Text-Image-to-Video (TI2V) generation aims to generate a video from an image following a text description, which is also referred to as text-guided image animation. Most existing methods struggle to generate videos that align well with the text prompts, particularly when motion is specified. To overcome this limitation, we introduce MotiF, a simple yet effective approach that directs the model's learning to the regions with more motion, thereby improving the text alignment and motion generation. We use optical flow to generate a motion heatmap and weight the loss according to the intensity of the motion. This modified objective leads to noticeable improvements and complements existing methods that utilize motion priors as model inputs. Additionally, due to the lack of a diverse benchmark for evaluating TI2V generation, we propose TI2V Bench, a dataset consists of 320 image-text pairs for robust evaluation. We present a human evaluation protocol that asks the annotators to select an overall preference between two videos followed by their justifications. Through a comprehensive evaluation on TI2V Bench, MotiF outperforms nine open-sourced models, achieving an average preference of 72%. The TI2V Bench is released in https://wang-sj16.github.io/motif/.

翻译：文本-图像到视频（TI2V）生成旨在根据文本描述从图像生成视频，这也被称为文本引导的图像动画。大多数现有方法难以生成与文本提示良好对齐的视频，特别是在指定运动时。为了克服这一限制，我们提出了MotiF，这是一种简单而有效的方法，它将模型的学习引导至具有更多运动的区域，从而改善文本对齐和运动生成。我们使用光流生成运动热图，并根据运动强度对损失进行加权。这种改进的目标函数带来了显著的提升，并补充了利用运动先验作为模型输入的现有方法。此外，由于缺乏用于评估TI2V生成的多样化基准，我们提出了TI2V Bench，这是一个包含320个图像-文本对的数据集，用于进行稳健的评估。我们提出了一种人工评估协议，要求标注者在两个视频之间选择整体偏好，并给出其理由。通过在TI2V Bench上的全面评估，MotiF在九个开源模型中表现最佳，获得了72%的平均偏好度。TI2V Bench已在https://wang-sj16.github.io/motif/发布。