UniAnimate: Taming Unified Video Diffusion Models for Consistent Human Image Animation (2406.01188v1)

Abstract: Recent diffusion-based human image animation techniques have demonstrated impressive success in synthesizing videos that faithfully follow a given reference identity and a sequence of desired movement poses. Despite this, there are still two limitations: i) an extra reference model is required to align the identity image with the main video branch, which significantly increases the optimization burden and model parameters; ii) the generated video is usually short in time (e.g., 24 frames), hampering practical applications. To address these shortcomings, we present a UniAnimate framework to enable efficient and long-term human video generation. First, to reduce the optimization difficulty and ensure temporal coherence, we map the reference image along with the posture guidance and noise video into a common feature space by incorporating a unified video diffusion model. Second, we propose a unified noise input that supports random noised input as well as first frame conditioned input, which enhances the ability to generate long-term video. Finally, to further efficiently handle long sequences, we explore an alternative temporal modeling architecture based on state space model to replace the original computation-consuming temporal Transformer. Extensive experimental results indicate that UniAnimate achieves superior synthesis results over existing state-of-the-art counterparts in both quantitative and qualitative evaluations. Notably, UniAnimate can even generate highly consistent one-minute videos by iteratively employing the first frame conditioning strategy. Code and models will be publicly available. Project page: https://unianimate.github.io/.

Overview of UniAnimate: Consistent Human Image Animation Using Unified Video Diffusion Models

The paper "UniAnimate: Taming Unified Video Diffusion Models for Consistent Human Image Animation" addresses the problem of generating temporally coherent human image animations. This research presents UniAnimate, an innovative framework that overcomes existing drawbacks in diffusion-based animation techniques by leveraging unified video diffusion models.

Key Contributions

UniAnimate makes significant strides in improving the efficiency and output quality of human image animation. The primary contributions highlighted in the paper are:

1. Unified Video Diffusion Model: The authors propose a framework that integrates the reference image and the noised video within a unified diffusion model. This approach reduces the burden of separately encoding image features, simultaneously facilitating appearance alignment and temporal coherence.
2. Unified Noise Input Scheme: By introducing a noise input mechanism that supports both random noise and conditioning on the first frame, UniAnimate can generate long-term videos with smooth transitions, thereby overcoming the typical limitations of temporal Transformers in handling extensive sequences.
3. State Space Model for Temporal Modeling: The paper proposes using a state space model architecture to replace the conventional temporal Transformer, mitigating the constraints of quadratic computation and enhancing the model’s ability to handle extended sequences efficiently.

Numerical Performance and Analysis

Extensive experiments conducted on standard datasets, TikTok and Fashion, substantiate the efficacy of UniAnimate. In metrics such as PSNR, SSIM, and FVD, UniAnimate consistently delivers superior performance compared to established techniques like Animate Anyone and MagicAnimate, indicating more accurate and visually coherent animations. For instance, on the TikTok dataset, UniAnimate achieves a PSNR of 30.77 and an FVD of 148.06, underscoring its capability to produce high-quality and temporally consistent videos.

Implications and Future Directions

The introduction of UniAnimate marks a significant advancement in the field of video generation and human image animation, primarily due to its coherent integration of features and focus on long-term video synthesis. By addressing the computational complexities traditionally associated with video diffusion models, UniAnimate sets a precedent for future explorations into more efficient and robust animation frameworks.

Future research could explore augmenting the capacity of these models to handle higher-resolution data, potentially integrating more sophisticated pose estimation techniques. Furthermore, cross-domain applications such as generating animations from various multimedia inputs could benefit from the principles laid out in UniAnimate, providing a broad spectrum of practical implementations in creative industries, entertainment, and virtual reality environments.

Ultimately, UniAnimate opens new avenues for research, where enhancements in computational efficiency directly translate into improved user experiences and broader applicability in real-world scenarios. The framework’s ability to generate seamless, long-duration animations demonstrates its alignment with ongoing advancements in AI, where large-scale, coherent content generation remains a pivotal focus.