Table 3. Text-to-video generation results (zero-shot) on MSR-
VTT [47].

CLIPSIM (↑)

video frames and the corresponding text. We benchmarked
GPT4Video against four text-to-video specialized models:
CogVideo [14], MakeVideo [37], Latent-VDM [35], and
Latent-Shift [2], as well as two LLM-based methods, in-
cluding CoDi [38] and NExt-GPT [45]. The results, pre-
sented in Table 3, demonstrate that GPT4Video outper-
forms the aforementioned models across all evaluated met-
rics. It is noteworthy that while NExt-GPT integrates Zero-
Scope as the video decoder in its video generation process,
GPT4Video demonstrates enhanced precision in generating
video content. This observation reinforces the claims we
made in the introduction 1. For qualitative comparison re-
sults, please refer to Figure 4.
4.3. Safety Evaluation
GPT4Video