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arxiv: 2604.15086 · v1 · submitted 2026-04-16 · 💻 cs.MM · cs.CV· cs.SD

Recognition: unknown

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

Jianxuan Yang , Xinyue Guo , Zhi Cheng , Kai Wang , Lipan Zhang , Jinjie Hu , Qiang Ji , Yihua Cao
show 5 more authors
Yihao Meng Zhaoyue Cui Mengmei Liu Meng Meng Jian Luan
Authors on Pith no claims yet

Pith reviewed 2026-05-10 09:17 UTC · model grok-4.3

classification 💻 cs.MM cs.CVcs.SD
keywords video-to-audio generationcontrollable audio synthesiscross-modal conflictmultimodal frameworktemporal-timbre decouplingtextual controllabilityREPA alignmentmodality dropout
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The pith

ControlFoley unifies video, text, and reference audio control for video-to-audio generation by resolving cross-modal conflicts.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces ControlFoley to solve weak textual guidance and imprecise stylistic control in video-to-audio systems when visuals conflict with text or reference audio. It does so through a joint visual encoding that merges CLIP features with a spatio-temporal audio-visual encoder, a temporal-timbre decoupling step that keeps only timbre traits from reference audio, and a training process that aligns representations across modalities while randomly dropping inputs. These changes matter because existing methods often ignore text prompts or copy timing artifacts from audio examples, limiting practical use in creative workflows. The authors also release the VGGSound-TVC benchmark to measure controllability under different conflict strengths. If the claims hold, the result is more reliable multi-signal control without sacrificing sound quality or lip-sync.

Core claim

ControlFoley establishes a unified multimodal framework for video-to-audio generation that supports precise control via video content, text descriptions, and reference audio clips. The framework incorporates a joint visual encoding that combines CLIP features with a spatio-temporal audio-visual encoder to enhance alignment and textual influence. Temporal-timbre decoupling isolates discriminative timbre features from temporal cues in reference audio, while a modality-robust training scheme using unified multimodal representation alignment (REPA) and random modality dropout ensures the model remains effective across varying input combinations and conflicts.

What carries the argument

Joint visual encoding paradigm paired with temporal-timbre decoupling, which merges CLIP and spatio-temporal encoders for visual-text alignment and strips timing information from reference audio while retaining timbre features.

If this is right

  • Text prompts can direct audio output even when they directly contradict visual content.
  • Reference audio supplies stylistic traits without leaking unwanted temporal patterns into the result.
  • The model maintains competitive synchronization and quality against industrial video-to-audio systems.
  • The VGGSound-TVC benchmark provides a repeatable way to quantify controllability under graded visual-text conflicts.
  • Random modality dropout during training allows the system to operate when one or more input types are absent or conflicting.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The temporal-timbre split could transfer to other audio synthesis tasks that need style control independent of rhythm.
  • Creators could use live text adjustments to refine generated soundtracks in post-production pipelines.
  • Adding motion or depth maps to the joint visual encoder might increase precision in dynamic scenes.
  • Similar conflict-handling benchmarks could be built for image-to-video or text-to-music generation.

Load-bearing premise

That combining CLIP with spatio-temporal visual encoding, separating timbre from temporal cues in reference audio, and training with REPA alignment plus modality dropout will resolve cross-modal conflicts without harming synchronization or audio quality.

What would settle it

A controlled test on VGGSound-TVC where video shows one action but text specifies a clearly conflicting sound source; measure whether generated audio follows the text prompt more than the video while keeping synchronization metrics and perceptual quality scores at or above baseline levels.

Figures

Figures reproduced from arXiv: 2604.15086 by Jian Luan, Jianxuan Yang, Jinjie Hu, Kai Wang, Lipan Zhang, Mengmei Liu, Meng Meng, Qiang Ji, Xinyue Guo, Yihao Meng, Yihua Cao, Zhaoyue Cui, Zhi Cheng.

Figure 1
Figure 1. Figure 1: Left: Overview of the ControlFoley framework with three multimodal conditioning modes for controllable video-synchronized [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the ControlFoley model architecture. The proposed model integrates visual, text, and audio features into a [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Architecture of the proposed CAV-MAE-ST. The model [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The internal architecture of the Multimodal Transformer block. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Spectrogram comparison on the TV2A task. Left: [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Performance under different T-V Conflict Degrees. (a) [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Spectrogram and frequency response comparison on [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison on the TC-V2A task under conflicting [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparison with Kling-Foley under increasing levels [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Subjective evaluation page. 4.5 User Study To complement objective evaluation, we conduct subjective experi￾ments to assess perceptual quality and controllability across three tasks: TV2A, TC-V2A, and AC-V2A. Experimental Setup. We recruit 10 professional participants with relevant audio-visual experience. Before evaluation, detailed instruc￾tions are provided for each task and metric. Participants are un… view at source ↗
Figure 11
Figure 11. Figure 11: Ablation results of joint visual encoding under dif [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
read the original abstract

Recent advances in video-to-audio (V2A) generation enable high-quality audio synthesis from visual content, yet achieving robust and fine-grained controllability remains challenging. Existing methods suffer from weak textual controllability under visual-text conflict and imprecise stylistic control due to entangled temporal and timbre information in reference audio. Moreover, the lack of standardized benchmarks limits systematic evaluation. We propose ControlFoley, a unified multimodal V2A framework that enables precise control over video, text, and reference audio. We introduce a joint visual encoding paradigm that integrates CLIP with a spatio-temporal audio-visual encoder to improve alignment and textual controllability. We further propose temporal-timbre decoupling to suppress redundant temporal cues while preserving discriminative timbre features. In addition, we design a modality-robust training scheme with unified multimodal representation alignment (REPA) and random modality dropout. We also present VGGSound-TVC, a benchmark for evaluating textual controllability under varying degrees of visual-text conflict. Extensive experiments demonstrate state-of-the-art performance across multiple V2A tasks, including text-guided, text-controlled, and audio-controlled generation. ControlFoley achieves superior controllability under cross-modal conflict while maintaining strong synchronization and audio quality, and shows competitive or better performance compared to an industrial V2A system. Code, models, datasets, and demos are available at: https://yjx-research.github.io/ControlFoley/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The manuscript proposes ControlFoley, a unified multimodal video-to-audio (V2A) generation framework. It introduces a joint visual encoding paradigm integrating CLIP with a spatio-temporal audio-visual encoder, temporal-timbre decoupling to handle reference audio by suppressing redundant temporal cues while preserving timbre, and a modality-robust training scheme using unified multimodal representation alignment (REPA) and random modality dropout. The authors also present the VGGSound-TVC benchmark for evaluating textual controllability under varying degrees of visual-text conflict. The central claims are state-of-the-art performance across text-guided, text-controlled, and audio-controlled V2A tasks, superior controllability under cross-modal conflict, maintained strong synchronization and audio quality, and competitive or better results versus an industrial V2A system.

Significance. If the empirical claims hold after addressing the noted concerns, this would represent a meaningful advance in controllable multimodal generation by providing a unified framework that explicitly handles cross-modal conflicts and by releasing a new benchmark (VGGSound-TVC) that could standardize evaluation of textual controllability. The open release of code, models, datasets, and demos supports reproducibility and follow-on work in the multimedia field.

major comments (2)
  1. [3.2] §3.2 (temporal-timbre decoupling): The method is described as suppressing redundant temporal cues from reference audio while preserving timbre. However, reference audio timing frequently carries synchronization signals that overlap with video events; the manuscript provides no ablation results on AV alignment or synchronization metrics (e.g., those reported in §4) comparing the decoupled versus non-decoupled reference audio. This directly bears on the central claim that strong synchronization is maintained while achieving superior controllability.
  2. [4] §4 (experimental evaluation on VGGSound-TVC): The benchmark is introduced to measure textual controllability under visual-text conflict, yet the paper does not specify how conflict degrees are quantified or controlled during dataset construction, nor does it report the precise controllability metrics (e.g., text-audio similarity scores or human ratings) and their statistical significance. Without these details, the evidence for 'superior controllability under cross-modal conflict' remains difficult to assess and is load-bearing for the SOTA claim.
minor comments (3)
  1. The abstract and §1 would benefit from a concise table summarizing the key quantitative results (FID, CLAP, synchronization scores, etc.) against all baselines to allow immediate comparison.
  2. [3.3] Notation in the REPA loss equation (likely Eq. (X) in §3.3) could be clarified by explicitly defining the modality-specific encoders before the alignment term.
  3. Figure captions for the VGGSound-TVC examples should indicate the specific conflict level (low/medium/high) for each illustrated sample.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We address each major comment point by point below, outlining the revisions we will make to improve clarity and strengthen the empirical support for our claims.

read point-by-point responses

  1. Referee: [3.2] §3.2 (temporal-timbre decoupling): The method is described as suppressing redundant temporal cues from reference audio while preserving timbre. However, reference audio timing frequently carries synchronization signals that overlap with video events; the manuscript provides no ablation results on AV alignment or synchronization metrics (e.g., those reported in §4) comparing the decoupled versus non-decoupled reference audio. This directly bears on the central claim that strong synchronization is maintained while achieving superior controllability.

    Authors: We acknowledge that an explicit ablation isolating the impact of temporal-timbre decoupling on synchronization metrics would provide stronger evidence. The current results demonstrate overall strong synchronization alongside improved controllability, but we agree this does not fully isolate the decoupling component. In the revised manuscript, we will add ablation experiments reporting AV alignment and synchronization metrics (e.g., those used in §4) for both the decoupled and non-decoupled reference audio variants. This will directly address whether synchronization is preserved under decoupling. revision: yes

  2. Referee: [4] §4 (experimental evaluation on VGGSound-TVC): The benchmark is introduced to measure textual controllability under visual-text conflict, yet the paper does not specify how conflict degrees are quantified or controlled during dataset construction, nor does it report the precise controllability metrics (e.g., text-audio similarity scores or human ratings) and their statistical significance. Without these details, the evidence for 'superior controllability under cross-modal conflict' remains difficult to assess and is load-bearing for the SOTA claim.

    Authors: We agree that greater detail on VGGSound-TVC construction and evaluation is required to substantiate the controllability claims. In the revised version, we will expand the description of how conflict degrees are quantified (e.g., via semantic similarity thresholds between visual and textual modalities) and controlled during dataset construction. We will also report the precise controllability metrics, including text-audio similarity scores and human ratings, together with statistical significance tests to support the reported superiority under cross-modal conflict. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper's central claims rest on proposed architectural innovations (joint visual encoding, temporal-timbre decoupling, REPA with random dropout) and empirical results on VGGSound-TVC benchmark, without any equations, predictions, or uniqueness theorems that reduce by construction to fitted inputs or self-referential definitions. No load-bearing self-citations or ansatzes smuggled via prior work are present in the derivation; the evaluation is independent and externally falsifiable via reported metrics.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work relies on standard assumptions from multimodal learning and pre-trained models. No new physical entities or ad-hoc postulates are introduced in the abstract. Full details on any training hyperparameters or additional assumptions would require the complete manuscript.

axioms (2)
  • domain assumption CLIP provides effective visual features that improve audio-visual alignment when combined with spatio-temporal encoding
    Invoked in the joint visual encoding paradigm described in the abstract.
  • domain assumption Temporal and timbre information in reference audio can be meaningfully decoupled to improve stylistic control
    Basis for the proposed temporal-timbre decoupling component.

pith-pipeline@v0.9.0 · 5601 in / 1391 out tokens · 52429 ms · 2026-05-10T09:17:07.670649+00:00 · methodology

discussion (0)

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    wind noise

    noun phrase ( e . g . , " wind noise " , " ambulance siren ") - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - You will be given : - A video clip ( visual input ) - Its raw label : label _ L 0_ raw ( Note : label _ L 0_ raw may contain multiple comma - separated synonyms ) - - - - - - - - - - - - - - - - ...

  52. [52]

    female speech , woman speaking

    label _ L 0 ( Clean Ground Truth ) - If label _ L 0_ raw contains multiple comma - separated labels , SELECT exactly ONE label that best matches the visual content of the video . - If label _ L 0_ raw contains only one label , copy it directly without modification . - Do NOT introduce new semantics or change the event type . - Keep the label concise and i...

  53. [53]

    dog barking

    label _ L 1_ subject ( Weak Conflict : Subject Change ) - Change WHO produces the sound . - Preserve the TEMPORAL STRUCTURE and overall acoustic pattern . - The new subject must plausibly produce a similar type of sound . - IMPORTANT : When the subject changes , the ACTION WORD must be updated to a natural verb for the new subject . Examples : - " dog bar...

  54. [54]

    people clapping

    label _ L 1_ action ( Weak Conflict : Action Change ) - Keep the SAME subject . - Change the action , but STRICTLY preserve the temporal pattern . Temporal consistency rules : - Continuous stays continuous - Rhythmic stays rhythmic - Transient stays transient Examples : - " people clapping " -> " slapping table " - " dog barking " -> " dog coughing " - " ...

  55. [55]

    dog barking

    label _ L 2 ( Moderate Conflict : Semantic Mismatch , Physical Match ) - Change to a DIFFERENT semantic class . - The sound must remain acoustically and temporally compatible with the video's motion and rhythm . - Visual meaning may conflict , but sound structure must match . - Think like a Foley artist . Examples : - " dog barking " -> " knocking on door...

  56. [56]

    All labels look like valid VGGSound labels

  57. [57]

    Temporal structure is preserved across all rewritten labels

  58. [58]

    No extra text

    Output JSON ONLY . No extra text