Beyond Video-to-SFX: Audio Synthesis

• Beyond Video-to-SFX (BVS) is a video-conditioned audio synthesis framework that generates synchronized, intelligible speech and ambient sounds from video sequences.
• It employs a novel two-stage architecture that first creates unified audio semantic tokens from video and phonetic cues, then reconstructs high-fidelity audio via a video-conditioned model.
• Empirical results and ablation studies show that BVS overcomes limitations of prior V2A systems by enhancing speech intelligibility and environmental sound integration.

Searching arXiv for the specified paper and closely related work to ground the article. Beyond Video-to-SFX (BVS) is a video-conditioned audio synthesis framework for generating synchronized audio with environmentally aware intelligible speech for given videos. Introduced as a two-stage method, it combines a video-guided audio semantic model with a video-conditioned semantic-to-acoustic model, and is evaluated in scenarios including video-to-context-aware speech synthesis and immersive audio background conversion. In the current video-to-audio literature, BVS occupies a specific position: it addresses the recurrent gap between systems that generate Foley-like sound effects effectively and systems that can also produce intelligible, temporally aligned speech in dynamic visual contexts (Niu et al., 19 Sep 2025).

1. Research context and problem setting

Video-to-audio research has often concentrated on sound effects, background ambience, or music-like accompaniment rather than structured human vocalization. VidAudio-Bench formalizes this discrepancy by evaluating four audio categories—sound effects, music, speech, and singing—and reports that current V2A models perform poorly in speech and singing compared to sound effects. The benchmark further states that speech and singing exhibit substantially lower MOS, intelligibility, lip-sync, and semantic scores, and attributes this in part to the fact that existing V2A training data such as VGG-Sound and AudioSet under-represent structured human vocalizations (Zhang et al., 12 Apr 2026).

Earlier systems expose adjacent limitations. SVA is a training-free framework that uses a single key frame, Gemini Pro, AudioGen, and MusicGen to produce semantically coherent SFX and BGM, but it is explicitly described as coarse-grained, since one key frame ignores temporal events, and it has no direct modeling of per-frame audio timing or moving sound sources (Chen et al., 2024). SyncFusion addresses synchronization for Foley synthesis by extracting a binary onset track from silent video and conditioning a diffusion model on both that onset sequence and CLAP embeddings, thereby separating “when” events happen from “what” they sound like (Comunità et al., 2023). MultiFoley extends video-guided Foley generation with multimodal conditioning through text, audio, and video, supports full-bandwidth 48 kHz output, and is jointly trained on internet video datasets and professional SFX recordings, but its framing remains centered on Foley sound generation and multimodal control (Chen et al., 2024).

Within this landscape, BVS targets a narrower but difficult regime: intelligible speech embedded in environmental audio. The paper states that existing video-to-audio methods mainly focus on Foley sound generation and struggle to produce intelligible speech, while environmental speech synthesis approaches remain text-driven and fail to temporally align with dynamic video content. BVS is therefore defined around synchronized audio with environmentally aware intelligible speech rather than around SFX alone (Niu et al., 19 Sep 2025).

2. Two-stage architecture and semantic bottleneck

BVS decomposes video-conditioned audio synthesis into two stages. Stage I, called V2AS, is a video-guided audio semantic generation model. Its inputs are a sequence of video frame embeddings $V$ and phonetic speech cues $S_s$, where the phonetic cues are discrete speech SSL tokens extracted from a pretrained speech-enhancement model in the W2V-BERT quantized space. Its output is a sequence of unified audio semantic tokens $S_a$ in the same W2V-BERT codebook, which jointly encode speech and ambient-sound information. Stage II, called VS2A, takes the semantic token sequence $S_a$ together with video embeddings $V$ and predicts multi-layer acoustic codec tokens $A=\{A^1,A^2,\dots,A^K\}$, which are then decoded to waveform via Encodec (Niu et al., 19 Sep 2025).

The paper formulates the semantic stage as

$$\hat S_a=f_{\mathrm{V2AS}}(V,S_s;\theta_{\mathrm{V2AS}})$$

and trains it with an autoregressive-in-parallel MaskGit transformer. At training step $t$, a random subset of tokens is masked and the loss is

$$L_{\mathrm{V2AS}} = -\sum_{i: m_t^i=1} \log p_{\theta_{\mathrm{V2AS}}}\bigl(s_i^a \mid S_a^M,\,S_s,\,V\bigr).$$

Inference runs 16 iterative denoising steps with classifier-free guidance to produce $\hat S_a$.

The acoustic stage predicts codec layers sequentially:

$S_s$0

with masked-token cross-entropy

$S_s$1

The total objective is

$S_s$2

with $S_s$3 in practice.

This factorization is central to the method’s design. The paper states that, by factoring out the “semantic bottleneck” $S_s$4, stage I focuses on cross-modal fusion, while stage II concentrates on high-fidelity audio reconstruction. The ablation results later reported for single-stage and text-derived alternatives are consistent with that design choice (Niu et al., 19 Sep 2025).

3. Data, representations, and training recipe

BVS is trained on AS-Speech, described as filtered English AudioSet-Speech, comprising approximately 580 K video-audio pairs of 10 s each. Evaluation is conducted on AS-filtered, which contains 152 held-out YouTube clips with captions and transcripts. The preprocessing pipeline is explicit: video is sampled at 5 fps, encoded with EVA-CLIP into 768-dimensional features, mean-pooled, and combined with V2Cap LLaMa outputs to form 50×768 features; speech SSL tokens are obtained from Metis-SE stage 1 and quantized in the W2V-BERT space at 50 Hz; audio semantic tokens are W2V-BERT 2.0 features quantized by VQ-VAE at 50 Hz; and acoustic tokens are Encodec tokens at 50 Hz with 4 codebooks (Niu et al., 19 Sep 2025).

Training is carried out on 3× NVIDIA A100 (80 GB) with batch size 32 and AdamW. The learning rate is $S_s$5, with warm-up for 20 K steps in V2AS and 32 K steps in VS2A. The models are trained for approximately 80 epochs for V2AS and approximately 34 epochs for VS2A. Inference uses 16 steps for V2AS and $S_s$6 steps for VS2A layers 1–4, with classifier-free guidance scales 5.0 for V2AS and 2.5 for VS2A (Niu et al., 19 Sep 2025).

The representation choice is notable. The semantic tokens are described as unified audio semantic tokens in the same W2V-BERT codebook, jointly encoding speech and ambient-sound information. Qualitative demos and residual SSL samples are reported to confirm that the unified semantic tokens indeed capture both speech and ambient noise. This suggests that BVS treats environmental context not as a post-hoc additive background, but as part of a shared latent audio-semantic substrate before acoustic rendering (Niu et al., 19 Sep 2025).

4. Empirical results and ablation evidence

The paper evaluates BVS with four metrics: speech intelligibility using WER (%) via Whisper-large together with $S_s$7WER, temporal alignment using DeSync, and perceptual or semantic similarity using FAD and LPAPS via CLAP. In the video-to-context-aware speech setting, the reported results are as follows (Niu et al., 19 Sep 2025):

Method	WER / $S_s$8WER	FAD / DeSync / LPAPS
GT audio	27.4% / 3.2%	— / — / —
VoiceLDM	17.2% / 22.5%	0.36 / 1.49 / 6.48
VATT	98.9% / 75.6%	0.43 / 1.33 / 6.65
BVS	26.4% / 21.9%	0.41 / 1.33 / 6.38

In immersive audio background conversion, BVS is compared against SE + VATT. The paper reports 26.9% $S_s$9WER, 0.38 FAD, 1.32 DeSync, 6.42 LPAPS_S, and 6.28 LPAPS_T for BVS, versus 27.8% $S_a$0WER, 0.39 FAD, 1.22 DeSync, 6.69 LPAPS_S, and 6.47 LPAPS_T for SE + VATT (Niu et al., 19 Sep 2025).

The ablation study is particularly important for interpreting the architecture. On AS-filtered, VS2A reconstruction only yields 19.8% $S_a$1WER, 0.25 FAD, 1.02 DeSync, and 5.12 LPAPS. BVS-Single yields 99.1% $S_a$2WER, 1.18 FAD, 1.38 DeSync, and 6.78 LPAPS. BVS-Text yields 98.7% $S_a$3WER, 1.12 FAD, 1.49 DeSync, and 7.01 LPAPS. Full BVS yields 21.9% $S_a$4WER, 0.41 FAD, 1.33 DeSync, and 6.38 LPAPS. The paper’s stated takeaway is that removing the semantic stage or replacing SE-derived tokens with text-derived tokens catastrophically hurts WER and FAD, and that the two-stage, SE-guided, W2V-BERT-semantic design is essential for disentangling and fusing speech and context (Niu et al., 19 Sep 2025).

These results place BVS in a distinct empirical niche. It is not positioned as an oracle-quality talking-head system, nor as a generic high-fidelity Foley generator. Rather, the reported numbers support the claim that it can jointly generate intelligible, temporally aligned speech and realistic ambient sounds from raw video without synthetic noisy data or ASR transcripts (Niu et al., 19 Sep 2025).

5. Relation to the broader “beyond video-to-SFX” trend

The phrase “beyond video-to-SFX” also names a broader shift in the literature away from narrowly defined Foley generation. One strand is retrieval rather than generation. “Bridging High-Quality Audio and Video via Language for Sound Effects Retrieval from Visual Queries” describes a multimodal framework for recommending high-quality SFX given a video frame by first curating high-quality audio-visual pairs with BLOOM and CLIP and then training a contrastive retrieval model. In that setting, users preferred the retrieved SFX 68.1% of the time on HQ data and 66.4% of the time on in-the-wild data, and the work frames this as moving beyond brittle text metadata and noisy in-the-wild supervision (Wilkins et al., 2023).

A second strand broadens conditioning and controllability. MultiFoley introduces a latent diffusion transformer with frozen video and text encoders and an audio autoencoder, supports video, text, and reference-audio controls, and reports 48 kHz output together with strong automated and human evaluation. The work explicitly states that it goes “beyond video-to-SFX” by introducing multimodal controls, full-bandwidth fidelity, and a unified diffusion framework for VT2A, T2A, and audio-extension (Chen et al., 2024).

A third strand broadens application scope. AMAVA presents a real-time video-to-audio framework for blind and low vision assistance that distinguishes between low and high-movement scenes and then generates either spoken scene descriptions or sound cues. Its summary explicitly describes it as a “beyond-video-to-SFX” framework because it dynamically chooses between speech and non-speech channels, integrates neural TTS and neural SFX generation, and uses caching and throttling to reduce latency and clutter (Klein et al., 26 Apr 2026).

BVS in the narrow sense of (Niu et al., 19 Sep 2025) belongs to this broader trajectory, but with a specific methodological commitment: environmentally aware speech generation via unified audio semantic tokens. Relative to SFX retrieval, multimodal Foley control, or assistive audio feedback, its central novelty is the combination of speech SSL cues, video conditioning, and a semantic bottleneck that jointly models speech and ambient sound before waveform reconstruction (Niu et al., 19 Sep 2025).

6. Limitations, open problems, and benchmark implications

The paper identifies three principal limitations. First, the W2V-BERT semantic space was trained primarily on speech and thus has limited capacity to model very diverse non-speech sound effects. Second, precise lip-sync remains challenging given the dataset’s lack of talking-head supervision. Third, real-time inference is still heavy because of iterative transformer decoding. The authors suggest three corresponding directions: pretraining or finetuning on a more universal speech-audio SSL model such as USAD to better cover non-speech sounds, incorporating explicit lip-reading or audio-visual attention modules to tighten speech-video synchrony, and exploring efficient diffusion or distillation techniques for faster one-shot decoding (Niu et al., 19 Sep 2025).

These limitations align with broader observations from evaluation work. VidAudio-Bench argues for the necessity of task-aware architectures, balanced conditioning, data diversification, prompt engineering in VT2A, and a multi-dimensional evaluation loop using 13 reference-free metrics. It also reports a fundamental tension between instruction following and visually grounded generation in VT2A: dense visual captions improve video-audio semantic consistency but frequently degrade instruction following, because long captions can dilute or conflict with the core category instruction (Zhang et al., 12 Apr 2026). A plausible implication is that BVS partially sidesteps that specific VT2A tension by conditioning stage I on phonetic speech cues and video embeddings rather than on dense visual captions, although this remains an inference rather than a benchmarked claim.

More generally, BVS should be understood as one response to a field-wide transition documented across papers. SVA identifies speech from lip movement and multi-event Foley generation as future scenarios, SyncFusion shows that explicit temporal structure can materially improve synchronization, MultiFoley shows the value of multimodal control and professional SFX supervision, and VidAudio-Bench demonstrates that speech and singing remain the weakest categories under current protocols (Chen et al., 2024, Comunità et al., 2023, Chen et al., 2024, Zhang et al., 12 Apr 2026). Within that trajectory, Beyond Video-to-SFX denotes a move from Foley-only generation toward category-aware video-to-audio systems in which speech intelligibility, environmental context, and synchronization are modeled jointly rather than treated as separate post-processing problems.