StableAnimator: High-Quality Identity-Preserving Human Image Animation (2411.17697v2)

Abstract: Current diffusion models for human image animation struggle to ensure identity (ID) consistency. This paper presents StableAnimator, the first end-to-end ID-preserving video diffusion framework, which synthesizes high-quality videos without any post-processing, conditioned on a reference image and a sequence of poses. Building upon a video diffusion model, StableAnimator contains carefully designed modules for both training and inference striving for identity consistency. In particular, StableAnimator begins by computing image and face embeddings with off-the-shelf extractors, respectively and face embeddings are further refined by interacting with image embeddings using a global content-aware Face Encoder. Then, StableAnimator introduces a novel distribution-aware ID Adapter that prevents interference caused by temporal layers while preserving ID via alignment. During inference, we propose a novel Hamilton-Jacobi-BeLLMan (HJB) equation-based optimization to further enhance the face quality. We demonstrate that solving the HJB equation can be integrated into the diffusion denoising process, and the resulting solution constrains the denoising path and thus benefits ID preservation. Experiments on multiple benchmarks show the effectiveness of StableAnimator both qualitatively and quantitatively.

• The paper introduces StableAnimator, a diffusion framework that improves identity preservation and animation quality in human image animation without post-processing.
• StableAnimator achieves superior identity consistency, outperforming ControlNeXt by 47.1% in CSIM, and maintains high video fidelity with a strong FVD score.
• The framework integrates a Global Content-Aware Face Encoder, a Distribution-Aware ID Adapter, and HJB equation-based optimization to enhance identity preservation and face quality.

An Analysis of StableAnimator: A Framework for High-Quality, Identity-Preserving Human Image Animation

The paper "StableAnimator: High-Quality Identity-Preserving Human Image Animation" proposes a novel approach to human image animation using a diffusion framework that enhances identity consistency without the need for post-processing. This framework, dubbed StableAnimator, addresses significant challenges in the domain of human image animation by ensuring identity preservation across video frames—a task that has been notably difficult with existing diffusion models. The framework is built upon several key innovations that work together to maintain high-quality and ID-consistent video output.

Key Contributions and Methodology

StableAnimator innovates by integrating a series of thoughtfully designed components into a video diffusion model to achieve its objectives. The paper introduces the following primary elements in the methodology:

1. Global Content-Aware Face Encoder: This module improves the integration of face embeddings with a consideration for the global context of the image, such as background and layout. The face embeddings, traditionally isolated and lacking spatial context, are refined through cross-attention with image embeddings in this global content-aware setup.
2. Distribution-Aware ID Adapter: A central challenge in image animation is maintaining the identity consistency while ensuring spatial and temporal fidelity. The ID Adapter tackles this issue by aligning the output of cross-attentional mechanisms on face and image embeddings, counteracting the typical feature distortion introduced by temporal layers in diffusion models. This alignment uses a distribution-focused approach, calculating means and variances to modulate the interaction between face and image embedding outputs.
3. HJB Equation-Based Optimization: To further enhance face quality during the denoising process, StableAnimator applies an optimization strategy based on the Hamilton-Jacobi-BeLLMan (HJB) equation. This approach leverages stochastic optimal control principles to align the face animation output with pre-defined identity preservation objectives.

Evaluation and Results

The experimental evaluation of StableAnimator demonstrates significant improvements over contemporary methods in both quantitative and qualitative measures. Notably, it outperforms existing models like ControlNeXt in the Cosine Similarity Index Metric (CSIM) by 47.1%, showcasing its superior ability to maintain facial identity across animated sequences. Additionally, StableAnimator achieves a notable FVD score of 140.62, indicating high video fidelity. These results suggest that the careful design of training and inference modules effectively addresses the dual objectives of fidelity and identity consistency.

Implications and Future Work

The innovations in StableAnimator have substantial implications for fields such as virtual reality, entertainment, and digital human creation, where high-fidelity and identity-preserved animations are critical. The framework sets a precedent for future research to further explore the integration of advanced mathematical principles, like the HJB equation, in improving generative models’ performance.

Furthermore, the paper suggests several avenues for future developments. These include refining face identity embedding technologies and exploring additional post-processing recovery systems that could further improve animation quality without compromising fidelity or identity.

Overall, "StableAnimator: High-Quality Identity-Preserving Human Image Animation" presents meaningful advancements in the landscape of human animative modeling, offering practical and theoretical insights that could shape future research trajectories in AI-driven image processing. The methodology and subsequent improvements proposed in this paper provide a robust foundation for next-generation identity-preserving animation systems.