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Soundini: Sound-Guided Diffusion for Natural Video Editing

Seung Hyun Lee, Sieun Kim, Innfarn Yoo, Feng Yang, Donghyeon Cho, Youngseo Kim, Huiwen Chang, Jinkyu Kim, Sangpil Kim

arXiv:2304.06818v1cs.CV

TL;DR

Video editing methods struggle to combine dynamic, sound-responsive effects with temporal coherence. Soundini guides diffusion using audio latents and optical flow, and reports realistic, state-of-the-art edits; performance degrades when mask shape mismatches the sound content.

  • Problem

    Existing video-editing approaches emphasize temporal consistency or static styles, leaving dynamic visual-style changes over time difficult to achieve.

    Source: §1 · p. 2

  • Method

    Soundini guides diffusion with pre-trained audio latents for sound-responsive motion and appearance, and uses optical-flow guidance to match warped adjacent frames.

    Source: §1 · p. 2, §3 · p. 3, §3.2 · p. 5

  • Results

    Soundini produces realistic edits from varied sound sources and achieves state-of-the-art style-animation performance over existing video-editing methods, improving perceptual realism.

    Source: §1 · pp. 2–3, §6 · p. 8

  • Takeaways & Limitations

    The method supports local sound-guided video effects with temporal consistency, and also demonstrates global editing such as an object slowly sinking under underwater-bubbling sound.

    Source: §4.2.4 · p. 7, §6 · p. 8

    Performance degrades when the mask shape does not match the sound content within the masked area.

    Source: §5 · p. 8

Abstract

from arXiv · show

We propose a method for adding sound-guided visual effects to specific regions of videos with a zero-shot setting. Animating the appearance of the visual effect is challenging because each frame of the edited video should have visual changes while maintaining temporal consistency. Moreover, existing video editing solutions focus on temporal consistency across frames, ignoring the visual style variations over time, e.g., thunderstorm, wave, fire crackling. To overcome this limitation, we utilize temporal sound features for the dynamic style. Specifically, we guide denoising diffusion probabilistic models with an audio latent representation in the audio-visual latent space. To the best of our knowledge, our work is the first to explore sound-guided natural video editing from various sound sources with sound-specialized properties, such as intensity, timbre, and volume. Additionally, we design optical flow-based guidance to generate temporally consistent video frames, capturing the pixel-wise relationship between adjacent frames. Experimental results show that our method outperforms existing video editing techniques, producing more realistic visual effects that reflect the properties of sound. Please visit our page: https://kuai-lab.github.io/soundini-gallery/.

1. Introduction

The introduction identifies a gap in dynamic, temporally coherent video effects and proposes combining sound-guided diffusion with optical-flow guidance. It reports more realistic, state-of-the-art style animation compared with existing video-editing methods.

1. Introduction

  • The paper frames video editing as useful for movie-making and social media, while manual frame-by-frame editing requires substantial effort to maintain temporal coherence.User-aided tools can add effects such as drizzling or explosions to images and videos [9] [11].Source: §1 · pp. 1–2
  • Existing decomposition-based methods edit specific objects, while GAN-based approaches struggle to produce dynamic effects because they are limited to static styles [7] [29] [4].Although decomposition-based methods have strong temporal consistency, they cannot edit arbitrary video regions.Source: §1 · p. 2
  • Sound can represent dynamic scene context through timbre, intensity, and volume, but GAN-based video editing requires latent projection that trades reconstruction quality against editability [33] [34] [35] [3] [5] [51].The introduction motivates diffusion as an alternative, while noting that adding effects with DDPMs remains difficult because adjacent-frame motion knowledge is absent.Source: §1 · p. 2
  • The framework guides diffusion with pre-trained audio latents for sound-driven motion and appearance, while optical flow matches warped adjacent frames for temporal consistency.The paper also describes optical-flow guidance using bidirectional flow and pixel-wise warping between neighboring frames.Source: §1 · p. 2, Figure 3 · p. 3
  • Sound-guided style animation achieves state-of-the-art performance over existing video-editing methods, improving perceptual realism.The method is presented as producing realistic edits from various sound sources, including a thunderstorm-like ocean effect.Source: §1 · pp. 2–3

2. Related Work

Related work spans object-focused video editing, text-driven editing, diffusion, and sound-driven synthesis. The paper distinguishes its approach by targeting movable, audio-responsive visual changes alongside temporal consistency.

Video Editing

  • Prior GAN-based video-editing works [12] attempted optical-flow constraints between adjacent frames without further training on the video dataset; StyleGAN-based semantic editing [4] [58] edits each frame and focuses on faces and the GAN latent-space domain.Source: §2 · p. 3
  • Text-prompt video editors cannot produce movable visual effects when focused on consistent object content, while autoregressive approaches depend heavily on visual-patch order [7].Text-driven video diffusion models show successful motion and appearance editing, whereas Soundini controls continuous visual changes through audio manipulation [39] [56].Source: §2 · p. 3

Diffusion Models

  • Image diffusion guidance can control appearance and style without further training, but does not model relationships between frames needed for temporally consistent video [6] [22] [40].The paper positions optical-flow guidance as its response to this gap.Source: §2 · p. 3

Sound-Driven Visual Synthesis

  • Prior sound-driven synthesis uses audio for fine-detail style and appearance [34] [36], but cited sound-guided image methods do not generate audio-relevant motion [35] [42] [28].MM-Diffusion generates audio and video simultaneously but requires paired audio-visual training data [45].Source: §2 · p. 3

3. Method

Sound-guided diffusion uses audio features to shape local visual changes, while bidirectional optical-flow guidance promotes consistency across frames. The method also preserves source backgrounds and adjusts the strength of flow guidance to balance consistency and motion.

3. Method

  • The framework takes a mel spectrogram, binary mask, and source video as input and predicts an edited video using local-sound and optical-flow guidance.The mask specifies the region of interest for the edit.Source: §3 · p. 3

3.1. Local Sound Guidance

  • Local sound guidance matches video frames to audio-chunk embeddings and aligns adjacent-frame visual changes with sound transitions.The framework samples sound chunks along the mel-spectrogram timeline; Figure 4 illustrates semantic transitions and stronger visual changes as explosion volume rises.Source: §3.1 · pp. 4–5, Figure 4 · p. 4

3.2. Optical Flow Guidance

  • Bidirectional optical flow warps adjacent sampled frames for a pixel-level consistency loss, while background guidance preserves source-frame details.The background loss combines pixel L1 and perceptual loss; increasing flow guidance over denoising steps balances temporal consistency with dynamic motion.Source: §3.2 · pp. 5–6

4. Result

Section 4 introduces the details of the implementation and experiments.

4. Result

  • The section discusses the implementation and experiments.No further implementation or experimental details are specified in the supplied passage.Source: §4 · p. 6

4.1. Implementation Details

Evaluation uses DAVIS videos resized to 256×256 with 10 sampled frames, and the implementation sets guidance weights and sampling steps while also presenting a global-editing example.

4.1. Implementation Details

  • Evaluation on DAVIS uses 256×256 videos and 10 frames, measuring temporal consistency with T-diff, quality with FVD, FID, and IS, and semantic alignment with CLIP score.FID and IS average activations across frames; FVD uses Inception3D pretrained on Kinetics-400.Source: §4.1 · p. 6
  • For global editing, the source video is changed according to underwater bubbling sound, with image cloning used for natural blending.The figure caption identifies this as editing global areas of the video.Source: Figure 8 · p. 6
  • The guidance weights λSG, λDSG, λflow, and λback are 1000, 1000, 500, and 1000, respectively, with T set to 100.The authors report saturated performance improvement above 100 steps and a trade-off between sampling cost and total steps.Source: §4.1 · p. 6

4.2. Qualitative Analysis

The qualitative results show sound-driven semantic and volume changes, compare editing approaches, and demonstrate context-adaptive local effects and sound-guided global editing.

4.2.1 Effectiveness of Sound in Video Editing

  • Changing from fire crackling to explosion sound produces a corresponding semantic transition, while increasing explosion volume makes the visual style more noticeable.The reported transition is guided by acoustic features without latent interpolation.Source: §4.2.1 · pp. 6–7

4.2.2 Comparison with Baselines

  • Compared with NLA, our framework produces more realistic and natural local video edits; unlike NLA’s static added effect, it stylizes desired parts with audio while maintaining temporal consistency.For fairness, frames were 256 × 256 and the number of frames was ten. The method is more temporally consistent than blended diffusion and VQGAN+CLIP [14]; blended diffusion fails to maintain geometric properties between frames, while VQGAN+CLIP motion is not related to text or audio meaning, and our method delivers explicit pixel-level motion with style changes according to sound transition or volume.Source: §4.2.2 · p. 7

4.2.3 Context Adaptive Video Editing

  • Using the video’s global context, the method makes a fire effect appear swept away by waves when guided by fire-crackling sound.The authors contrast considering the entire video context with considering only the edited area.Source: §4.2.3 · p. 7

4.2.4 Sound Guided Video Global Editing

  • Global editing treats the entire frame as foreground and blends sampled frames with the source using image cloning to preserve target texture and lighting.The example shows an object slowly sinking in water in response to underwater bubbling sound.Source: §4.2.4 · p. 7

4.3. Quantitative Evaluation

On DAVIS, the authors report higher video quality and semantic consistency than baselines, with optical-flow guidance improving results and temporal consistency exceeding all baselines except NLA.

Quantitative Evaluation

  • Optical-flow guidance yields higher temporal consistency than the other baselines except NLA.The authors attribute NLA’s exception to geometric style transformations without changes in fine-grained details.Source: §4.3 · p. 7
  • Figure 10 contrasts sampling without Lflow, without adjusting Lflow, and with increasing Lflow after optical-flow guidance.The quantitative comparison uses DAVIS and ten audio types, including fire crackling, explosion, thunderstorm, and underwater bubbling.Source: §4.3 · p. 7, Figure 10 · p. 8
  • The method obtains higher video quality than baselines and the best semantic consistency; adding Lflow improves results over omitting it.The authors attribute quality regulation to optical-flow guidance managing style variety across independently sampled frames.Source: §4.3 · p. 8

4.4. User Study

In a study with 100 Amazon Mechanical Turk participants, Soundini outperformed three comparison methods on semantic consistency and realness.

4.4. User Study

  • Soundini outperformed NLA, Blended Diffusion, and VQGAN+CLIP on semantic consistency and realness in the human evaluation.Participants answered 20 questions comparing edited results.Source: §4.4 · p. 8
  • The user study recruited 100 participants from Amazon Mechanical Turk.Source: §4.4 · p. 8
  • Participants rated perceptual realness on a Likert scale from 1 (low) to 5 (high).Source: §4.4 · p. 8

4.5. Ablation Study

The ablation study reports that optical-flow guidance improves temporal consistency, while independent sampling fails to maintain it. A separate example shows a mask–sound mismatch where Soundini does not produce the intended natural video.

4.5. Ablation Study

  • Optical-flow guidance produces higher temporal consistency than versions removing the flow loss or its scheduling, while independent sampling fails to maintain consistency.Figure 10 compares these variants and denoised frames across adjacent-frame time steps.Source: §4.5 · p. 8
  • With bird song and a heart-shaped mask, Soundini fails to produce a natural video associated with birds’ wing beats.Source: Figure 11 · p. 8
  • The ablation contrasts optical-flow guidance with independent sampling that does not account for adjacent-frame relationships.Source: §4.5 · p. 8

5. Discussion

Soundini’s performance degrades when the mask shape does not match the sound content in the masked region, consistent with its focus on pixel-wise relationships between adjacent frames. The discussion also flags risks from inappropriate or deceptive edits.

Limitation

  • Soundini’s performance degrades when the mask shape is inconsistent with the sound content inside the mask.The authors attribute this limitation to the method’s focus on pixel-wise relationships between adjacent frames.Source: §5 · p. 8

Societal Impact

  • Because users control the edits, the authors identify a risk of generating immoral or inappropriate visual content.Source: §5 · p. 8
  • The authors warn that fake-video misuse involving public figures could erode trust in media.Source: §5 · p. 8

6. Conclusion

Soundini uses sound and localization masks to add realistic visual effects to videos through local sound guidance for diffusion. The conclusion highlights audio-visual embeddings and optical-flow guidance for temporal consistency and context-adaptive results.

6. Conclusion

  • Soundini adds realistic visual effects to videos using local sound guidance for diffusion, given sound and localization masks.Source: §6 · p. 8
  • The framework uses an audio-visual embedding space to edit videos across various sound sources.Source: §6 · p. 8
  • The authors describe optical-flow-guided diffusion sampling as helpful for temporal consistency and context-adaptive results.Source: §6 · p. 8

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