Does Hearing Help Seeing? Investigating Audio-Video Joint Denoising for Video Generation
This addresses the problem of enhancing video generation quality for AI systems by leveraging cross-modal training, though it is incremental as it builds on existing audio-video generative methods.
The paper investigates whether audio-video joint denoising training improves video generation quality, even when focusing only on video, and finds consistent improvements on challenging motion subsets, suggesting audio acts as a privileged signal for better video dynamics.
Recent audio-video generative systems suggest that coupling modalities benefits not only audio-video synchrony but also the video modality itself. We pose a fundamental question: Does audio-video joint denoising training improve video generation, even when we only care about video quality? To study this, we introduce a parameter-efficient Audio-Video Full DiT (AVFullDiT) architecture that leverages pre-trained text-to-video (T2V) and text-to-audio (T2A) modules for joint denoising. We train (i) a T2AV model with AVFullDiT and (ii) a T2V-only counterpart under identical settings. Our results provide the first systematic evidence that audio-video joint denoising can deliver more than synchrony. We observe consistent improvements on challenging subsets featuring large and object contact motions. We hypothesize that predicting audio acts as a privileged signal, encouraging the model to internalize causal relationships between visual events and their acoustic consequences (e.g., collision $\times$ impact sound), which in turn regularizes video dynamics. Our findings suggest that cross-modal co-training is a promising approach to developing stronger, more physically grounded world models. Code and dataset will be made publicly available.