JoyVASA: Portrait and Animal Image Animation with Diffusion-Based Audio-Driven Facial Dynamics and Head Motion Generation (2411.09209v4)

Abstract: Audio-driven portrait animation has made significant advances with diffusion-based models, improving video quality and lipsync accuracy. However, the increasing complexity of these models has led to inefficiencies in training and inference, as well as constraints on video length and inter-frame continuity. In this paper, we propose JoyVASA, a diffusion-based method for generating facial dynamics and head motion in audio-driven facial animation. Specifically, in the first stage, we introduce a decoupled facial representation framework that separates dynamic facial expressions from static 3D facial representations. This decoupling allows the system to generate longer videos by combining any static 3D facial representation with dynamic motion sequences. Then, in the second stage, a diffusion transformer is trained to generate motion sequences directly from audio cues, independent of character identity. Finally, a generator trained in the first stage uses the 3D facial representation and the generated motion sequences as inputs to render high-quality animations. With the decoupled facial representation and the identity-independent motion generation process, JoyVASA extends beyond human portraits to animate animal faces seamlessly. The model is trained on a hybrid dataset of private Chinese and public English data, enabling multilingual support. Experimental results validate the effectiveness of our approach. Future work will focus on improving real-time performance and refining expression control, further expanding the applications in portrait animation. The code is available at: https://github.com/jdh-algo/JoyVASA.

• The paper introduces a two-stage diffusion-based framework that decouples static 3D facial features from dynamic, audio-driven expressions to produce extended animations.
• The method employs a diffusion transformer that processes language-agnostic audio cues, enabling motion generation for both human and animal portraits.
• Experimental results with multilingual datasets show competitive image and video quality, demonstrating the framework's adaptability and performance.

JoyVASA: Advanced Techniques in Audio-Driven Image Animation

The paper "JoyVASA: Portrait and Animal Image Animation with Diffusion-Based Audio-Driven Facial Dynamics and Head Motion Generation" presents a novel method for generating animated video content driven by audio inputs, specifically targeting the animation of both human and animal portraits. The approach distinguishes itself through the application of innovative diffusion-based models, enabling the generation of coherent facial dynamics and head motions based solely on audio cues. This work departs from traditional methodologies that rely heavily on reference motions or additional conditions, offering a more generalized and versatile framework.

The proposed method is centered around a two-stage process. In the initial stage, a decoupled facial representation framework is introduced, which effectively separates static 3D facial representations from dynamic facial expressions. This separation allows for the creation of extended video sequences by combining static facial data with dynamic, audio-driven motion sequences, overcoming common limitations related to video length and inter-frame continuity.

In the second stage, the authors implement a diffusion transformer model to generate motion sequences. This model processes audio cues independently from the character identity, thus achieving a level of abstraction that supports diverse character types, including animals. The generated motion sequences, together with static 3D representations, are then used by a pre-trained generator to render high-quality animation. This dual-stage approach broadens the scope of animated characters, extending beyond human likenesses to include non-human entities, thus highlighting the framework's adaptability.

Strong experimental results attest to the effectiveness of this approach. The implementation utilizes a hybrid dataset composed of private Chinese and public English sources, thereby providing multilingual support and demonstrating the model's capability to generalize across different languages and cultural contexts. Comparative analyses with existing methods show that JoyVASA delivers competitive results in terms of image and video quality metrics, synchronization correctness, and temporal smoothness, without requiring supplemental guidance or reference frames.

While JoyVASA provides a robust solution for many previously encountered challenges in audio-driven portrait animation, certain constraints identified in the paper invite further exploration. Notably, the quality of the generated videos can be limited by the first-stage models' performance, including the encoders and decoders used to extract and render facial features. Addressing these limitations in future work could significantly enhance output fidelity and the framework’s applicability in varied use cases.

Apart from improving foundational model performance, potential future research directions may include advancing real-time processing speeds and refining expression control, thereby enhancing user interactivity and manipulation options. Moreover, the implementation of stronger face representation models, such as EMOPortrait, could potentially address existing performance limitations, particularly in handling considerable pose variations.

In closing, JoyVASA represents a meaningful advancement in audio-driven animation frameworks, leveraging diffusion models to achieve high-quality results with a degree of abstraction conducive to a broad array of application scenarios. This work not only pushes the boundaries of what is currently achievable in this domain but also lays the groundwork for future research paths to further enrich autonomous animation systems.