- The paper introduces MusicInfuser, which adapts pre-trained text-to-video diffusion models for music-driven video synthesis using lightweight cross-attention and LoRA mechanisms, eliminating the need for motion capture data.
- Evaluations show MusicInfuser outperforms previous models in style, beat alignment, movement realism, and video quality across diverse music genres and video lengths.
- MusicInfuser enables creative applications in music video production and virtual concerts and offers a new paradigm for integrating auditory cues into existing video diffusion models.
Overview of MusicInfuser: Making Video Diffusion Listen and Dance
The paper "MusicInfuser: Making Video Diffusion Listen and Dance" introduces an innovative method for generating dance videos that are synchronized to specific music tracks. The approach, termed "MusicInfuser," leverages existing pre-trained text-to-video diffusion models to accommodate musical inputs seamlessly. Unlike previous efforts that depend on complex multimodal models or resource-intensive motion capture data, this research demonstrates a streamlined adaptation by introducing lightweight music-video cross-attention mechanisms and a low-rank adapter. Consequently, this enables high-quality music-driven video generation while maintaining the flexibility and diverse capabilities of the foundational diffusion models.
Methodological Insights
The crux of MusicInfuser's approach lies in its ability to adapt pre-trained text-to-video models for music-driven video synthesis without requiring motion capture data or new multimodal models. By integrating music-video cross-attention and a low-rank adapter, the method aligns video generation models with musical rhythms. This is achieved through the development of a Zero-Initialized cross-attention (ZICA) mechanism, which interacts with video tokens and music features, initially maintaining unaffected video outputs and gradually incorporating auditory influence through controlled training of adaptor networks. Moreover, the paper also introduces a Higher Rank Low-Rank Adaptation (HR-LoRA), which increases adaptability specifically for video tokens by accommodating the complexity associated with temporal aspects of video generation.
To optimize the training process further, a novel noise scheduling strategy named Beta-Uniform Scheduling is devised. This technique transitions during training from a Beta distribution—emphasizing low noise levels—to a uniform distribution, ensuring that fine aspects of choreography are accommodated early in training to maintain the pre-trained model's choreography knowledge. Furthermore, the paper enriches the model's generalization capabilities by training on a combination of structured datasets and diverse in-the-wild videos sourced from online platforms like YouTube. Distinct and parameterized captioning approaches help models retain a prompt adherence, fostering prompt-based stylistic control.
Experimental Evaluation
Assessing the synthesized dance videos involves a comprehensive evaluation framework using advanced Video-LLMs capable of processing complex multimodal information effectively. The results on various quality metrics indicate that MusicInfuser excels in style and beat alignment, movement realism, and video quality compared to state-of-the-art models like MM-Diffusion and Mochi. Adaptability across diverse music genres—including unseen tracks—and extended video lengths further signify the method's robustness and scalability. Moreover, the built-in text prompts allow for dynamic alterations to dance style and setting, marking MusicInfuser as a versatile tool for audio-driven choreography synthesis.
Implications and Future Outlook
Practically, MusicInfuser paves the way for creative applications in music video production, virtual concerts, and dance education, where personalized and instantaneous music-choreographed videos are desirable. On a theoretical frontier, it offers a paradigm shift in integrating auditory cues with video diffusion models for generative tasks. By circumventing traditional reliance on motion capture or large-scale joint models, MusicInfuser demonstrates a compelling direction in embedding multimodal capabilities into existing generative frameworks.
Future research paths include further refinement of model adaptability to capture even more complex and nuanced dance styles and deeper integration of textual inputs for enhanced choreography specificity. The ongoing evolution of diffusion models presents vast opportunities to explore further incorporation of music and its emotional dynamics, perhaps through expanded cross-modality attention architectures or more intricate noise scheduling mechanisms. As the discourse in generative AI progresses, MusicInfuser stands as a noteworthy contribution, shaping the narrative of music-driven video synthesis.