Industry experts at the Shanghai International Film Festival identified compute power, distribution, and directable generative video as the primary obstacles to AI adoption [1].
These challenges represent a significant bottleneck for the Chinese film industry as it attempts to integrate generative AI into large-scale professional workflows. Without controllable tools and sufficient hardware, the transition from experimental AI clips to full-length feature films remains difficult [2].
During the SIFFORUM panel titled "Smart Tech, Immersive Worlds, The Next Film Revolution," speakers discussed the gap between current AI capabilities and the needs of professional directors [1]. Yan Yijun, the vice president of AI foundational model builder MiniMax, was among the participants who addressed these systemic limitations [2].
One primary concern is the lack of directable AI. Current generative video tools often produce unpredictable results, which conflicts with the precise control required for cinematography and storytelling [1]. Filmmakers need the ability to dictate specific movements, lighting, and character consistency across scenes, a level of precision that current models have not yet mastered [2].
Compute power remains a critical infrastructure gap. The high demand for processing power to render complex, high-resolution AI video requires hardware resources that are not yet widely accessible to all production houses [1]. This scarcity limits the speed of iteration and the ability to scale AI tools across an entire production pipeline [2].
Distribution networks also present a hurdle. The fragmented nature of how content is delivered and managed in the region complicates the rollout of AI-driven distribution strategies [1]. These logistical barriers, combined with technical shortcomings, create a tiered environment where only the most resource-rich studios can fully leverage emerging technology [2].
“Compute power, distribution, and directable generative video AI are identified as the biggest challenges.”
The transition of AI from a novelty tool to a production standard in China depends on shifting from 'prompt-based' generation to 'director-based' control. While the creative appetite for AI is high, the industry's growth is currently tethered to physical infrastructure—specifically GPU availability and compute clusters—and the development of more granular control interfaces for video generation.
