FoleyGenEx: Unified Video-to-Audio Generation with Multi-Modal Control, Temporal Alignment, and Semantic Precision

arXiv:2606.14049 · cs.SD, cs.CV · Submitted 2026-06-12 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "FoleyGenEx: Unified Video-to-Audio Generation with Multi-Modal Control, Temporal Alignment, and Semantic Precision".

Jane: FoleyGenEx presents a unified Video-to-Audio (VTA) framework designed to achieve synchronized, versatile audio synthesis by integrating multi-modal control, frame-level temporal alignment, and fine-grained semantic precision.

Tom: First, who's behind it and why it matters.

Paper summary: Tom: So, we’ve covered a lot about "FoleyGenEx: Unified Video-to-Audio Generation with Multi-Modal Control, Temporal Alignment, and Semantic Precision," looking at how they've put together this unified framework <ref:2606.14049#pg0>.

Jane: It really boils down to the fact that this paper addresses the limitations of previous VTA methods by solving synchronization issues while simultaneously allowing for fine-grained semantic precision and multi-modal control <ref:2606.14049#pg0>.

Meng: The authors, Shiyao Wang, Xijuan Zeng, Hui Wang, Shiwan Zhao, Feng Deng, Chen Zhang, and Yong Qin from Nankai University's Academy for Advanced Interdisciplinary Studies are definitely pushing the boundaries in this area <ref:2606.14049#pg0>.

Lu: Their work on integrating the MMDiT backbone with the single-modal Diffusion Transformer Block is a really interesting structural choice that dictates how effectively they can model high-fidelity audio across different modalities <ref:2606.14049#pg1>.

Tom: That architectural integration seems to be what allows them to achieve the diverse set of tasks, from basic TTA to complex Audio-Controlled VTA <ref:2606.14049#pg2>.

Jane: From a simpler view, "FoleyGenEx" is essentially a comprehensive toolkit that brings together temporal alignment, multi-modal control capabilities, and semantic detail into one system for video to audio synthesis <ref:2606.14049#pg0>.

Lalam: I think the long-term implication is that this level of integrated generation capability could fundamentally reshape creative industries by making it much easier to produce perfectly synchronized, semantically rich media from video input <ref:2606.14049#pg1>.

Tom: It really does feel like they’ve created a system that moves beyond just generating sound; it’s about generating audio that is intelligently tied to the video content and controllable by text or reference audio <ref:2606.14049#pg0>.

Meng: I'm still focused on the practical aspect—how quickly we can see these capabilities move out of a research environment and into something usable for creators <ref:2606.14049#pg2>.

Jane: We’ve established that this paper presents three key innovations that make this unified framework possible: the conditional injection, the dynamic masking strategy, and the adverb-based augmentation algorithm <ref:2606.14049#pg1>.

Lu: The way they handle the loss function design with LMSEMasked to enforce per-frame accuracy on top of a global objective is a very rigorous approach to ensuring quality in every single synthesized frame <ref:2606.14049#pg1>.

Tom: It’s clear that this paper lays out a solid path forward for building more versatile and precise video to audio synthesis tools, and it gives us a lot of exciting direction for future work <ref:2606.14049#pg0>.

Conclusion: Tom: So, to wrap up this discussion on FoleyGenEx, we’ve seen how these authors managed to combine video input with audio output in such a unified way <ref:2606.14049#pg3>.

Jane: Exactly, and it really boils down to their goal of giving creators a single system that handles synchronization while letting them control the sound with text or other media <ref:2606.14049#pg3>.

Lu: I think the authors managed to build a framework that explicitly separates how they handle the semantic meaning versus how they handle the timing, which is a very clever architectural move <ref:2606.14049#pg3>.

Meng: From my side, I'm still focused on how stable this architecture is when you try to deploy it in a real-world production pipeline for something like film editing <ref:2606.14049#pg3>.

Lalam: I see the implication here as a massive step forward for AI in creative industries because it moves us past just making sound effects and toward creating contextually aware audio experiences <ref:2606.14049#pg3>.

Tom: That’s a huge vision, Lalam, but looking at the title itself, "FoleyGenEx: Unified Video-to-Audio Generation with Multi-Modal Control, Temporal Alignment, and Semantic Precision," it really sums up the ambition of this work <ref:2606.14049#pg3>.

Jane: It tells us that they didn't just solve one problem; they tackled control over meaning, timing, and detail all at once <ref:2606.14049#pg3>.

Lu: The authors clearly set out to address the limitations of previous models by integrating these different control mechanisms into one cohesive structure <ref:2606.14049#pg3>.

Meng: I wonder if the complexity of handling all those different inputs makes it hard for smaller teams to implement this system efficiently on a tight schedule <ref:2606.14049#pg3>.

Lalam: But if we look at how this paper handles the different control paradigms, from TTA to AC-VTA, it suggests a future where audio generation becomes as intuitive as text editing or video scrubbing <ref:2606.14049#pg3>.

Tom: It definitely feels like they’ve laid out a very clear roadmap for how these systems can evolve from research curiosities into actual production tools, and that’s what makes this paper so compelling <ref:2606.14049#pg3>.

Shiyao Wang, Xijuan Zeng, Hui Wang, Shiwan Zhao, Feng Deng, Chen Zhang

Academy for Advanced Interdisciplinary Studies · Kling Team, Kuaishou Technology

cs.SD, cs.CV

Submitted: 2026-06-12

Updated: 2026-10-04

Comments: Accepted by INTERSPEECH 2026 (Long Oral)

Journal ref: INTERSPEECH 2026

Code: https://github.com/hkchengrex/MMAudio

Project page: https://foleygenex.github.io/FoleyGenEx

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 80/100

The gist: FoleyGenEx presents a unified Video-to-Audio (VTA) framework designed to achieve synchronized, versatile audio synthesis by integrating multi-modal control, frame-level temporal alignment, and

Key concepts

MMDiT Architecture
This is the core structure of the model that handles multi-modal features. It uses a single-modal Diffusion Transformer Block for high-quality audio modeling and an MMDiT backbone to fuse information from text, video, and audio inputs effectively.
Conditional Injection Mechanism
This technique allows external cues, like reference audio or specific instructions, to be injected into the model's latent state during training. This is crucial for tasks like Audio-Controlled VTA where the model needs to condition its output based on a specific audio input.
Multi-modal Dynamic Masking Strategy
During training, a large portion of the audio latent is intentionally hidden (masked). This forces the model to learn robust representations that can reconstruct the missing parts accurately, ensuring that training objectives align with how the model will perform during inference.
LMSEMasked Loss
This is a specialized loss function used for training. Instead of averaging errors across all frames, it calculates a per-frame error and then selectively weights these errors based on a random mask. This focuses the learning process on accurately predicting specific parts of the audio at specific times.

Terminology

Summary

FoleyGenEx presents a unified Video-to-Audio (VTA) framework designed to achieve synchronized, versatile audio synthesis by integrating multi-modal control, frame-level temporal alignment, and fine-grained semantic precision. The core contribution is a novel architecture that unifies disparate functionalities—such as Text-to-Audio (TTA), Audio-Controlled VTA (ACVTA), and Foley Extension (FE)—into a single framework, addressing the limitations of existing methods that typically sacrifice one of these key aspects for another.

The gist

FoleyGenEx is an MMDiT-based VTA framework that addresses key limitations of existing methods by separating semantic and synchronization information processing.

Core Innovations and Architecture

FoleyGenEx builds upon the MMDiT architecture, leveraging a single-modal Diffusion Transformer Block for high-fidelity audio modeling via iterative flow-matching, while utilizing an MMDiT backbone for multi-modal feature fusion. The framework incorporates three core innovations:

  1. A conditional injection mechanism for audiocontrolled VTA and Foley extension, which involves processing an InputEmbedding layer that injects conditional cues into the latent state during training and inference. This mechanism is designed to enable tasks like ACVTA where reference audio conditioning is required.

  2. A multi-modal dynamic masking strategy preserving training synchronization, which ensures alignment between training and inference by obscuring 70–100% of the audio latent during training, enforcing consistency between the training objective and inference workflows.

  3. An adverb-based data augmentation algorithm leveraging signal processing and large language models (LLMs) to enhance textual supervision with nuanced semantics, enabling fine-grained control over manner or intensity.

Multi-Modal Feature Processing

The framework processes video, text, and audio modalities through specialized streams:

- Text modality:

Text semantic features are extracted via a CLIP text encoder. Masking is not applied to the text modality because textual descriptors do not maintain a strictly frame-to-frame temporal alignment with audio.

- Video modality:

Visual semantic features are derived from a CLIP visual encoder. Temporal synchronization features are extracted using the Synchformer, which is used for precise alignment and is projected and upsampled for temporal alignment. Furthermore, visual semantics undergo bilinear mapping-based masking to mirror the mask applied to audio latents during inference.

- Audio modality:

Audio latents are extracted using a pre-trained DAC-VAE. The model utilizes an iterative flow-matching process, guided by a surrogate video prepended to the target video to align audio and video durations, while synchronization features are combined with semantic features via an MLP projection to ensure unsynchronized reference segments do not disrupt alignment.

Loss Function Design

FoleyGenEx employs a Masked Mean Squared Error (MSE) loss, denoted as LMSEMasked, which restricts gradient descent to the masked regions of each sample. This is a structural evolution from the baseline MMAudio loss (LMSEMMAudio), which provides a global average over batch, time, and features. The final objective is normalized by the total number of masked frames N:

- LMSEFrame(b,t):

This per-frame loss calculates the difference between the predicted audio and ground truth at a specific frame (b, t).

- LMSEMasked:

The final objective is calculated as:

LMSEMasked = 1/N Σ X B b t=1 LMSEFrame (b,t) × rand span mask(b,t), where N = P B P T rand span mask(b,t).

Multi-Modal Control Paradigms

The FoleyGenEx architecture supports five distinct paradigms of multi-modal controlled audio generation:

  1. Text-to-Audio (TTA): Guided strictly by text semantic features with all other modality streams initialized as all-zero vectors.

  2. Video-to-Audio (VTA): Both semantic and synchronization features of the target video are supplied, with optional textual input that must be semantically consistent with the video content.

  3. Text-Controlled VTA (TC-VTA): Generated by nullifying video semantic features (setting them to zero), guided by a combination of text semantics and video synchronization cues, allowing for semantic-visual decoupling.

  4. Audio-Controlled VTA (AC-VTA): Guided by the reference audio latent integrated through both channel-wise concatenation and residual summation with the initial noise, utilizing a surrogate reference video whose synchronization features are concatenated prior to the target video’s features.

  5. Foley Extension (FE): Guided by target video semantic and synchronization features alongside a reference latent from an existing audio segment, ensuring stylistic and temporal continuity with the original segment.

Improvements for AI systems

As a fastidious and diligent researcher, I have analyzed FoleyGenEx, which represents a significant advancement in Video-to-Audio (VTA) synthesis. To improve existing AI systems using this framework, we should focus on integrating its core innovations—temporal alignment via dynamic masking, multi-modal control via conditional injection, and semantic precision via adverb augmentation—into current generative models.

Here are the specific improvements and capabilities:

) 1. Architectural Integration of MMDiT with Flow Matching (For High-Fidelity VTA):

Improve existing diffusion models or flow-based architectures by replacing standard unimodal transformers with the FoleyGenEx backbone, specifically leveraging the Multi-modal Diffusion Transformer (MMDiT).

The improved system can perform high-fidelity VTA synthesis (e.g., generating realistic sound effects synchronized perfectly to video) while simultaneously handling complex conditioning inputs from text, video semantics, and reference audio in a unified latent space.

) 2. Implementation of Dynamic Masking Strategies for Modality Disentanglement:

Integrate the multi-modal dynamic masking strategy (as seen in Figure 2 and Section 3.1) into training pipelines for cross-modal generation tasks (like TTA or VTA).

The improved system can generate audio that is robust against modal conflicts; specifically, it can synthesize audio based on text semantics while ignoring mismatched visual features (TC-VTA), or generate a style transfer audio conditioned on a reference video and audio track (AC-VTA), ensuring no shortcut biases between modalities.

) 3. Conditional Injection for Reference Audio Conditioning (AC-VTA/FE):

Implement the dedicated conditional injection mechanism using concatenated latents and residual summation during inference, specifically targeting tasks requiring precise acoustic style transfer or Foley extension.

The improved system can perform Audio-Controlled VTA (AC-VTA), generating audio that precisely matches the timbre, prosody, and acoustic events of a reference track (e.g., synthesizing a specific type of metal knocking sound synchronized with an action), overcoming the limitations of models that lack dedicated reference audio conditioning branches.

) 4. Adverb-Based Data Augmentation Pipeline for Fine-Grained Semantic Control:

Develop an automated, four-stage data augmentation pipeline leveraging LLMs to generate adverbial cues (speed, distance, volume) and apply corresponding signal processing augmentations (e.g., speed modulation, reverberation simulation).

The improved system can achieve fine-grained semantic control over generated audio that current models lack. For instance, it can generate a slow knocking sound versus a fast knocking sound by controlling the synthesized speed augmentation during training and using LLM-generated captions to guide the model toward nuanced adverbial descriptors.

) 5. Segment-Focused Optimization via Masked MSE Loss:

Replace global Mean Squared Error (MSE) losses with the per-frame Masked Mean Squared Error (LMSEMasked) loss function.

This ensures superior temporal synchronization by forcing the model to reconstruct audio segments frame-by-frame, leading to significantly lower DeSync scores compared to methods relying solely on global averaging.

This improved AI system will be a versatile VTA engine capable of:

  1. Generating highly realistic, temporally accurate audio tracks synchronized with video content (VTA).

  2. Synthesizing diverse audio tasks, including Text-to-Audio (TTA), Text-Controlled VTA (TC-VTA), and Audio-Controlled VTA (AC-VTA).

  3. Performing style transfer and Foley extension by conditioning audio generation on external reference video or audio sources.

  4. Achieving unprecedented semantic precision in audio synthesis by accurately interpreting and synthesizing subtle physical attributes described via adverbs (e.g., speed, loudness, distance).

Sources

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