# SAVGBench: Benchmarking Spatially Aligned Audio-Video Generation

arXiv: https://arxiv.org/abs/2412.13462 · HTML: https://mlsanity.com/paper/2412.13462/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2024.

## Built on by (most cited first, 10 of 10)

- [PhyAVBench: A Challenging Audio Physics-Sensitivity Benchmark for Physically Grounded Text-to-Audio-Video Generation](https://mlsanity.com/paper/2512.23994/) (2025)
- [Do Joint Audio-Video Generation Models Understand Physics?](https://mlsanity.com/paper/2605.07061/) (2026)
- [Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification](https://mlsanity.com/paper/2507.12042/) (2025)
- [AudioMotionBench: Evaluating Auditory Motion Perception in Audio LLMs](https://mlsanity.com/paper/2511.13273/) (2025)
- [Integrating Spatial and Semantic Embeddings for Stereo Sound Event Localization in Videos](https://mlsanity.com/paper/2509.06598/) (2025)
- [The Attention Triangle in Audio-Video Models](https://mlsanity.com/paper/2609.03586/) (2026)
- [StereoSync: Spatially-Aware Stereo Audio Generation from Video](https://mlsanity.com/paper/2510.05828/) (2025)
- [ToS: A Team of Specialists ensemble framework for Stereo Sound Event Localization and Detection with distance estimation in Video](https://mlsanity.com/paper/2601.17611/) (2026)
- [Joint and Cross-Modal Video-Audio Generation and Editing: A Unified Formulation and Design Taxonomy](https://mlsanity.com/paper/2609.34381/) (2026)
- [Hear the World in Stereo: Learning Dynamic Spatial Correspondence for Immersive Joint Video-Audio Generation](https://mlsanity.com/paper/2609.38748/) (2026)

## Abstract (arXiv)

This work addresses the lack of multimodal generative models capable of producing high-quality videos with spatially aligned audio. While recent advancements in generative models have been successful in video generation, they often overlook the spatial alignment between audio and visuals, which is essential for immersive experiences. To tackle this problem, we establish a new research direction in benchmarking the Spatially Aligned Audio-Video Generation (SAVG) task. We introduce a spatially aligned audio-visual dataset, whose audio and video data are curated based on whether sound events are onscreen or not. We also propose a new alignment metric that aims to evaluate the spatial alignment between audio and video. Then, using the dataset and metric, we benchmark two types of baseline methods: one is based on a joint audio-video generation model, and the other is a two-stage method that combines a video generation model and a video-to-audio generation model. Our experimental results demonstrate that gaps exist between the baseline methods and the ground truth in terms of video and audio quality, as well as spatial alignment between the two modalities.