VideoJAM is an innovative video generation framework aimed at improving the motion coherence and visual quality of video generation models through joint appearance-motion representation. This technique introduces an inner-guidance mechanism that dynamically uses the model's own predicted motion signals to guide video generation effectively, especially in generating complex motion types. The primary advantages of VideoJAM include significantly enhanced motion coherence while maintaining high visual quality, requiring no substantial modifications to training data or model architecture, making it applicable to any video generation model. This technology holds significant application potential in the field of video generation, particularly in scenarios that necessitate high motion coherence.