Animating the Past: Reconstruct Trilobite via Video Generation
Xiaoran Wu, Zien Huang, Chonghan Yu

TL;DR
This paper presents a novel automatic text-to-video method for reconstructing trilobite behavior from fossils, enhancing visual realism and consistency to aid scientific research and education.
Contribution
It introduces a prompt learning approach using large language models and reward-based fine-tuning to improve trilobite video generation from fossil images.
Findings
Generated videos show higher realism than baselines
Method effectively captures trilobite visual details
Enhances scientific and educational visualization
Abstract
Paleontology, the study of past life, fundamentally relies on fossils to reconstruct ancient ecosystems and understand evolutionary dynamics. Trilobites, as an important group of extinct marine arthropods, offer valuable insights into Paleozoic environments through their well-preserved fossil records. Reconstructing trilobite behaviour from static fossils will set new standards for dynamic reconstructions in scientific research and education. Despite the potential, current computational methods for this purpose like text-to-video (T2V) face significant challenges, such as maintaining visual realism and consistency, which hinder their application in science contexts. To overcome these obstacles, we introduce an automatic T2V prompt learning method. Within this framework, prompts for a fine-tuned video generation model are generated by a large language model, which is trained using…
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Taxonomy
Topics3D Surveying and Cultural Heritage
MethodsSparse Evolutionary Training
