生成式视频技术视域下影视与游戏行业生产 方式的转变研究

Research on the Transformation of Production Modes in the Film, Television and Game Industries from the Perspective of Generative Video Technology

摘要: 近年来，生成式人工智能技术正深刻重塑影视与游戏等文化创意行业的生产逻辑。区别于传统数字工具仅完成剪辑、合成等辅助任务，生成式视频技术已深入参与创意构思、视觉预演、数字资产生成、叙事组织和内容分发，推动生产方式发生根本性转变。本文从生产方式变革的视角出发，分析其对影视与游戏行业流程结构、创作主体和产业生态中的多重影响。研究发现，生成式视频技术促使内容生产从“执行导向”转向“生成导向”，在影视领域，体现为数据评估、全程可视化、虚拟制片和多线程协同的加速融合；在游戏领域，则表现为与程序化内容生成、实时交互动态叙事系统的深度整合。然而，这一技术进程同时也伴生着主体性弱化、版权归属模糊、算法规训和虚假影像传播等结构性问题。有鉴于此，未来应在人机协同框架下，通过完善人工决策节点、规范数据与版权管理、推行内容标识制度及构建多方协同治理机制，在提升技术效率的同时维护艺术价值的核心地位。

Abstract: In recent years, generative artificial intelligence has been profoundly reshaping the production logic of cultural and creative industries, particularly film, television, and games. Unlike traditional digital tools that mainly perform auxiliary tasks such as editing and compositing, generative video technology has become deeply involved in creative ideation, visual previsualization, digital asset generation, narrative organization, and content distribution, thereby driving a fundamental transformation in production modes. From the perspective of production transformation, this paper examines the multiple impacts of generative video technology on process structures, creative subjects, and industrial ecosystems in the film, television, and game industries. The study finds that generative video technology is shifting content production from an “execution-oriented” model to a “generation-oriented” model. In the film and television industry, this transformation is reflected in the accelerated integration of data-based evaluation, full-process visualization, virtual production, and multi-threaded collaboration; in the game industry, it is manifested in the deep integration of procedural content generation, real-time interaction, and dynamic narrative systems. However, this technological process also gives rise to structural problems, including the weakening of creative subjectivity, ambiguity in copyright ownership, algorithmic discipline, and the dissemination of synthetic or misleading images. Therefore, future development should be grounded in a human-AI collaborative framework. By improving human decision-making mechanisms, standardizing data and copyright management, implementing content labeling systems, and constructing multi-stakeholder governance mechanisms, it is possible to enhance technological efficiency while maintaining the central position of artistic value.