Emergent Mind

Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion Dependency

(2409.02634)

Published Sep 4, 2024 in cs.CV

Abstract

With the introduction of diffusion-based video generation techniques, audio-conditioned human video generation has recently achieved significant breakthroughs in both the naturalness of motion and the synthesis of portrait details. Due to the limited control of audio signals in driving human motion, existing methods often add auxiliary spatial signals to stabilize movements, which may compromise the naturalness and freedom of motion. In this paper, we propose an end-to-end audio-only conditioned video diffusion model named Loopy. Specifically, we designed an inter- and intra-clip temporal module and an audio-to-latents module, enabling the model to leverage long-term motion information from the data to learn natural motion patterns and improving audio-portrait movement correlation. This method removes the need for manually specified spatial motion templates used in existing methods to constrain motion during inference. Extensive experiments show that Loopy outperforms recent audio-driven portrait diffusion models, delivering more lifelike and high-quality results across various scenarios.

Loopy framework achieves natural motion using inter/intra-clip temporal layers and audio-to-latents module.

Overview

The Loopy model introduces a diffusion model for audio-driven portrait video generation, utilizing dual temporal layers to capture long-term motion dependencies and enhance natural movement.
Key components include an audio-to-latents module, template-free design, dual U-Net architecture, and a temporal segment module, all contributing to the model's ability to generate high-fidelity portrait motions from audio inputs.
Extensive experimentation demonstrated Loopy's superior performance in image quality, smoothness, audio-visual synchronization, and motion metrics, outperforming existing methods, especially in complex emotional scenarios.

Overview of "Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion Dependency"

The paper "Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion Dependency," presents a novel approach to audio-driven portrait video generation by introducing an innovative diffusion model named Loopy. The Loopy model specifically addresses the limitations of existing zero-shot audio-driven methods, which often rely on additional spatial conditions to stabilize movements. This reliance potentially compromises the naturalness and freedom of motion.

Key Contributions

Dual Temporal Layers: The Loopy model incorporates inter-clip and intra-clip temporal modules. The inter-clip temporal layer models the relationships between motion frames from preceding clips, whereas the intra-clip temporal layer tightens the temporal relationships within the current clip. This design aids in capturing long-term motion dependency, enhancing the generation of natural, anthropomorphic movements in portrait videos.
Audio-to-Latents Module: This module transforms audio features and facial motion-related features into motion latents, which serve as conditions in the denoising process. This transformation leverages strongly correlated motion conditions to more effectively model the relationship between audio and portrait motion.
Long-Term Temporal Dependency: By extending the temporal receptive field to cover over 100 frames, the model capitalizes on long-term motion information, thus learning and generating more natural and lifelike motion patterns.
Template-Free Design: Unlike previous methods that incorporate auxiliary spatial templates for motion stabilization, Loopy achieves significant improvements in synthesis quality and motion naturalness without such templates. The model avoids the manual specification of spatial motion constraints, leading to higher fidelity in audio-driven motion generation.

Methodological Advances

The research performed extensive experimentation to arrive at the final architecture of Loopy, which builds upon the Stable Diffusion (SD) model. The key methodological enhancements include:

Dual U-Net Architecture: The Loopy model employs a dual U-Net structure, where a reference net processes motion frames and reference images. This reference net works concurrently with the main denoising U-Net, ensuring the integration of long-term temporal dependencies into the generation process.
Temporal Segment Module: This module further extends the temporal coverage by segmenting motion frames and abstracting them effectively. This process helps the model capture motion styles and further solidifies the temporal relationships.
Multistage Training: The training process involves a two-stage approach where the first stage focuses on image-level variations and the second stage incorporates full temporal and audio-conditional training. This methodology ensures the convergence and stability of the model.
Inference Techniques: The paper describes the use of class-free guidance during inference, combining outputs with different condition settings. This technique enhances the model's adaptability to various conditions, fine-tuning the synthesis output.

Experimental Validation

The researchers conducted extensive tests on multiple datasets to validate the performance of Loopy. The evaluation focused on several metrics:

Image Quality (IQA): Measures the visual quality of individual frames.
Smoothness: Assesses the temporal stability of generated videos.
SyncC and SyncD: Metrics to evaluate audio-visual synchronization.
Motion Metrics (Glo and Exp): Quantify global and dynamic facial motion expressiveness.

The results demonstrated that Loopy outperformed existing methods significantly across various metrics, especially in complex and emotional scenarios. The experiments indicated that the proposed inter/intra-clip temporal modules and audio-to-latents module substantially improved the naturalness and stability of the generated videos.

Implications and Future Directions

The proposed methodology has several theoretical and practical implications:

Higher Fidelity in Audio-Driven Motion: By utilizing the long-term temporal dependencies and avoiding manual spatial condition imposition, Loopy can generate more natural and dynamic portrait movements solely from audio inputs.
Template-Free Motion Generation: This capability simplifies the process of video synthesis by eliminating the need for spatial motion templates, making the method more scalable and applicable in various real-world scenarios.
Future Enhancements: The Loopy framework opens avenues for further research in optimizing the temporal segment module and exploring different motion modeling techniques to balance the trade-off between computational complexity and motion fidelity.

In conclusion, the development of Loopy represents a significant step toward more natural and flexible audio-driven portrait video generation. The integration of long-term motion dependency and advanced temporal modeling sets a new standard in the field, paving the way for future advancements in AI-driven human video synthesis.

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