MuseV is a diffusion model-based virtual human video generation framework that supports the generation of unlimited length videos. It utilizes a novel visual conditional parallel denoising approach. It provides a pre-trained virtual human video generation model, supporting functionalities like Image2Video, Text2Image2Video, and Video2Video. MuseV is compatible with the Stable Diffusion ecosystem, including base models, LoRA, ControlNet, and more. It supports multi-reference image techniques such as IPAdapter, ReferenceOnly, ReferenceNet, and IPAdapterFaceID. MuseV's strength lies in its ability to generate high-fidelity, unlimited length videos, specifically targeting the video generation domain.