Driven by multimodal large language models (MLLMs), significant advancements have been made in tasks related to images and videos, including visual question answering, narrative generation, and interactive editing. However, achieving fine-grained understanding of video content still poses major challenges. These challenges involve tasks such as pixel-level segmentation, tracking with language descriptions, and visual question answering based on specific video prompts. Although current state-of-the-art video perception models excel in segmentation and tracking tasks, they still fall short in open language understanding and conversational capabilities.