Do It Yourself

AnimateAnyone

Stable character animation with Stable Diffusion

Moore-AnimateAnyone is based on the Animate Anyone↗︎ framework, which is an advanced tools in the field of image-to-video synthesis, particularly for character animation. Like Deforum↗︎ or Neural Frames↗︎ it uses Stable Diffusion↗︎ for generating the frames, but with more coherent in style and less flickering, which offer greater capabilities for consistent video and animation generation for characters.

Main Features:

  1. Image-to-Video Synthesis: The primary function of Moore-AnimateAnyone is to transform static images into animated videos. This tool is adept at maintaining the intricate details of the original image while enabling the control of poses in the video.
  2. High Resolution: The generated results can reach resolutions of 512x768, showcasing the tool's capability to produce high-quality animations.
  3. Sophisticated Training and Inference: Moore-AnimateAnyone employs a two-stage training process, focusing initially on single frames before addressing temporal aspects, ensuring both spatial and temporal consistency in the animations.
  4. Python-Based Environment: The tool is implemented in Python and recommends using Python version 3.10 or higher.

Limitations and Future Plans:

  • Current limitations include occasional artifacts in backgrounds and suboptimal results due to scale mismatches. Additionally, some flickering and jittering may occur in certain scenarios.
  • Moore-AnimateAnyone plans to address these issues in future updates to enhance the tool's capabilities.

Community and Support:

  • The project is open source under the Apache-2.0 license, with ongoing community contributions and support, including a dedicated version for Windows users.
  • A Gradio Demo is available, allowing users to preview Moore-AnimateAnyone's capabilities through HuggingFace Spaces↗︎.

Animate Anyone

Key Highlights:

  • Purpose: Animate Anyone is designed for converting static character images into animated videos, emphasizing detail preservation and pose control.
  • Components: The framework includes ReferenceNet for maintaining appearance details and a Pose Guider for controlling and varying the character’s pose.
  • Temporal Modeling: This feature ensures smooth transitions between frames for consistent animation.
  • Training Strategy: Animate Anyone employs a unique two-stage training process, enhancing both spatial and temporal consistency.
  • Applications: The framework is suitable for applications such as fashion video synthesis and human dance generation, demonstrating state-of-the-art results in human video synthesis benchmarks.

Limitations and Future Potential:

  • Current challenges include handling hand movements, unseen character parts, and operational efficiency.
  • The method has the potential to be foundational for various image-to-video applications beyond character animation.