Controllable video generation: A survey

Analysis

Why This Paper Matters

Controllable video generation is a rapidly advancing area with applications in film production, virtual reality, and autonomous driving. This survey addresses the need for a structured overview of the field, which has seen a surge of methods but lacks a unified framework. By systematically categorizing approaches, the paper helps researchers navigate the landscape and identify gaps.

The survey also highlights the importance of open-source models, which democratize access to cutting-edge technology. As video generation becomes more accessible, understanding control mechanisms becomes crucial for practical deployment. This paper provides a timely synthesis that can accelerate progress by clarifying terminology and taxonomies.

Technical Contributions

The paper's main contribution is a taxonomy that organizes controllable video generation methods based on the type of control signal (e.g., text, motion, structure) and the generation backbone (e.g., GANs, diffusion models). It reviews popular open-source models, discussing their architectures and control capabilities. The survey also outlines key challenges such as temporal consistency, fine-grained control, and evaluation metrics.

Key innovations highlighted include:

• Text-to-video generation with fine-grained semantic control.
• Motion transfer and pose-guided generation.
• Structural conditioning using segmentation maps or depth.
• Hybrid approaches combining multiple control signals.

Results

As a survey, the paper does not present new experimental results. Instead, it synthesizes findings from existing literature, offering qualitative comparisons of methods. It notes that diffusion-based approaches currently dominate due to their high quality and flexibility, while GAN-based methods offer efficiency but less controllability. The survey also points out the lack of standardized benchmarks, making direct comparisons difficult.

Significance

This survey is significant as it provides a comprehensive reference that can guide both newcomers and experts. By structuring the field, it facilitates knowledge transfer and helps identify promising research directions. The emphasis on open-source models encourages reproducibility and collaboration. As controllable video generation matures, such surveys will be essential for maintaining a coherent research agenda and for translating advances into practical applications.