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.