The ROMP (RObust Multi-person Pose) project is structured around a modular deep learning pipeline for 3D human pose and shape estimation. The System Configuration component initializes global settings and parameters, which are then consumed by various parts of the system. The Data Input & Preprocessing component is responsible for preparing diverse datasets, feeding preprocessed data to both the Core Deep Learning Models for training and the Inference & 3D Reconstruction Pipeline for real-time processing. The Core Deep Learning Models house the neural network architectures (ROMP, BEV, TRACE) that perform the core task of feature extraction and pose/shape prediction. During training, the Model Training & Evaluation component orchestrates the learning process, calculating losses, updating model weights, and evaluating performance. For inference, the Inference & 3D Reconstruction Pipeline takes raw model outputs, leverages the 3D Body Model (SMPL) to generate 3D meshes, and, for video inputs, interacts with the Multi-person Tracking component to ensure consistent tracking across frames. Finally, the Results Visualization & Export component handles the rendering of 2D keypoints and 3D meshes, and facilitates the export of results for further analysis or integration with external tools. This architecture ensures a clear separation of concerns, enabling efficient development, training, and deployment of the human pose estimation system.
Components
System Configuration
Manages global settings, logging, and initial parameter loading for the entire ROMP system.
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Data Input & Preprocessing
Handles loading, augmentation, and preparation of image and video datasets, providing standardized inputs for both training and inference.
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Core Deep Learning Models
Encapsulates the main neural network architectures (ROMP, BEV, TRACE) responsible for extracting features and predicting human pose and shape parameters.
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3D Body Model (SMPL)
Manages the SMPL (Skinned Multi-Person Linear) model, fundamental for representing 3D human body shape and pose, and generating 3D meshes.
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Inference & 3D Reconstruction Pipeline
Orchestrates the end-to-end inference process, from raw input to final 3D human pose and shape results, including processing raw model outputs and generating 3D meshes. This component also serves as the primary user API.
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Model Training & Evaluation
Manages the training loops for pre-training and fine-tuning deep learning models, including loss calculation and performance evaluation against ground truth data.
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Multi-person Tracking
Implements algorithms for tracking multiple individuals across video frames and applying temporal optimization techniques to smooth inconsistencies in pose estimations.
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Results Visualization & Export
Handles the rendering of 2D keypoints, 3D meshes, and heatmaps, and provides functionalities to export results to external tools and formats (e.g., Blender).