CARI4D is the official CVPR 2026 research codebase for category-agnostic 4D reconstruction of human-object interaction from monocular video. It is fundamentally a research pipeline rather than a production application: raw RGB-D video (primarily BEHAVE, plus HODome/InterCap and custom clips) is processed through a chain of external foundation models - UniDepth for metric depth, NLF/SAM3D for human pose, FoundationPose for object tracking - then aligned, rendered into H5 training files, refined by a learned temporal transformer (CoCoNet), and finally optimized at test time to output temporally consistent SMPL-H body parameters, object 6D pose, and hand contact labels. The repo contains training, inference, preprocessing, and evaluation entry points, but no API/service layer; the runnable scripts are research pipeline drivers.
EasyMocap is a multi-view 3D human motion capture toolbox designed for research and prototyping. It provides end-to-end pipelines from video input to 3D body mesh recovery, including camera calibration, 2D keypoint detection (via OpenPose, HRNet, MediaPipe, YOLO), multi-view association and triangulation, SMPL/SMPL-X body model fitting, and neural body rendering. The architecture is modular: a config-driven factory pattern (via YAML and `load_object`) instantiates pipeline stages (detection, triangulation, fitting, visualization), and a command-line entry point `emc` orchestrates the workflow. The repository also contains annotation tools, a real-time 3D visualization server using Open3D, and neural body research code (NeRF-based) for novel view synthesis. Despite its research character, the core pipeline is structured as a reusable application framework with separate packages for estimators, operations, and visualization. However, the codebase exhibits significant technical debt: hardcoded paths, duplicated modules (e.g., two HRNet implementations), incomplete error handling, mixed Chinese/English comments, and stale vendored dependencies (Eigen 3.3.7, pybind11). There are no unit tests, and many scripts rely on external model checkpoints that may not be accessible. The repository is actively maintained with recent commits, but the code quality reflects a fast-moving research project rather than a production-grade system.