CelebV-Text: A Large-Scale Facial Text-Video Dataset

arXiv 2023 32.5 benchmark

TLDR

Introduces CelebV-Text, a large-scale facial text-video dataset with 70,000 clips and 20 text descriptions each, enabling face-centric text-to-video generation research.

Reasoning

The paper's strength is providing a large, high-quality, diverse dataset with semi-automatically generated text and a benchmark, addressing a gap in facial text-to-video generation. Weakness is that the abstract focuses on dataset construction and statistics rather than novel generation methods, and no downstream world-model capabilities are demonstrated.

Read-first score

Read-first score 32.5, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 1.

Recency 8%

65.1

Uses a gentle age decay so recent papers surface without erasing older foundations. 2023

Methodology quality 25%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=analysis,benchmark,dataset,evaluation

Reproducibility 25%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=dataset,github

Topical relevance 42%

1.4

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Field roles

Candidate

Rank sensitivity

Stability: volatile; rank range: 350.

Keyword Scores

video world model

world model

world simulator

generative world model

interactive world model

world dynamics prediction

model-based reinforcement learning world model

Resources

Paper