Despite recent advances, generated video models still struggle to realistically represent motion. Many existing models focus primarily on pixel-level reconstructions, often leading to inconsistency in motion. These drawbacks become apparent as unrealistic physics, missing frames, or distortions in complex motion sequences. For example, models may struggle to portray dynamic actions such as rotational movements and gymnastics and object interactions. Addressing these issues is essential for AI to improve the realism of video generated by AI, especially as applications expand into creative and professional domains.
Meta AI presents VideoJam, a framework designed to introduce more powerful motion representations in video generation models. By encouraging collaborative appearance motion expression, VideoJam improves consistency of generated movements. Unlike traditional approaches that treat motion as a secondary consideration, VideoJam integrates it directly into both the training and inference processes. This framework can be incorporated into existing models with minimal changes, providing an efficient way to improve motion quality without modifying training data.
Technical approaches and benefits
VideoJam consists of two main components:
Training Phase: Both the input video (X1) and its corresponding motion representation (D1) are exposed to noise and embedded in a single joint latent representation using a linear layer (Win+). The diffusion model handles this representation, with two linear projection layers predicting both appearance and motion components (WOUT+). This structured approach helps balance appearance fidelity and motion coherence, reducing the common trade-offs found in previous models. Inference stage (Internal adjustment mechanism): During inference, VideoJam introduces internal adjustment. Here, the model utilizes its own evolving motion prediction to guide video generation. Unlike traditional techniques that rely on fixed external signals, internal adjustments allow the model to dynamically adjust the motion representation, leading to smoother, more natural transitions between frames.
insight
VideoJam’s ratings show significant improvements in the consistency of movement across different types of videos. The key findings are as follows:
Enhanced Motion Representation: Compared to established models such as Sora and Kling, VideoJam reduces artifacts such as frame distortion and unnatural object transformation. Improved Motion Fidelity: VideoJam consistently achieves a higher motion coherence score for both automated and human ratings. Versatility of the overall model: The framework effectively integrates with a variety of pre-trained video models, demonstrating adaptability without the need for extensive retraining. Efficient Implementation: VideoJam uses only two additional linear layers to improve video quality, making it a lightweight and practical solution.
Conclusion
VideoJam offers a structured approach to improving motion coherence in AI-generated videos by integrating motion as a critical component rather than an afterthought. This framework allows models to generate videos with greater temporal consistency and realism by leveraging collaborative motion representations and internal adjustment mechanisms. (Because minimal architectural changes are required, VideoJam provides a practical way to improve the motion quality of the generated video models, making it more reliable for a variety of applications.
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Aswin AK is a consulting intern at MarkTechPost. He is pursuing a double degree at Haragpur, Indian Institute of Technology. He is passionate about data science and machine learning, bringing academic background and practical experience to solve real-world cross-domain challenges.
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