X-Dyna: Expressive Dynamic Human Image Animation (2501.10021v2)

Abstract: We introduce X-Dyna, a novel zero-shot, diffusion-based pipeline for animating a single human image using facial expressions and body movements derived from a driving video, that generates realistic, context-aware dynamics for both the subject and the surrounding environment. Building on prior approaches centered on human pose control, X-Dyna addresses key shortcomings causing the loss of dynamic details, enhancing the lifelike qualities of human video animations. At the core of our approach is the Dynamics-Adapter, a lightweight module that effectively integrates reference appearance context into the spatial attentions of the diffusion backbone while preserving the capacity of motion modules in synthesizing fluid and intricate dynamic details. Beyond body pose control, we connect a local control module with our model to capture identity-disentangled facial expressions, facilitating accurate expression transfer for enhanced realism in animated scenes. Together, these components form a unified framework capable of learning physical human motion and natural scene dynamics from a diverse blend of human and scene videos. Comprehensive qualitative and quantitative evaluations demonstrate that X-Dyna outperforms state-of-the-art methods, creating highly lifelike and expressive animations. The code is available at https://github.com/bytedance/X-Dyna.

• The paper introduces a Dynamics-Adapter that efficiently integrates reference appearance into the diffusion backbone to preserve dynamic motion details.
• It employs a local facial expression control module to capture identity-disentangled expressions for synchronized and lifelike animation.
• X-Dyna outperforms state-of-the-art methods by significantly reducing DTFVD, demonstrating enhanced dynamic texture and motion realism.

Overview of X-Dyna: Expressive Dynamic Human Image Animation

The paper introduces X-Dyna, a novel framework for zero-shot human image animation that is diffusion-based. It leverages facial expressions and body movements derived from a driving video to animate a static human image. By integrating realistic and context-aware dynamics for both the human subject and the surrounding environment, X-Dyna advances previous efforts in this domain, which have predominantly focused on pose control but often lack dynamic detail fidelity.

Core Contributions of X-Dyna

X-Dyna addresses several critical aspects of human image animation, introducing the Dynamics-Adapter as a central component. This lightweight module integrates the reference appearance context into the spatial attentions of the diffusion backbone, thereby preserving the animated subject's dynamic details while maintaining the fidelity of motion generation. Key contributions include:

1. Dynamics-Adapter: Unlike standard approaches that impose strong appearance constraints leading to static results, this module allows for efficient reference appearance integration while preserving the generative backbone's capacity for dynamic synthesis.
2. Local Control Module for Facial Expressions: X-Dyna employs a novel technique for capturing identity-disentangled facial expressions, enhancing the realism of synchronized facial animation across identities.
3. Unified Framework: By learning from a diverse mix of human and scene videos, X-Dyna intelligently synthesizes natural scene dynamics and physical human motion, outperforming state-of-the-art methods in both qualitative and quantitative evaluations.

Methodological Insights

The methodology section of the paper elucidates the architectural innovations brought forth by X-Dyna. A pretrained Stable Diffusion (SD) model augmented with ControlNet handles human pose control, while an additional control module focuses on facial expressions. The Dynamics-Adapter maintains consistency across frames by injecting detailed appearance through supervised query and output projections within the UNet. This setup is optimized to balance appearance fidelity with motion realism, trained across a vast dataset of human and scene videos.

Evaluation and Results

In experimental evaluations, X-Dyna demonstrates superior performance in generating visually expressive animations compared to contemporary methods like MagicAnimate and MimicMotion. Metrics such as Dynamic Texture Frechet Video Distance (DTFVD) and content-debiased FVD were employed to quantitatively assess the dynamic texture generation and animation quality. Notably, X-Dyna achieved significant reductions in DTFVD, indicating enhanced dynamic detail fidelity in both foreground and background elements.

The research shows that X-Dyna's advancements have practical applications in fields like digital arts, virtual humans, and social media content creation, all requiring high levels of animation realism and contextual consistency. The ability of X-Dyna to animate images effectively from diverse sources points towards a future where virtual human synthesis can achieve levels of accuracy and expressiveness previously unattainable.

Future Implications and Developments

The work opens potential pathways for subsequent research and application in AI-generated media. Future developments could explore integrating more complex environmental interactivity and motion dynamics through further advancements in diffusion models and deep learning techniques. Such progress could undoubtedly enhance the scope of virtual reality experiences and synthetic media production.

Overall, the paper presents a cohesive vision for the enhancement of human image animation technologies, highlighting X-Dyna's capability to blend intricate control mechanisms with dynamic synthesis to push the boundaries of what is achievable in zero-shot image animation.