Title: Visual Transformation Telling. Abstract: In this paper, we propose a new visual reasoning task, called Visual Transformation Telling (VTT). Given a series of states (i.e.~images), a machine is required to describe what happened (i.e.~transformation) between every two adjacent states. Different from most existing visual reasoning tasks, which focus on state reasoning, VTT concentrates on transformation reasoning. Moreover, describing the transformation in the form of language is more natural and closer to the real application than the property change way in the previous TVR task. We collect 13,547 samples from two instructional video datasets, i.e.~CrossTask and COIN, and extract desired states and transformation descriptions to form a suitable VTT benchmark dataset. After that, we introduce an end-to-end learning model for VTT, named TTNet. TTNet consists of three components to mimic human's cognition process of reasoning transformation. First, an image encoder, e.g. CLIP, reads content from each image, then a context encoder links the image content together, and at last, a transformation decoder autoregressively generates transformation descriptions between every two adjacent images. This basic version of TTNet is difficult to meet the cognitive challenge of VTT, that is to identify abstract transformations from images with small visual differences, and the descriptive challenge, which asks to describe the transformation consistently. In response to these difficulties, we propose three strategies to improve TTNet. Specifically, TTNet leverages difference features to emphasize small visual gaps, masked transformation model to stress context by forcing attention to neighbor transformations, and auxiliary category and topic classification tasks to make transformations consistent by sharing underlying semantics among representations. We adapt some typical methods from visual storytelling and dense video captioning tasks, considering their similarity with VTT. Our experimental results show that TTNet achieves better performance on transformation reasoning. In addition, our empirical analysis demonstrates the soundness of each module in TTNet, and provides some insight into transformation reasoning.