VOID is a Netflix research codebase for removing objects from videos while also erasing their physical side effects, such as shadows, reflections, and secondary motion caused by the removed object. The core runtime is a two-pass diffusion inpainting system built on CogVideoX: pass 1 performs coarse object removal guided by a quadmask, and pass 2 refines temporal consistency using warped noise and optical-flow-inspired conditioning. The repo is primarily an application/research library rather than a pure data project: it ships a `videox_fun` Python package with model definitions, custom diffusion pipelines, inference and training scripts, a FastAPI/Ray inference API, Gradio UIs, and a Colab notebook, alongside synthetic data-generation code that uses Blender and Kubric.