SSM Meets Video Diffusion Models: Efficient Long-Term Video Generation with Structured State Spaces
arXiv:2403.07711v5 Announce Type: replace-cross Abstract: Given the remarkable achievements in image generation through diffusion models, the research community has shown increasing interest in extending these models to video generation. Recent diffusion models for video generation have predominantly utilized attention layers to extract temporal features. However, attention layers are limited by their computational costs, which increase quadratically with the sequence length. This limitation presents significant challenges when generating longer video sequences using diffusion models. To overc
The increasing demand for more efficient and longer video generation models is driving innovation beyond the limitations of traditional attention-based architectures.
This development addresses a fundamental computational bottleneck in AI, potentially enabling the generation of much longer, higher-fidelity video content which has implications across media, simulation, and data synthesis.
The adoption of Structured State Spaces (SSMs) offers a more scalable approach to handling long-sequence dependencies in video, fundamentally altering how video diffusion models are designed and their performance ceiling.
- · AI research labs
- · Content creation platforms
- · Simulation and gaming industries
- · Companies developing foundation video models
- · Pure attention-based model developers
- · Companies relying on short-form video generation
More efficient and longer video generation becomes feasible for commercial applications.
New applications emerge for AI-generated video, such as automated long-form content production and realistic virtual environment creation.
The ability to generate extended, coherent video sequences could accelerate progress in embodied AI and robotics, by providing rich, synthetic training data.
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Read at arXiv cs.AI