2025/06/05 by Wenfeng Lin, Lin, Wenfeng, Renjie Chen +22 · 2 citations
Computer Science · #Multimodal Machine Learning Applications #Generative Adversarial Networks and Image Synthesis #Domain Adaptation and Few-Shot Learning
paper · pdf · doi:10.48550/arxiv.2506.05343
Recent advances in video generation demand increasingly efficient training recipes to mitigate escalating computational costs. In this report, we present ContentV, an 8B-parameter text-to-video model that achieves state-of-the-art performance (85.14 on VBench) after training on 256 x 64GB Neural Processing Units (NPUs) for merely four weeks. ContentV generates diverse, high-quality videos across multiple resolutions and durations from text prompts, enabled by three key innovations: (1) A minimalist architecture that maximizes reuse of pre-trained image generation models for video generation; (2) A systematic multi-stage training strategy leveraging flow matching for enhanced efficiency; and (3) A cost-effective reinforcement learning with human feedback framework that improves generation quality without requiring additional human annotations. All the code and models are available at: https://contentv.github.io.