STAGE, a GenAI data toolkit for auditing 3D human pose estimators. We enable a text-to-image model to control the 3D human body pose in the generated image. This allows us to create customized annotated data covering a wide range of open-world attributes. We leverage STAGE and generate a series of benchmarks to audit the sensitivity of popular pose estimators towards attributes such as gender, ethnicity, age, clothing, location, and weather. Our results show that the presence of such naturally occurring attributes can cause severe degradation in the performance of pose estimators and leads us to question if they are ready for open-world deployment.

• The paper introduces STAGE, a toolkit that generates controlled synthetic images to systematically audit 3D human pose estimators.
• It leverages ControlNet and SMPL models to produce high-fidelity, diverse 3D pose images for precise evaluation.
• Experimental results reveal that variations in clothing, gender/age, and environmental conditions notably degrade pose estimation accuracy.

Are Pose Estimators Ready for the Open World? STAGE: Synthetic Data Generation Toolkit for Auditing 3D Human Pose Estimators

The paper "Are Pose Estimators Ready for the Open World? STAGE: Synthetic Data Generation Toolkit for Auditing 3D Human Pose Estimators" investigates the robustness of state-of-the-art (SOTA) 3D human pose estimation (HPE) models across a variety of open-world conditions. While substantial progress has been made in 3D HPE as demonstrated by existing benchmarks, the real-world applicability of these models remains under-explored. This work aims to bridge this gap by introducing STAGE, a synthetic data generation toolkit designed to evaluate HPE models under diverse, user-specified conditions.

Introduction and Motivation

3D HPE is pivotal for numerous applications, including autonomous vehicles, robotics, and interactive systems that operate in human-centric environments. The criticality of these applications mandates not only average high accuracy but also robustness to various factors that could influence performance. Current benchmarks, such as Human3.6M and 3DPW, do not fully capture the breadth of real-world conditions. Consequently, there is a need for a systematic and controlled approach to stress-test HPE models against attributes like clothing, gender, age, weather, and location variations.

Methodology: STAGE

STAGE leverages recent advancements in text-to-image generative models to develop a customized benchmark generator. By employing a combination of ControlNet and SMPL models, STAGE ensures that the generated images are both realistic and accurately aligned with specified 3D poses. The primary goal is to create image pairs that differ only by a single attribute, allowing controlled experiments to measure the sensitivity of pose estimators to various attributes.

Pose-Conditioned Image Generation

STAGE builds upon ControlNet, specifically extending it to include controls for 3D rendering of SMPL meshes, detailed depth maps, and semantic encodings. This enables the generation of images that are coherent in their 3D human poses while maintaining a high degree of visual fidelity and diversity. By utilizing a combination of 3D and 2D datasets for training, STAGE achieves superior alignment and quality in the synthesized images.

Benchmark Generation

STAGE provides flexibility by allowing users to define custom prompts that specify attributes such as gender, age, clothing, and scene context. The toolkit uses these prompts to generate synthetic images that can be evaluated by HPE models. The generated benchmarks facilitate an analysis of models’ sensitivity to specific attributes by comparing performance on base images against those with modified attributes.

Evaluation Protocol

To quantify the robustness and sensitivity of HPE models, the paper introduces the concept of Percentage of Degraded Poses (PDP). This metric measures the proportion of poses that exhibit significant errors when subjected to attribute changes. The risk associated with each attribute is then evaluated to understand the extent to which different attributes affect model performance.

Experimental Results

The evaluation covers popular pose estimators like SPIN, PARE, MeTRAbs, PyMAF-X, and SMPLer-X, among others. Key findings indicate:

• Clothing and Texture: Changes in clothing items, especially those covering large portions of the body, and textures significantly impact HPE performance. Models trained on diverse datasets with larger architectures exhibit lower sensitivity to these factors.
• Protected Attributes: Gender and age changes are shown to affect model outputs, highlighting areas for improvement in making models more fair and less biased.
• Location and Weather: Contrary to conventional belief, indoor environments with variable context like restaurants and bars appear more challenging than typical lab settings. Adverse weather conditions such as snow also degrade pose estimation accuracy.

Implications and Future Work

The findings from STAGE have significant theoretical and practical implications. They underscore the necessity of extending HPE models’ training regimes to include a wider range of conditions to enhance robustness in real-world scenarios. Moreover, policymakers and developers of safety-critical systems must consider these sensitivities to ensure reliable deployment.

Future work could explore enhancing the realism and diversity of generated images further, incorporating multiple human subjects, and controlling for more complex environmental elements. As generative models continue to improve, STAGE itself can evolve, providing ever more rigorous standards for benchmarking HPE systems.

Conclusion

STAGE offers a powerful tool for auditing 3D HPE models, providing the means to systematically and empirically evaluate their robustness across a spectrum of open-world attributes. This work lays the groundwork for more informed deployment of pose estimation systems in practical applications, emphasizing the need for thorough real-world validation to ensure safety and fairness. The release of the STAGE toolkit will empower researchers and practitioners to conduct fine-grained evaluations, driving the development of more resilient HPE solutions.