SenseTime, together with Shanghai AI Lab (S-Lab), Nanyang Technological University, and the Chinese University of Hong Kong, launches SenseNova-Vision on arXiv, redefining computer vision tasks as a "unified multimodal generation" problem. Traditional CV models need separate dedicated prediction heads for different tasks like detection, segmentation, depth estimation, and keypoints, while SenseNova-Vision uses a unified multimodal base, with natural language instructions plus optional visual prompts, directly generating text (symbol output), images (dense spatial prediction), or text-image hybrid (composite tasks). The paper shows that this single model can be benchmarked against dedicated systems on structured visual understanding, dense geometric prediction, segmentation, multi-view geometry, and other tasks. More critically, the team open-sourced SenseNova-Vision Corpus simultaneously — a vision instruction-response corpus spanning text, image, and mixed targets, along with the matching pretrained weights (GitHub: OpenSenseNova/SenseNova-Vision). The CV community can, for the first time, use a base to replace the entire vision toolbox just like using an LLM. The significance of this path for the engineering world is far greater than a single SOTA number: it pushes the "vision capability is text generation" paradigm to engineering-grade, future general bases no longer need to plug in dedicated small models like YOLO, Segment Anything, Depth Anything — a generative base handles it end-to-end. This may be the next watershed for the CV industry shifting from "task-driven" to "generation-driven".