FolAI: Synchronized Foley Sound Generation with Semantic and Temporal Alignment

TL;DR

FolAI introduces a two-stage generative framework that automates Foley sound creation by aligning audio with video motion and user-defined semantics, enhancing efficiency and creative control.

Contribution

The paper presents a novel modular approach combining temporal structure estimation and diffusion-based semantic sound generation for synchronized video-audio Foley synthesis.

Findings

Reliable temporal alignment with visual motion

Semantic consistency with user input

Perceptually realistic audio outputs

Abstract

Traditional sound design workflows rely on manual alignment of audio events to visual cues, as in Foley sound design, where everyday actions like footsteps or object interactions are recreated to match the on-screen motion. This process is time-consuming, difficult to scale, and lacks automation tools that preserve creative intent. Despite recent advances in vision-to-audio generation, producing temporally coherent and semantically controllable sound effects from video remains a major challenge. To address these limitations, we introduce FolAI, a two-stage generative framework that decouples the when and the what of sound synthesis, i.e., the temporal structure extraction and the semantically guided generation, respectively. In the first stage, we estimate a smooth control signal from the video that captures the motion intensity and rhythmic structure over time, serving as a temporal scaffold for the audio. In the second stage, a diffusion-based generative model produces sound effects conditioned both on this temporal envelope and on high-level semantic embeddings, provided by the user, that define the desired auditory content (e.g., material or action type). This modular design enables precise control over both timing and timbre, streamlining repetitive tasks while preserving creative flexibility in professional Foley workflows. Results on diverse visual contexts, such as footstep generation and action-specific sonorization, demonstrate that our model reliably produces audio that is temporally aligned with visual motion, semantically consistent with user intent, and perceptually realistic. These findings highlight the potential of FolAI as a controllable and modular solution for scalable, high-quality Foley sound synthesis in professional and interactive settings. Supplementary materials are accessible on our dedicated demo page at https://ispamm.github.io/FolAI.