A Survey of Interactive Generative Video (2504.21853v1)

Abstract: Interactive Generative Video (IGV) has emerged as a crucial technology in response to the growing demand for high-quality, interactive video content across various domains. In this paper, we define IGV as a technology that combines generative capabilities to produce diverse high-quality video content with interactive features that enable user engagement through control signals and responsive feedback. We survey the current landscape of IGV applications, focusing on three major domains: 1) gaming, where IGV enables infinite exploration in virtual worlds; 2) embodied AI, where IGV serves as a physics-aware environment synthesizer for training agents in multimodal interaction with dynamically evolving scenes; and 3) autonomous driving, where IGV provides closed-loop simulation capabilities for safety-critical testing and validation. To guide future development, we propose a comprehensive framework that decomposes an ideal IGV system into five essential modules: Generation, Control, Memory, Dynamics, and Intelligence. Furthermore, we systematically analyze the technical challenges and future directions in realizing each component for an ideal IGV system, such as achieving real-time generation, enabling open-domain control, maintaining long-term coherence, simulating accurate physics, and integrating causal reasoning. We believe that this systematic analysis will facilitate future research and development in the field of IGV, ultimately advancing the technology toward more sophisticated and practical applications.

A Survey of Interactive Generative Video

Interactive Generative Video (IGV) is a burgeoning domain that integrates video generation with interactive features, catering to the demand for high-quality, interactive content in various sectors. The paper "A Survey of Interactive Generative Video" systematically explores IGV, delineating its applications in gaming, embodied AI, and autonomous driving, and presenting a blueprint for future research directions.

IGV is characterized by its ability to produce varied and high-quality video content through generative models, while also allowing user engagement via control signals and immediate feedback. This technology enables practical applications across domains by combining anticipation of user needs with dynamic adaptability.

Applications of IGV

1. Gaming:

IGV enables the creation of expansive virtual worlds where content evolves and personalizes itself based on player interactions, significantly reducing development costs by automating asset creation. Instances like Oasis and WHAM exemplify early application stages, setting a precedent for generating infinitely explorable and personalized game landscapes.

2. Embodied AI:

IGV enhances AI systems by acting as physics-aware synthetic environments crucial for training robotics and related agents. It overcomes data scarcity by offering diverse synthetic scenarios, improving agents' ability to generalize across diverse tasks and environments.

3. Autonomous Driving:

In this domain, IGV aids in the simulation of complex real-world scenarios. It allows comprehensive safety testing and validation through high-fidelity video simulations, offering a risk-free platform for developing and validating autonomous systems.

Framework for IGV

The paper proposes a modular framework to facilitate IGV's development, consisting of five critical components:

• Generation: Focuses on real-time, high-fidelity video content creation. Key challenges include achieving high frame rates and ensuring temporal coherence.
• Control: Ensures precise and intuitive user interaction with virtual environments through multi-modal interfaces.
• Memory: Maintains consistency across generated video content over time, tackling issues related to static and dynamic coherence.
• Dynamics: Simulates accurate physical phenomena and supports parametric control settings, facilitating realistic interactions in simulations.
• Intelligence: Develops advanced reasoning to support causality and predictive modeling within virtual environments.

Technical Challenges and Future Directions

• Real-time Performance: Achieving seamless user interaction requires overcoming computational limitations inherent in video generation models. Techniques such as model compression and optimized algorithms for diffusion processes show promise.
• Control Flexibility: Future IGV systems must enable open-domain control, allowing new and unseen interactions without sacrificing precision or responsiveness.
• Enhanced Memory and Dynamics: Advances in architecture must ensure memory components provide consistent context over extended sequences, while enhancing dynamics to accurately model complex physical systems.
• Integrated Intelligence: Intelligent, self-evolving digital realms require rigorous development in causal reasoning and autonomous adaptation capabilities, potentially realizing persistent, evolving virtual worlds akin to those depicted in speculative fiction.

This paper outlines the current landscape of IGV, describing both existing capabilities and areas ripe for innovation. Through detailed examination of technological frameworks and potential applications, it sets the stage for further investigations that could profoundly impact both digital and real-world domains by leveraging IGV's potential. The survey underscores the necessity for continued exploration in multi-modal integration, open-domain adaptability, computational efficiency, and reasoning capabilities.

In essence, IGV stands as a pivotal technology with the potential to redefine interactions across domains, from revolutionizing gaming content to enhancing autonomous systems and beyond. As research in this area progresses, it holds the promise to cultivate ever-more dynamic and responsive digital environments.