论文概要
研究领域: NLP 作者: Ziwei Zhou, Zeyuan Lai, Rui Wang 发布时间: 2025-04-10 arXiv: 2504.07857
中文摘要
文本到音视频(T2AV)生成正迅速成为媒体创作的核心接口，但其评估仍然碎片化。现有基准测试主要孤立地评估音频和视频，或依赖粗略的嵌入相似性，无法捕捉真实提示词所需的细粒度联合正确性。我们推出了AVGen-Bench，一个用于T2AV生成的任务驱动基准测试，包含跨11个真实世界类别的高质量提示词。为支持全面评估，我们提出了一个多粒度评估框架，结合轻量级专家模型与多模态大语言模型(MLLM)，实现从感知质量到细粒度语义可控性的评估。我们的评估揭示了强音视频美学与弱语义可靠性之间的显著差距，包括在文本渲染、语音连贯性、物理推理方面的持续失败，以及音乐音高控制的普遍失效。
原文摘要
Text-to-Audio-Video (T2AV) generation is rapidly becoming a core interface for media creation, yet its evaluation remains fragmented. Existing benchmarks largely assess audio and video in isolation or rely on coarse embedding similarity, failing to capture the fine-grained joint correctness required by realistic prompts. We introduce AVGen-Bench, a task-driven benchmark for T2AV generation featuring high-quality prompts across 11 real-world categories. To support comprehensive assessment, we propose a multi-granular evaluation framework that combines lightweight specialist models with Multimodal Large Language Models (MLLMs), enabling evaluation from perceptual quality to fine-grained semantic controllability. Our evaluation reveals a pronounced gap between strong audio-visual aesthetics a...
--- *自动采集于 2026-04-12*
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