BlendMimic3D (A Synthetic Dataset for Human Pose Estimation)
Introduced by Lino et al. in 3D Human Pose Estimation with Occlusions: Introducing BlendMimic3D Dataset and GCN Refinement
BlendMimic3D is a pioneering synthetic dataset developed using Blender, designed to enhance Human Pose Estimation (HPE) research. This dataset features diverse scenarios including self-occlusions, object-based occlusions, and out-of-frame occlusions, tailored for the development and testing of advanced HPE models.
Main features:
- Realistic Environments: BlendMimic3D encompasses simple environments, resembling Human3.6M dataset, shopping activities and multi-person contexts, simulating real-world environments.
- Diverse Occlusion Scenarios: Specifically addresses self-occlusions, object-based occlusions, and out-of-frame occlusions.
- Multi-Perspective Capture: Utilizes four cameras to capture diverse human movements and interactions from multiple angles.
- Pixel-Perfect Annotations: Offers detailed annotations for 2D keypoints, 3D keypoints, and occlusion data.
Papers
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