  Video generation has many unique challenges beyond those of image generation.
The temporal dimension introduces extensive possible variations across frames,
over which consistency and continuity may be violated. In this study, we move
beyond evaluating simple actions and argue that generated videos should
incorporate the emergence of new concepts and their relation transitions like
in real-world videos as time progresses. To assess the Temporal
Compositionality of video generation models, we propose TC-Bench, a benchmark
of meticulously crafted text prompts, corresponding ground truth videos, and
robust evaluation metrics. The prompts articulate the initial and final states
of scenes, effectively reducing ambiguities for frame development and
simplifying the assessment of transition completion. In addition, by collecting
aligned real-world videos corresponding to the prompts, we expand TC-Bench's
applicability from text-conditional models to image-conditional ones that can
perform generative frame interpolation. We also develop new metrics to measure
the completeness of component transitions in generated videos, which
demonstrate significantly higher correlations with human judgments than
existing metrics. Our comprehensive experimental results reveal that most video
generators achieve less than 20% of the compositional changes, highlighting
enormous space for future improvement. Our analysis indicates that current
video generation models struggle to interpret descriptions of compositional
changes and synthesize various components across different time steps.
