# Sounding Video Generator: A Unified Framework for Text-guided Sounding Video Generation

arXiv: https://arxiv.org/abs/2303.16541 · HTML: https://mlsanity.com/paper/2303.16541/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 66 references that are in the map.
- First posted on arXiv 29 Mar 2023.

## Builds on (most cited first, 30 of 47)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval](https://mlsanity.com/paper/2104.00650/) (2021)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [CogView: Mastering Text-to-Image Generation via Transformers](https://mlsanity.com/paper/2105.13290/) (2021)
- [VideoGPT: Video Generation using VQ-VAE and Transformers](https://mlsanity.com/paper/2104.10157/) (2021)
- [VGGSound: A Large-scale Audio-Visual Dataset](https://mlsanity.com/paper/2004.14368/) (2020)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [MoCoGAN: Decomposing Motion and Content for Video Generation](https://mlsanity.com/paper/1707.04993/) (2017)
- [Generating Videos with Scene Dynamics](https://mlsanity.com/paper/1609.02612/) (2016)
- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [Self-Supervised Learning by Cross-Modal Audio-Video Clustering](https://mlsanity.com/paper/1911.12667/) (2019)
- [The Sound of Pixels](https://mlsanity.com/paper/1804.03160/) (2018)
- [GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions](https://mlsanity.com/paper/2104.14806/) (2021)
- [N"UWA: Visual Synthesis Pre-training for Neural visUal World creAtion](https://mlsanity.com/paper/2111.12417/) (2021)

## Built on by (most cited first, 15 of 15)

- [MovieFactory: Automatic Movie Creation from Text using Large Generative Models for Language and Images](https://mlsanity.com/paper/2306.07257/) (2023)
- [A Simple but Strong Baseline for Sounding Video Generation: Effective Adaptation of Audio and Video Diffusion Models for Joint Generation](https://mlsanity.com/paper/2409.17550/) (2024)
- [A Versatile Diffusion Transformer with Mixture of Noise Levels for Audiovisual Generation](https://mlsanity.com/paper/2405.13762/) (2024)
- [MM-LDM: Multi-Modal Latent Diffusion Model for Sounding Video Generation](https://mlsanity.com/paper/2410.01594/) (2024)
- [Draw an Audio: Leveraging Multi-Instruction for Video-to-Audio Synthesis](https://mlsanity.com/paper/2409.06135/) (2024)
- [GLOBER: Coherent Non-autoregressive Video Generation via GLOBal Guided Video DecodER](https://mlsanity.com/paper/2309.13274/) (2023)
- [InstructAV2AV: Instruction-Guided Audio-Video Joint Editing](https://mlsanity.com/paper/2605.18467/) (2026)
- [MobileVidFactory: Automatic Diffusion-Based Social Media Video Generation for Mobile Devices from Text](https://mlsanity.com/paper/2307.16371/) (2023)
- [Super Encoding Network: Recursive Association of Multi-Modal Encoders for Video Understanding](https://mlsanity.com/paper/2506.07576/) (2025)
- [ProAV-DiT: A Projected Latent Diffusion Transformer for Efficient Synchronized Audio-Video Generation](https://mlsanity.com/paper/2511.12072/) (2025)
- [Training-Free Video Editing via Optical Flow-Enhanced Score Distillation](https://mlsanity.com/paper/2406.04888/) (2024)
- [DiffDesign: Controllable Diffusion with Meta Prior for Efficient Interior Design Generation](https://mlsanity.com/paper/2411.16301/) (2024)
- [TEn-CATG:Text-Enriched Audio-Visual Video Parsing with Multi-Scale Category-Aware Temporal Graph](https://mlsanity.com/paper/2509.04086/) (2025)
- [COMUNI: Decomposing Common and Unique Video Signals for Diffusion-based Video Generation](https://mlsanity.com/paper/2410.01718/) (2024)
- [GroupVideo: Multi-Identity Customized Text-to-Video Generation](https://mlsanity.com/paper/2607.21027/) (2026)

## Abstract (arXiv)

As a combination of visual and audio signals, video is inherently multi-modal. However, existing video generation methods are primarily intended for the synthesis of visual frames, whereas audio signals in realistic videos are disregarded. In this work, we concentrate on a rarely investigated problem of text guided sounding video generation and propose the Sounding Video Generator (SVG), a unified framework for generating realistic videos along with audio signals. Specifically, we present the SVG-VQGAN to transform visual frames and audio melspectrograms into discrete tokens. SVG-VQGAN applies a novel hybrid contrastive learning method to model inter-modal and intra-modal consistency and improve the quantized representations. A cross-modal attention module is employed to extract associated features of visual frames and audio signals for contrastive learning. Then, a Transformer-based decoder is used to model associations between texts, visual frames, and audio signals at token level for auto-regressive sounding video generation. AudioSetCap, a human annotated text-video-audio paired dataset, is produced for training SVG. Experimental results demonstrate the superiority of our method when compared with existing textto-video generation methods as well as audio generation methods on Kinetics and VAS datasets.