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         <h2 class="title is-3 is-centered">Abstract</h2>
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-            We propose a novel text-to-video (T2V) generation benchmark, <i>ChronoMagic-Bench</i><sup>1</sup>, to evaluate the temporal 
-            and metamorphic capabilities of the T2V models (e.g., Sora<sup>2</sup> and Lumiere<sup>3</sup>) in time-lapse video generation. 
-            In contrast to existing benchmarks that focus on visual quality and textual relevance of generated videos, <i>ChronoMagic-Bench</i> 
-            focuses on the models’ 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, <i>ChronoMagic-Bench</i> introduces <strong>1,649</strong> 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 ensures a comprehensive evaluation of the models’ 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 <i>ChronoMagic-Bench</i>, 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 <i>ChronoMagic-Pro</i> 
-            dataset, containing <strong>460k</strong> high-quality pairs of 720p time-lapse videos and detailed captions. Each caption ensures high 
-            physical pertinence and large metamorphic amplitude, which have a far-reaching impact on the T2V generation community.
+            We propose a novel text-to-video (T2V) generation benchmark, <i>ChronoMagic-Bench</i>, to evaluate the temporal and metamorphic 
+            knowledge skills in time-lapse video generation of the T2V models (e.g. Sora and Lumiere). Compared to existing benchmarks that 
+            focus on visual quality and text relevance of generated videos, <i>ChronoMagic-Bench</i> focuses on the models’ 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 control. For these purposes, <i>ChronoMagic-Bench</i> introduces <b>1,649</b> 
+            prompts and real-world videos as references, categorized into four major types of time-lapse videos: biological, human creation, 
+            meteorological, and physical phenomena, which are further divided into 75 subcategories. This categorization ensures a comprehensive 
+            evaluation of the models’ capacity to handle diverse and complex transformations. To accurately align human preference on 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 <i>ChronoMagic-Bench</i>, we conduct comprehensive manual 
+            evaluations of eighteen representative T2V models, revealing their strengths and weaknesses across different categories of prompts, 
+            providing a thorough evaluation framework that addresses current gaps in video generation research. More encouragingly, we create a 
+            large-scale <i>ChronoMagic-Pro</i> dataset, containing <b>460k</b> high-quality pairs of 720p time-lapse videos and detailed captions. 
+            Each caption ensures high physical content and large metamorphic amplitude, which have a far-reaching impact on the video generation community.
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