arxiv: 2604.04348 · v1 · submitted 2026-04-06 · 💻 cs.SD · cs.CV· cs.MM

Recognition: no theorem link

OmniSonic: Towards Universal and Holistic Audio Generation from Video and Text

Weiguo Pian , Saksham Singh Kushwaha , Zhimin Chen , Shijian Deng , Kai Wang , Yunhui Guo , Yapeng Tian

Authors on Pith no claims yet

Pith reviewed 2026-05-10 20:19 UTC · model grok-4.3

classification 💻 cs.SD cs.CVcs.MM

keywords audio generationvideo-to-audiotext-to-audiodiffusion modelsflow matchingmultimodal generationspeech synthesisholistic audio

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The pith

A diffusion model with triple cross-attention generates full audio scenes that include on-screen sounds, off-screen sounds, and speech from video and text.

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

The paper defines a new task of universal holistic audio generation that requires producing complete auditory scenes containing both visible and invisible environmental sounds plus human speech. It presents OmniSonic as a flow-matching diffusion model whose TriAttn-DiT backbone runs three separate cross-attention operations, one for each audio type, while an MoE gate balances their influence at every generation step. The authors also release UniHAGen-Bench, a dataset of more than one thousand video-text pairs spanning ambient, instrumental, and spoken scenarios. Experiments show that this single architecture surpasses earlier video-only and non-speech joint models on standard metrics and in listener studies, indicating that unified conditioning can replace the current patchwork of specialized generators.

Core claim

OmniSonic is a flow-matching-based diffusion framework jointly conditioned on video and text. It features a TriAttn-DiT architecture that performs three cross-attention operations to process on-screen environmental sound, off-screen environmental sound, and speech conditions simultaneously, with a Mixture-of-Experts gating mechanism that adaptively balances their contributions during generation. The model is evaluated on the new UniHAGen-Bench covering three representative on/off-screen speech-environment scenarios and consistently outperforms prior state-of-the-art approaches on both objective metrics and human evaluations.

What carries the argument

TriAttn-DiT, a diffusion transformer that applies three distinct cross-attention layers—one each for on-screen environmental, off-screen environmental, and speech conditioning—together with an MoE gating layer that dynamically weights their contributions during audio synthesis.

If this is right

A single model can now synthesize auditory scenes that mix ambient events, musical instruments, and human speech without needing separate pipelines for speech and non-speech audio.
The UniHAGen-Bench supplies a standardized testbed that measures both environmental and speech fidelity in the presence of on- and off-screen sources.
Joint video-text conditioning with adaptive gating yields higher objective scores and listener preference than prior video-only or non-speech joint generators.
The architecture supports diverse domains while maintaining the ability to render sounds that are not visible on screen.

Where Pith is reading between the lines

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

The same three-way conditioning pattern could be applied to other multimodal tasks that require balancing visible, invisible, and linguistic signals, such as generating video from audio descriptions.
If the MoE router learns stable routing across longer sequences, the framework may scale to minute-long videos without additional architectural changes.
Replacing multiple task-specific audio generators with one unified model would simplify deployment in applications such as video editing tools or virtual-reality sound design.

Load-bearing premise

The three cross-attention modules together with the MoE gating can process and balance on-screen environmental, off-screen environmental, and speech conditions at the same time without destructive interference or loss of quality.

What would settle it

On UniHAGen-Bench samples that contain simultaneous on-screen action, off-screen events, and spoken dialogue, generate audio with the full TriAttn-DiT model and with each cross-attention module ablated; if any ablation version matches or exceeds the full model on FAD, CLAP scores, and human preference for completeness, the claim that triple attention plus MoE is required for balanced holistic output is falsified.

Figures

Figures reproduced from arXiv: 2604.04348 by Kai Wang, Saksham Singh Kushwaha, Shijian Deng, Weiguo Pian, Yapeng Tian, Yunhui Guo, Zhimin Chen.

**Figure 2.** Figure 2: (A) Overview of our proposed OmniSonic, which mainly consists of an environmental text encoder (FLAN-T5), a speech [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Visualization of the spectrograms of generated audios and the ground-truth. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Ablation study on the MoE Gating module using in-the [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Interface for the subjective evaluation. [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗

read the original abstract

In this paper, we propose Universal Holistic Audio Generation (UniHAGen), a task for synthesizing comprehensive auditory scenes that include both on-screen and off-screen sounds across diverse domains (e.g., ambient events, musical instruments, and human speech). Prior video-conditioned audio generation models typically focus on producing on-screen environmental sounds that correspond to visible sounding events, neglecting off-screen auditory events. While recent holistic joint text-video-to-audio generation models aim to produce auditory scenes with both on- and off-screen sound but they are limited to non-speech sounds, lacking the ability to generate or integrate human speech. To overcome these limitations, we introduce OmniSonic, a flow-matching-based diffusion framework jointly conditioned on video and text. It features a TriAttn-DiT architecture that performs three cross-attention operations to process on-screen environmental sound, off-screen environmental sound, and speech conditions simultaneously, with a Mixture-of-Experts (MoE) gating mechanism that adaptively balances their contributions during generation. Furthermore, we construct UniHAGen-Bench, a new benchmark with over one thousand samples covering three representative on/off-screen speech-environment scenarios. Extensive experiments show that OmniSonic consistently outperforms state-of-the-art approaches on both objective metrics and human evaluations, establishing a strong baseline for universal and holistic audio generation. Project page: https://weiguopian.github.io/OmniSonic_webpage/

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.

Desk Editor's Note private letter to a colleague

OmniSonic defines a new UniHAGen task for joint on/off-screen and speech audio from video plus text, using TriAttn-DiT with MoE gating, but the outperformance claims rest on experiments whose details are not visible in the abstract.

read the letter

The paper's main contribution is carving out UniHAGen as a task that requires generating complete auditory scenes with on-screen environmental sounds, off-screen events, and human speech all at once. Prior video-to-audio work mostly ignored off-screen audio, and the holistic models that tried to cover both on- and off-screen stayed away from speech. OmniSonic tries to close that gap with a flow-matching diffusion model whose TriAttn-DiT block runs three separate cross-attentions and then mixes them through MoE gating. They also release UniHAGen-Bench with over a thousand samples across three speech-environment scenarios, which gives the field a concrete evaluation set it did not have before. That combination of task definition, architecture, and benchmark is what is actually new here, and it is a reasonable step forward for media and VR applications that need richer soundtracks. The architecture description itself is clear enough that someone working on conditional diffusion could implement the triple-attention idea and test it. The benchmark construction is practical and directly tied to the stated limitations of earlier models. The soft spot is that the abstract gives no training data description, no exact baseline implementations, no loss terms for the MoE, and no ablations on whether the three attentions interfere. The stress-test worry about speech dominating the gating because of higher energy is plausible on its face, and without seeing how the experts are conditioned or whether the paper reports separate metrics for off-screen quality, it is hard to know if the holistic claim holds. If the full paper has those controls and shows the off-screen component does not degrade, the result strengthens; right now the superiority statement cannot be checked from what is provided. This work is aimed at people already doing audio-visual generation or multimodal diffusion. A reader who needs a new benchmark or wants to try triple conditioning will get direct value from the task setup and the model sketch. It is coherent enough on its own terms to deserve a serious referee, mainly because the gap it targets is real and the benchmark is usable even if the model needs more validation. I would send it to peer review with a request for the missing experimental details and ablations on the gating behavior.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes Universal Holistic Audio Generation (UniHAGen), a task for synthesizing complete auditory scenes from video and text that include on-screen environmental sounds, off-screen environmental sounds, and human speech. It introduces OmniSonic, a flow-matching diffusion model featuring a TriAttn-DiT architecture with three parallel cross-attention operations (one each for on-screen env, off-screen env, and speech) whose outputs are balanced by a Mixture-of-Experts (MoE) gating mechanism. The authors also release UniHAGen-Bench, a new benchmark of over 1,000 samples spanning three on/off-screen speech-environment scenarios, and claim that OmniSonic consistently outperforms prior state-of-the-art methods on both objective metrics and human evaluations.

Significance. If the reported superiority holds under rigorous verification, the work would advance audio generation by addressing the gap in holistic scene synthesis that jointly handles environmental sounds and speech. The new UniHAGen-Bench could become a useful community resource for standardized evaluation. The TriAttn-DiT + MoE design offers a concrete architectural proposal for multi-condition conditioning, though its practical effectiveness remains to be demonstrated.

major comments (2)

[TriAttn-DiT and MoE description] The description of the TriAttn-DiT architecture provides no equations, pseudocode, or loss terms defining the MoE gating mechanism or how the three cross-attention outputs are combined and conditioned. This is load-bearing for the central outperformance claim, because the skeptic concern that speech (higher energy, clearer supervision) may dominate and suppress off-screen environmental sounds cannot be evaluated without these details.
[Experiments section] No information is given on training data composition and size, exact objective metrics and their implementations, baseline reproductions, number of runs, or statistical significance tests. Since the manuscript's primary contribution is the empirical superiority on UniHAGen-Bench, these omissions prevent verification of the results and leave open the possibility of evaluation biases.

minor comments (1)

[Abstract] The abstract states that OmniSonic 'establishes a strong baseline' but does not quantify the margin of improvement or report variance across seeds; adding these numbers would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We agree that greater technical specificity is required to support the central claims and enable verification. We will revise the manuscript to address both major comments fully, as detailed below.

read point-by-point responses

Referee: [TriAttn-DiT and MoE description] The description of the TriAttn-DiT architecture provides no equations, pseudocode, or loss terms defining the MoE gating mechanism or how the three cross-attention outputs are combined and conditioned. This is load-bearing for the central outperformance claim, because the skeptic concern that speech (higher energy, clearer supervision) may dominate and suppress off-screen environmental sounds cannot be evaluated without these details.

Authors: We acknowledge that the current manuscript description of the TriAttn-DiT and MoE components is insufficiently detailed. The architecture employs three parallel cross-attention branches to separately process on-screen environmental, off-screen environmental, and speech conditions, with the MoE gating mechanism intended to adaptively balance their influence and mitigate dominance by higher-energy signals such as speech. We agree this requires explicit formalization. In the revised manuscript we will add the governing equations for each cross-attention operation, the MoE router and expert combination formulas, the overall conditioning mechanism, and the flow-matching loss terms. These additions will allow direct evaluation of how the gating prevents suppression of off-screen sounds. revision: yes
Referee: [Experiments section] No information is given on training data composition and size, exact objective metrics and their implementations, baseline reproductions, number of runs, or statistical significance tests. Since the manuscript's primary contribution is the empirical superiority on UniHAGen-Bench, these omissions prevent verification of the results and leave open the possibility of evaluation biases.

Authors: We agree that the Experiments section lacks the information needed for reproducibility and independent verification. In the revised manuscript we will expand this section to specify the training data sources, composition, and total size; the precise definitions and code-level implementations of all objective metrics; the procedures used to reproduce each baseline; the number of independent runs conducted; and the statistical significance tests applied (including p-values). We will also add further analysis of results across the three scenario types in UniHAGen-Bench to address potential evaluation biases. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical architecture and benchmark proposal

full rationale

The paper proposes a new task (UniHAGen), a flow-matching diffusion model (OmniSonic with TriAttn-DiT and MoE gating), and a new benchmark (UniHAGen-Bench) with experimental comparisons. No equations, derivations, or predictions are presented that reduce to self-definitions, fitted inputs renamed as outputs, or load-bearing self-citations. Performance claims rest on end-to-end training and external evaluations rather than any internal reduction by construction.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Relies on standard assumptions of diffusion/flow-matching generative models and learned neural network parameters; introduces new architectural modules but no new physical entities.

free parameters (1)

DiT and MoE hyperparameters
Numerous learned weights, attention dimensions, expert counts, and gating parameters tuned during training.

axioms (1)

domain assumption Flow-matching diffusion can produce coherent multimodal-conditioned audio when separate attention streams are balanced by MoE.
Invoked to justify the TriAttn-DiT design choice.

pith-pipeline@v0.9.0 · 5569 in / 1240 out tokens · 31413 ms · 2026-05-10T20:19:50.825540+00:00 · methodology

discussion (0)

Reference graph

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