Audio-Synchronized Visual Animation (2403.05659v2)

Abstract: Current visual generation methods can produce high quality videos guided by texts. However, effectively controlling object dynamics remains a challenge. This work explores audio as a cue to generate temporally synchronized image animations. We introduce Audio Synchronized Visual Animation (ASVA), a task animating a static image to demonstrate motion dynamics, temporally guided by audio clips across multiple classes. To this end, we present AVSync15, a dataset curated from VGGSound with videos featuring synchronized audio visual events across 15 categories. We also present a diffusion model, AVSyncD, capable of generating dynamic animations guided by audios. Extensive evaluations validate AVSync15 as a reliable benchmark for synchronized generation and demonstrate our models superior performance. We further explore AVSyncDs potential in a variety of audio synchronized generation tasks, from generating full videos without a base image to controlling object motions with various sounds. We hope our established benchmark can open new avenues for controllable visual generation. More videos on project webpage https://lzhangbj.github.io/projects/asva/asva.html.

• The paper introduces a novel task (ASVA) to animate static images using audio cues for precise temporal synchronization.
• It presents a diffusion-based model (AVSD) that integrates frozen audio features and ImageBind tokens to capture fine-grained motion dynamics.
• The curated ASVA dataset and robust evaluation metrics (FID, IA, IT, FVD, AlignSync, RelSync) demonstrate the model's effectiveness in multimedia applications.

An Analysis of "Audio-Synchronized Visual Animation"

The paper "Audio-Synchronized Visual Animation" proposes a nuanced approach to the field of video generation, particularly focusing on temporal synchronization using audio cues. Traditional models in visual generation have largely emphasized text as the controlling input, which typically offers semantic guidance at a broader scale. However, this research redirects the paradigm by utilizing audio as an input source, given its potential for precise temporal control. This paper introduces a novel task called Audio-Synchronized Visual Animation (ASVA), designed to animate static images such that the resultant motions are temporally synchronized with auditory inputs.

The authors efficiently tackled prominent challenges in the domain of synchronized generation. Firstly, constructing a dataset with intrinsic audio-visual synchronization proved challenging due to the noisy nature and unsynchronized audio-visual pairs in existing datasets. The authors addressed this by creating ASVA, a dataset derived from VGGSound, featuring a curated selection of videos across 15 categories focusing on synchronized events. The curation process involved a two-tier pipeline–automatic filtering for preliminary selection and manual verification for final refinement, ensuring high-quality audio-visual synchronization.

On the methodological front, they showcased a diffusion model—termed Audio-Video Synchronized Diffusion (AVSD)—crafted to generate animations that are dynamically and temporally aligned with audio. Unlike prior models, which failed to capture the nuanced synchronization of motion, the AVSD integrates frozen audio features into the diffusion process, enabling the model to capture fine-grained temporal dynamics. The architecture benefits from advancements like ImageBind, which encodes audio into segmented tokens, allowing enhanced synchronization.

The results indicate the efficacy of this integrated approach. Beyond subjective observations, the authors present a robust numerical evaluation using metrics like FID, IA, IT, FVD, as well as novel synchronization metrics (AlignSync and RelSync) specifically designed to gauge audio-video synchronization. These metrics offer insight into the semantic and temporal coherence inherent in the generated outputs.

The paper's contributions to the field are substantial, as it broadens the scope of controllable visual generation to leverage audio for synchronized control. The introduction of ASVA sets a precedent for future benchmarks in this niche, implying significant implications for applications where audio-driven temporal precision is paramount. This includes areas ranging from multimedia production to augmented reality interfaces, where the synchrony between visual content and auditory cues is critical.

Furthermore, this research paves the way for speculative future developments in AI, particularly in enhancing multimodal learning and understanding. The methodology outlined in AVSD could be extrapolated to improve current models by integrating more complex synchronization cues. The potential exists to apply this framework across other domains, encouraging the development of models that replicate intricate real-world interactions more authentically.

In conclusion, while the paper’s focus on audio-visual synchronization through ASVA adds a unique dimension to video generation, it simultaneously highlights a limitation—its scalability and generalizability across a broader range of audio-visual classes remain untested. Expanding the dataset and refining the models to accommodate a diverse range of sounds are promising directions for subsequent research. Yet, the groundwork established in this paper surely positions it as a valuable reference point for advances in synchronizing multi-sensory data streams within AI and machine learning.