CyberHost: Taming Audio-driven Avatar Diffusion Model with Region Codebook Attention (2409.01876v3)

Abstract: Diffusion-based video generation technology has advanced significantly, catalyzing a proliferation of research in human animation. However, the majority of these studies are confined to same-modality driving settings, with cross-modality human body animation remaining relatively underexplored. In this paper, we introduce, an end-to-end audio-driven human animation framework that ensures hand integrity, identity consistency, and natural motion. The key design of CyberHost is the Region Codebook Attention mechanism, which improves the generation quality of facial and hand animations by integrating fine-grained local features with learned motion pattern priors. Furthermore, we have developed a suite of human-prior-guided training strategies, including body movement map, hand clarity score, pose-aligned reference feature, and local enhancement supervision, to improve synthesis results. To our knowledge, CyberHost is the first end-to-end audio-driven human diffusion model capable of facilitating zero-shot video generation within the scope of human body. Extensive experiments demonstrate that CyberHost surpasses previous works in both quantitative and qualitative aspects.

• The paper introduces a Region Codebook Attention mechanism that enhances local feature generation for face and hand regions.
• It employs human-prior-guided strategies, including Body Movement Map and Hand Clarity Score, to reduce uncertainty in audio-driven motion synthesis.
• Quantitative metrics such as SSIM, PSNR, FID, and FVD demonstrate CyberHost's superiority over traditional two-stage animation frameworks.

CyberHost: Enhancing Audio-driven Human Animation via Region Codebook Attention

The paper on CyberHost introduces an audio-driven human animation model equipped with an innovative Region Codebook Attention mechanism aimed at improving local feature generation, particularly focusing on the human face and hands. This work contributes significantly to the field of audio-driven animation by addressing prevalent challenges such as hand integrity, identity consistency, and the natural rendering of motion, which have been relatively underexplored in cross-modality animation research.

CyberHost's primary advancement lies in its Region Codebook Attention, which leverages a learnable spatio-temporal memory bank—a motion codebook—designed to capture general local structural features, including topological details and motion patterns. This allows for refined synthesis of critical regions by integrating identity-specific details extracted from cropped local images. This dual approach, incorporating both learned priors from a broad dataset and identity descriptors, enhances the model’s capacity for detailed and personalized feature representation.

The paper further propounds a comprehensive set of human-prior-guided training strategies, formulated to reduce motion generation uncertainty derived from weak audio-to-motion correlations. Key innovations in this respect include:

1. Body Movement Map: A control condition used to indicate anticipated body movement ranges, which is encoded and integrated into the model to stabilize root node movements.
2. Hand Clarity Score: This score indicates the clarity of hand regions in training images—integral for mitigating motion blur effects—thereby enhancing the robustness of hand region synthesis.
3. Pose-aligned Reference Feature: This uses encoded skeleton maps for capturing the structure of the reference image, ensuring topological consistency in generated outputs.
4. Local Enhancement Supervision: Involves the implementation of auxiliary losses to prioritize learning in regions with richer detail, such as facial landmarks, which are pivotal for enhancing facial synthesis accuracy.

Quantitative and qualitative evaluations indicate that CyberHost outperforms existing methodologies, including two-stage frameworks, in generating realistic and expressive human animation. The model demonstrates superior performance across metrics including SSIM, PSNR, FID, and FVD, indicating improvements in video quality and synchronization of audio-visual elements. Notably, CyberHost achieves significant advancements in zero-shot video generation capabilities, attesting to its generalization potential in open-set scenarios.

In terms of theoretical implications, this paper highlights the efficacy of integrating cross-modal attention mechanisms to enhance diffusion models' aptitude in synthesizing complex audiovisual patterns. The flexibility of the architecture suggests future research directions, including enhancing multimodal integration to support varied driving signals and exploring optimization techniques that further stabilize and refine audio-driven full-body animation. This prospect opens up new avenues in both commercial applications and theoretical research for human-video generation technologies.

CyberHost stands as a pivotal contribution to the domain of cross-modality synthesis, in which fine-tuning localized feature generation and addressing the nuances of multimodal learning may spearhead innovations in creating richly detailed, life-like animations.