AmbientNova: Temporally-Aligned Ambient Audio Conditioned Video Animation
Video Results
Baseline
ocean-noise (fixed category, 2s)
AmbientNova (Ours)
Prompt: evening beach with gentle ocean noise
✨ Learns finer details, learns to "erase" obstructions.
Baseline
Prompt: 'wind-noise' (fixed category, 2s)
AmbientNova (Ours)
Prompt: desert dunes with gentle breeze
✨ More motion intelligence and context understanding.
Baseline
Prompt: 'wind noise' (fixed category, 2s)
AmbientNova (Ours)
Prompt: Forest sounds daytime
✨ Captures rich forest ambience with natural motion.
AmbientNova
Prompt: rain with thunderstorm sounds
✨ Rain droplets and the background responds to storm/cloudburst!
AmbientNova
Prompt: clicking combined with typing ambience
✨ Notice how hands hit the keys!
Abstract
Recent advances in latent diffusion modeling have enabled high-fidelity video generation from diverse conditioning signals—text, audio, or even zero-shot synthesis. Yet a critical gap persists: while ambient sounds encode rich temporal narratives, current models reduce these dynamic soundscapes to static semantic prompts, generating mere seconds of loosely-coupled visuals.
Current methods, limited to a response time of at most 2 seconds, cannot capture the extended temporal dependencies inherent in ambient phenomena, such as rainfall intensifying or office visual like keyboard typing.
We present the novel framework for long-form (15-60s) ambient video generation with precise frame-level audio-visual synchronization, transforming ambient soundscapes into coherent visual narratives that maintain both semantic consistency and fine-grained temporal alignment throughout their duration. Our end-to-end training framework for audio-conditioned video generation is compatible with existing latent diffusion models (LDM). Our approach introduces three key technical contributions: Enchanced multimodal conditioning, fine-grained temporal alignment, and an adapted latent diffusion architecture for long-form generation.
Methodology
Approach Comparison: Our hierarchical approach vs. baseline methods
Data Processing Pipeline: Dataset parsing and preprocessing workflow
Hierarchical Multimodal Conditioning: Joint audio-text encoding for temporal alignment
Temporal Sync Classifier: Frame-level audio-visual synchronization mechanism
Overall Architecture: End-to-end framework for audio-conditioned video generation
Results Summary
Comprehensive overview of our qualitative and quantitative performance across multiple ambient video generation scenarios, demonstrating superior temporal alignment and visual quality.
BibTeX
@article{singh2025ambientnova,
title={AmbientNova: Joint Audio-Text Conditioning for Temporally-Aligned Video Animation},
author={Singh, Esha, Dhrif Hassen},
journal={TBA},
year={2025},
note={Work in progress}
}