We propose a novel text-to-video (T2V) generation benchmark, ChronoMagic-Bench, to evaluate the temporal and metamorphic capabilities of the T2V models (e.g. Sora and Lumiere) in time-lapse video generation. In contrast to existing benchmarks that focus on the visual quality and textual relevance of generated videos, ChronoMagic-Bench focuses on the model's ability to generate time-lapse videos with significant metamorphic amplitude and temporal coherence. The benchmark probes T2V models for their physics, biology, and chemistry capabilities, in a free-form text query. For these purposes, ChronoMagic-Bench introduces 1,649 prompts and real-world videos as references, categorized into four major types of time-lapse videos: biological, human-created, meteorological, and physical phenomena, which are further divided into 75 subcategories. This categorization comprehensively evaluates the model's capacity to handle diverse and complex transformations. To accurately align human preference with the benchmark, we introduce two new automatic metrics, MTScore and CHScore, to evaluate the videos' metamorphic attributes and temporal coherence. MTScore measures the metamorphic amplitude, reflecting the degree of change over time, while CHScore assesses the temporal coherence, ensuring the generated videos maintain logical progression and continuity. Based on the ChronoMagic-Bench, we conduct comprehensive manual evaluations of ten representative T2V models, revealing their strengths and weaknesses across different categories of prompts, and providing a thorough evaluation framework that addresses current gaps in video generation research. Moreover, we create a large-scale ChronoMagic-Pro dataset, containing 460k high-quality pairs of 720p time-lapse videos and detailed captions ensuring high physical pertinence and large metamorphic amplitude.

翻译：我们提出了一个新颖的文本到视频（T2V）生成基准——ChronoMagic-Bench，用于评估T2V模型（例如Sora和Lumiere）在延时视频生成中的时间与蜕变能力。与现有主要关注生成视频视觉质量和文本相关性的基准不同，ChronoMagic-Bench侧重于模型生成具有显著蜕变幅度和时间连贯性的延时视频的能力。该基准通过自由形式的文本查询，探究T2V模型在物理、生物和化学方面的能力。为此，ChronoMagic-Bench引入了1,649个提示词和真实世界视频作为参考，这些内容被归类为四大类延时视频：生物现象、人造过程、气象变化和物理现象，并进一步细分为75个子类别。这种分类全面评估了模型处理多样且复杂变化的能力。为了精确地将人类偏好与基准对齐，我们引入了两个新的自动评估指标——MTScore和CHScore，用于评估视频的蜕变属性和时间连贯性。MTScore衡量蜕变幅度，反映随时间变化的程度；而CHScore评估时间连贯性，确保生成的视频保持逻辑上的递进和连续性。基于ChronoMagic-Bench，我们对十个具有代表性的T2V模型进行了全面的人工评估，揭示了它们在不同类别提示下的优势和不足，并提供了一个全面的评估框架，以弥补当前视频生成研究中的空白。此外，我们创建了一个大规模数据集ChronoMagic-Pro，包含46万对高质量的720p延时视频及其详细描述，确保了高度的物理相关性和较大的蜕变幅度。