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The Power of Sound (TPoS): Audio Reactive Video Generation with Stable Diffusion

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arxiv 2309.04509 v1 pith:6MDF2NZI submitted 2023-09-08 cs.SD cs.CVcs.GReess.AS

classification cs.SDcs.CVcs.GReess.AS
keywords audiotposgenerationvideoaudio-to-videodiffusionmagnitudemodel
verification ladder T0 review T1 audit T2 compute T3 formal
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In recent years, video generation has become a prominent generative tool and has drawn significant attention. However, there is little consideration in audio-to-video generation, though audio contains unique qualities like temporal semantics and magnitude. Hence, we propose The Power of Sound (TPoS) model to incorporate audio input that includes both changeable temporal semantics and magnitude. To generate video frames, TPoS utilizes a latent stable diffusion model with textual semantic information, which is then guided by the sequential audio embedding from our pretrained Audio Encoder. As a result, this method produces audio reactive video contents. We demonstrate the effectiveness of TPoS across various tasks and compare its results with current state-of-the-art techniques in the field of audio-to-video generation. More examples are available at https://ku-vai.github.io/TPoS/

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. When RLHF Fails: A Mechanistic Taxonomy of Reward Hacking, Collapse, and Evaluator Gaming

    cs.LG 2026-06 unverdicted novelty 6.0 of 10

    RLHF failures are classifiable training transitions: aggressive PPO yields localized reward hacking that row-level diagnostics catch and pre-transition features partially predict.

  2. Secure & Personalized Music-to-Video Generation via CHARCHA

    cs.AI 2025-02 conditional novelty 4.0 of 10

    An automated pipeline generates personalized music videos from audio alone, using a CAPTCHA-style liveness check (CHARCHA) to collect and protect the user's facial identity.

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