💡 The paper introduces a unified multimodal model that formulates computer vision tasks as generation problems using natural language and visual prompts. This approach allows for a single model to perform a wide range of vision tasks without requiring task-specific architectures. The model, called SenseNova-Vision, uses natural-language instructions and optional visual prompts to specify tasks and generates responses as text, images, or mixed text-and-image outputs. To support large-scale training, the authors created the SenseNova-Vision Corpus, a computer-vision instruction-response corpus that spans text, image, and mixed targets. The model is trained on this corpus, along with auxiliary multimodal data, and achieves performance comparable to specialized systems across diverse vision tasks, including detection, OCR, keypoint estimation, segmentation, and camera pose estimation. The results demonstrate that a single unified model can match leading task-specialized systems, suggesting that unified multimodal generation is a scalable route for integrating computer vision capabilities into general-purpose foundation models. The model and corpus are publicly available, providing a valuable resource for the research community. Overall, the paper presents a significant contribution to the field of computer vision, offering a unified and flexible approach to tackling a wide range of vision tasks.