MotiF: Making Text Count in Image Animation with Motion Focal Loss

• URL: http://arxiv.org/abs/2412.16153v1
• Date: Fri, 20 Dec 2024 18:57:06 GMT
• Title: MotiF: Making Text Count in Image Animation with Motion Focal Loss
• Authors: Shijie Wang, Samaneh Azadi, Rohit Girdhar, Saketh Rambhatla, Chen Sun, Xi Yin,
• Abstract summary: Text-Image-to-Video (TI2V) generation aims to generate a video from an image following a text description. We introduce MotiF, a simple yet effective approach that directs the model's learning to the regions with more motion. We use optical flow to generate a motion heatmap and weight the loss according to the intensity of the motion.
• Score: 27.950633331625284
• License:
• Abstract: 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/.

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