arXiv:2410.07194
Technical Report: Competition Solution For Modelscope-Sora
원문 초록
This report presents the approach adopted in the Modelscope-Sora challenge, which focuses on fine-tuning data for video generation models. The challenge evaluates participants' ability to analyze, clean, and generate high-quality datasets for video-based text-to-video tasks under specific computational constraints. The provided methodology involves data processing techniques such as video description generation, filtering, and acceleration. This report outlines the procedures and tools utilized to enhance the quality of training data, ensuring improved performance in text-to-video generation models.
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VCapsBench: A Large-scale Fine-grained Benchmark for Video Caption Quality Evaluation
InfLVG: Reinforce Inference-Time Consistent Long Video Generation with GRPO
LOVE: Benchmarking and Evaluating Text-to-Video Generation and Video-to-Text Interpretation
On-device Sora: Enabling Training-Free Diffusion-based Text-to-Video Generation for Mobile Devices
VPO: Aligning Text-to-Video Generation Models with Prompt Optimization
RecTable: Fast Modeling Tabular Data with Rectified Flow
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