VideoJAM
V
Videojam
Overview :
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.
Target Users :
VideoJAM is designed for scenes that require high-quality video generation, especially applications demanding high motion coherence, such as film production, animation design, virtual reality, and augmented reality. It assists creators in generating more realistic video content while saving time and costs.
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Use Cases
Generate a video of a skateboarder performing flips in mid-air.
Create a video of a ballet dancer spinning on the surface of a lake.
Generate a video of a panda breakdancing in a neon-lit alley.
Features
Enhance motion coherence in video generation through joint appearance-motion representation
Introduce an inner-guidance mechanism to dynamically guide video generation
Support high-quality generation of complex motion types
Applicable without modifying training data or expanding model size
Significantly improve visual quality and motion coherence in video generation
How to Use
1. Prepare a video generation model that supports VideoJAM.
2. Integrate the VideoJAM framework into the model, extending the training objectives to predict appearance and motion.
3. During the training phase, use joint representation learning for appearance and motion.
4. In the inference phase, enable the inner-guidance mechanism to dynamically guide video generation using predicted motion.
5. Adjust parameters as needed to optimize the generation results.
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