LoCoT2V-Bench: Benchmarking Long-Form and Complex Text-to-Video Generation

Researchers introduce LoCoT2V-Bench, a new benchmark for evaluating long-form video generation from complex text prompts, along with LoCoT2V-Eval, a multi-dimensional evaluation framework. Testing 17 models reveals that while perceptual quality is strong, fine-grained text alignment and character consistency remain major technical challenges in the field.

The release of LoCoT2V-Bench addresses a critical gap in AI video generation research. While short-form text-to-video models have achieved impressive results, evaluating longer, more complex video generation has lacked standardized benchmarks and evaluation methodologies. This benchmark uses real-world video data with hierarchical metadata including character settings and camera behaviors, creating a more realistic testing environment than previous approaches.

The AI video generation field has experienced rapid advancement over the past two years, with models like Runway, Pika, and others capturing significant investor attention. However, production-grade applications require capabilities beyond what current systems deliver. The introduction of LoCoT2V-Eval's five-dimensional evaluation framework—spanning perceptual quality, text-video alignment, temporal consistency, dynamic quality, and Human Expectation Realization Degree—provides developers with granular insights into specific weaknesses.

The benchmark's findings carry significant implications for the industry. The pronounced capability gaps across evaluation dimensions reveal that while models excel at generating visually appealing content and maintaining background coherence, they struggle with prompt faithfulness and maintaining character identity throughout longer sequences. These are precisely the features required for commercial applications in film production, advertising, and content creation.

Looking ahead, this research establishes a measurement standard that will likely accelerate model development by identifying specific areas requiring improvement. Teams building video generation models can now prioritize character consistency and prompt alignment based on quantifiable metrics. The release of code and data at the provided repository ensures broader adoption within the research community and positions this benchmark as a reference standard for evaluating future long-form video generation systems.

• →LoCoT2V-Bench provides the first standardized benchmark for evaluating long-form, complex text-to-video generation using real-world video data.
• →Testing 17 models reveals strong perceptual quality but significant weaknesses in fine-grained text alignment and character consistency across video sequences.
• →The proposed five-dimensional LoCoT2V-Eval framework enables granular assessment of different aspects critical for production-grade video generation systems.
• →Character identity preservation and prompt faithfulness remain the key technical bottlenecks limiting current video generation models from commercial deployment.
• →Open-sourced code and data democratize access to evaluation standards, likely accelerating industry-wide improvements in long-form video generation.