ControlFoley: Unified and Controllable Video-to-Audio Generation with Cross-Modal Conflict Handling

arXiv:2604.15086 · cs.MM, cs.CV, cs.SD · Submitted 2026-04-16 · 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: "ControlFoley: Unified and Controllable Video-to-Audio Generation with Cross-Modal Conflict Handling".

Jane: ControlFoley proposes a unified and controllable multimodal Video-to-Audio (V2A) generation framework designed to achieve precise control across video, text, and reference audio while robustly handling cross-modal conflicts.

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

Title and authors: Tom: Let's talk about the title and who wrote this. "ControlFoley: Unified and Controllable Video-to-Audio Generation with Cross-Modal Conflict Handling." It sounds pretty comprehensive, doesn't it? Who are the authors, Lu?

Lu: The team behind this includes Jianxuan Yang, Xinyue Guo, Zhi Cheng MiLM Plus from Xiaomi Inc., Kai Wang MiLM Plus from Wuhan University, Lipan Zhang MiLM Plus from Xiaomi Inc., Jinjie Hu MiLM Plus from Xiaomi Inc., Qiang Ji MiLM Plus from Xiaomi Inc., Yihua Cao MiLM Plus from Xiaomi Inc., Yihao MengMiLM Plus, Zhaoyue CuiMiLM Plus, Mengmei LiuMiLM Plus, and Jian LuanMiLM Plus all contributing. It’s a large collaboration spanning different institutions.

Jane: It's a big team effort, which suggests the complexity of the problem they are trying to solve is quite high. The title itself tells us that the core focus is on achieving control across video and audio while managing those tricky conflicts between what's seen and what's heard.

Meng: A large team can mean more diverse expertise, but I wonder if that size also means a less streamlined development process when we need to deploy something quickly. ControlFoley sounds like it’s trying to solve a very complex alignment issue right out of the gate.

Lalam: The sheer breadth of contributions from different entities suggests they are building a really deep foundation for this system, which is exactly what we need when we're pushing the boundaries of multimodal AI capabilities.

The paper's summary: Tom: Now that we know who’s behind it, let's get into the actual substance of what ControlFoley does. What is the core research summary here? Jane, walk us through the main idea in simple terms.

Jane: The main idea is that existing Video-to-Audio methods are good at basic synchronization but fail when there's a mismatch between what you see and what the text prompt tells you to hear. ControlFoley proposes a new way to guide this generation using specific visual, textual, and audio features simultaneously so it can handle these conflicts better.

Lu: Specifically, they introduce joint visual encoding using CAVMAE-ST combined with CLIP features. This dual-branch design is meant to help the model understand both the spatial video information and the language context at once, which is key for stabilizing control when things get messy.

Meng: That sounds computationally intensive; combining two different encoders must add complexity to the training process. I wonder how they managed to keep that process tractable enough for effective development.

Lalam: What really stands out in the summary is their focus on decoupling temporal and timbre information, which suggests they are treating sound style as something separate from the exact timing of the video frames, which is a very clever way to manage audio fidelity.

The paper's improvements: Tom: That decoupling idea you mentioned sounds really interesting. Jane, can you elaborate on some of the specific improvements they propose in ControlFoley? What makes this approach different from what we see in models like Kling-Foley or ThinkSound?

Jane: They have three main innovations. First, they have this joint visual encoding to improve textual control when there's conflict. Second, they use a temporal–timbre decoupling strategy for precise timbre control in audio generation. And third, they use a unified training approach called REPA loss and random modality dropout to make the model more robust if one of the inputs is missing.

Lu: The REPA loss is important because it directly addresses the need to preserve semantic consistency between modalities, which I think is crucial for making sure the generated audio matches what's happening visually. It’s a direct response to problems in representation learning mentioned in prior work like twenty-two twenty-seven thirty thirty-two thirty-nine forty.

Meng: From an engineering standpoint, adding these specific loss functions and dropout strategies shows they are thinking about generalization; it's not just about achieving high scores on a single benchmark. But the authors also mentioned that their method doesn't cover every scenario. What are the explicit limitations they point out?

Jane: They do flag that while ControlFoley performs exceptionally well across its three main tasks—TV2A, TC-V2A, and AC-V2A—the method does have specific areas where performance varies depending on the nature of the conflict. For instance, they show that in TC-V2A scenarios, the impact of visual cues on text control changes as conflict levels increase; specifically, "IB decreases more rapidly than all baselines as the conflict level increases," which shows how it adapts to modality priorities.

Conclusion: Tom: Wow, so we've covered a lot about how this paper uses joint encoding and decoupling to handle conflicts. Let's wrap up with the conclusion. What is the big picture implication of ControlFoley?

Jane: The big implication is that we can move towards more flexible and reliable V2A systems where users have much finer control over the final audio output, whether they want strict text adherence or precise style matching. It shows that integrating different types of guidance mechanisms—visual semantics, text semantics, and timbre features—can lead to better overall alignment.

Lu: I think the real impact lies in how this architecture allows for dynamic balancing between modalities; it suggests a future where AI doesn't just follow instructions blindly but intelligently decides which piece of information is most reliable at any given moment.

Meng: For practical deployment, that flexibility is key because real-world content creation rarely gives you perfect, clean inputs. If the system can adapt to imperfect or conflicting signals without collapsing, that’s where the immediate utility lies for engineers.

Lalam: From a cultural standpoint, if we can create audio generation that is so contextually rich and controllable, it opens up new possibilities for personalized media and interactive narratives that feel truly alive because the sound matches the visual and emotional intent perfectly.

Tom: So, to summarize, ControlFoley gives us a unified way to tackle video-to-audio generation with explicit mechanisms for conflict handling through joint encoding and decoupling temporal and timbre information. It’s a really solid piece of work that pushes what we can expect from these models. Jane, Lu, Meng, Lalam—thanks for joining us on this deep dive into ControlFoley. We’ll be right back after the break to talk about some of those other papers we've been looking at.

Jianxuan Yang, Xinyue Guo, Zhi Cheng, Kai Wang, Lipan Zhang, Jinjie Hu, Qiang Ji, Yihua Cao, Yihao Meng

Xiaomi Inc. · Wuhan University

cs.MM, cs.CV, cs.SD

Submitted: 2026-04-16

Updated: 2026-09-29

Code: https://github.com/xiaomi-research/controlfoley

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

Importance score: 89/100

The gist: ControlFoley proposes a unified and controllable multimodal Video-to-Audio (V2A) generation framework designed to achieve precise control across video, text, and reference audio while robustly

Key concepts

ControlFoley
A unified framework for Video-to-Audio generation that aims to achieve precise control across video, text, and reference audio while robustly managing conflicts between what is seen and what is heard.
Joint Visual Encoding
The use of joint visual encoding combining CAVMAE-ST with CLIP features. This dual-branch design helps the model simultaneously understand both the spatial information in the video and the context from text, which stabilizes control during conflicting inputs.
Temporal–Timbre Decoupling
A strategy that treats sound style as separate from exact timing in video frames. This allows for precise timbre control in audio generation, separating how the sound is styled from when it occurs visually.

Terminology

Summary

ControlFoley proposes a unified and controllable multimodal Video-to-Audio (V2A) generation framework designed to achieve precise control across video, text, and reference audio while robustly handling cross-modal conflicts. This work addresses fundamental limitations in existing V2A methods, specifically weak textual controllability under visual-text semantic conflict and imprecise stylistic control due to entangled temporal and timbre information in reference audio. By introducing novel architectural components and training strategies, ControlFoley establishes a unified framework enabling precise control across text-guided (TC-V2A), text-controlled (TV2A), and audio-controlled (AC-V2A) generation tasks, complemented by a new benchmark to systematically evaluate textual controllability.

ControlFoley's Core Innovations

The framework introduces three key innovations to enhance multimodal alignment and controllability:

  1. Joint Visual Encoding with CAV-MAE-ST: This paradigm combines a spatio-temporally optimized CAVMAE-ST encoder with CLIP visual features. This dual-branch design mitigates text-visual conflict and enhances textual control authority in TC-V2A by leveraging both vision-language alignment and audio-visual aligned representations.

  2. Temporal–Timbre Decoupling for Precise Timbre Control: To enable fine-grained control over acoustic style, this strategy suppresses redundant temporal information in reference audio while preserving discriminative timbre features, thereby avoiding temporal interference.

  3. Modality-Robust Training with Unified REPA: The model employs a unified multimodal representation alignment (REPA) loss and random modality dropout during training. The REPA loss aligns the generated audio representations with an aggregated multimodal condition, ensuring robust performance under arbitrary modality absence.

Framework Architecture and Conditioning

ControlFoley is built upon a Multimodal Diffusion Transformer (MMDiT) backbone, modified with several key components to improve control. The architecture integrates visual, text, and audio features into a multimodal transformer network comprising 18 multimodal DiT blocks and 36 unimodal DiT blocks. Global conditions incorporating visual semantics, textual semantics, and timbre features are integrated for precise control. Specifically:

**: Joint Visual Encoding: The input video is processed by both a CLIP visual encoder and the proposed CAV-MAE-ST encoder. The final visual representation is obtained through fusion: z joint v = Proj(z CLIP v) + Proj(z CAV v). This design ensures strong cross-modal synergy when semantics are consistent (TV2A) and stabilizes multimodal fusion under conflict (TC-V2A). Global conditions are derived from visual semantics, textual semantics, and timbre features. The frame-aligned synchronization condition is also integrated for precise control. 4.3.1 TV2A: ControlFoley achieves state-of-the-art performance across three benchmarks (VGGSoundTest, KlingAudioEval, MovieGenAudioBench), consistently attaining the highest CLAP scores and lowest DeSync values compared to baselines like AudioX and Kling-Foley. 4.3.2 TC-V2A: The model demonstrates superior textual controllability under cross-modal conflict; specifically, IB decreases more rapidly than all baselines as the conflict level increases, indicating that the model effectively suppresses conflicting visual cues, while maintaining consistently higher CLAP scores to show stronger text controllability. 4.3.3 AC-V2A: For audio-controlled generation, ControlFoley achieves the best performance on all metrics across both Greatest Hits and AC-VAS datasets, achieving the highest timbre similarity and superior synchronization compared to task-specific models like CondFoleyGen. The dual-path reference audio conditioning—incorporating both global semantic and global timbre conditioning—is shown to yield the best results in Resemblyzer score (timbre similarity) and DeSync score (temporal sync). 4.4 Comparison with Industrial Baselines: When compared against Kling-Foley, ControlFoley consistently outperforms it in semantic alignment, temporal synchronization, and perceptual quality across all datasets. Furthermore, under conflicting conditions (TC-V2A), ControlFoley exhibits a more pronounced decrease in IB compared to Kling-Foley, suggesting a more flexible and controllable trade-off between modalities by dynamically adjusting modality priorities. 4.5 User Study: Subjective evaluations confirm objective metrics, with ControlFoley achieving the highest Mean Opinion Score (MOS) across all tasks, indicating superior perceptual alignment and quality. A significant finding in TC-V2A is that ControlFoley adaptively handles cross-modal conflict better than models like ThinkSound, which show a bias toward text rather than balanced multimodal control. 4.6 Ablations: Ablation studies confirm the necessity of the proposed components; for instance, removing REPA loss leads to degradation in semantic alignment and distribution matching, highlighting its role in "preserving semantic consistency between modalities.

Improvements for AI systems

Here are the specific improvements that can be made to AI systems by implementing ControlFoley, along with a description of what these improved systems can achieve:

) [1] The proposed system (ControlFoley) introduces a unified and controllable Video-to-Audio (V2A) framework capable of generating video-synchronized audio under three distinct control paradigms: Text-Controlled V2A (TC-V2A), Audio-Controlled V2A (AC-V2A), and Unified TV2A.

) [1] It achieves precise control by integrating a joint visual encoding paradigm (CAVMAE-ST combined with CLIP) that enhances textual controllability under cross-modal conflict, specifically mitigating the visual dominance phenomenon where text is overridden by salient visual cues.

) [1] The system implements a Temporal-Timbre Decoupling strategy for AC-V2A. This technique suppresses redundant temporal information from reference audio while preserving discriminative timbre features, enabling interference-free and precise acoustic style control (e.g., generating specific material sounds or vocal timbres).

) [1] It utilizes a Modality-Robust Training scheme with Unified Representation Alignment (REPA) loss and random modality dropout. This ensures the model maintains stable, high-quality generation performance even when visual, textual, or reference audio modalities are missing during inference.

) [1] The system introduces VGGSound-TVC, a novel benchmark designed to systematically quantify textual controllability under varying degrees of visual-text semantic conflict (Levels L0 to L3).

Improved AI System Capabilities:

  1. A unified V2A generator that can synthesize high-fidelity audio synchronized with video while allowing users to control the output via three distinct inputs:

  2. Textual Prompts (TC-V2A): The system can generate audio precisely following complex textual instructions, even when the text semantically conflicts with visual information in the video (e.g., generating a piano playing sound from a video of someone typing on a keyboard). It dynamically balances reliance between visual cues and textual semantics to ensure high semantic alignment without sacrificing temporal synchronization.

  3. Audio-Based Style Control (AC-V2A): The system can generate audio that perfectly matches the acoustic style (timbre) of an input reference sound (e.g., generating a specific wood chopping sound from a video, using a reference clip of wood chopping as the style guide), while simultaneously ensuring it is perfectly synchronized with the video's motion.

  4. Robustness and Reliability: The system is highly reliable in real-world applications because it can generate audio even if one or more inputs are missing (e.g., generating audio from a video only, or from a text prompt and reference audio), thanks to its modality-robust training scheme.

  5. Systematic Evaluation: Researchers can rigorously benchmark the controllability of V2A models using VGGSound-TVC, allowing for objective comparison of how effectively different architectures handle semantic conflicts in textual guidance.

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