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--- |
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license: mit |
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language: |
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- en |
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pipeline_tag: image-to-video |
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tags: |
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- GAN |
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- U-Net |
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--- |
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# Model Card: Ayo_Generator for GIF Frame Generation |
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## Model Overview |
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The **Ayo_Generator** model is a GAN-based architecture designed to generate animated sequences, such as GIFs, from a single input image. The model uses a combination of CNN layers, upsampling, and attention mechanisms to produce smooth, continuous motion frames from a static image input. The architecture is particularly suited for generating simple animations (e.g., jumping, running) in pixel-art styles or other low-resolution images. |
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## Intended Use |
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The **Ayo_Generator** can be used in creative projects, animation generation, or for educational purposes to demonstrate GAN-based sequential generation. Users can input a static character image and generate a sequence of frames that simulate motion. |
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### Applications |
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- **Sprite Animation for Games:** Generate small animated characters from a single pose. |
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- **Educational Demos:** Teach GAN-based frame generation and image-to-motion transformations. |
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- **GIF Creation:** Turn still images into animated GIFs with basic motion patterns. |
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## How It Works |
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1. **Input Image Encoding:** The input image is encoded through a series of convolutional layers, capturing spatial features. |
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2. **Frame-Specific Embedding:** Each frame is assigned an embedding that indicates its position in the sequence. |
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3. **Sequential Frame Generation:** Each frame is generated sequentially, with the generator network using the previous frame as context for generating the next. |
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4. **Attention and Skip Connections:** These features help retain spatial details and produce coherent motion across frames. |
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## Model Architecture |
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- **Encoder:** Uses multiple convolutional layers to encode the input image into a lower-dimensional feature space. |
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- **Dense Layers:** Compress and embed the encoded information to capture relevant features while reducing dimensionality. |
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- **Decoder:** Upsamples the compressed feature representation, generating frame-by-frame outputs. |
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- **Attention and Skip Connections:** Improve coherence and preserve details, helping to ensure continuity across frames. |
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## Training Data |
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The **Ayo_Generator** was trained on a custom dataset containing animated characters and their associated motion frames. The dataset includes: |
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- **Character Images:** Base images from which motion frames were generated. |
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- **Motion Frames:** Frames for each character to simulate movement, such as walking or jumping. |
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### Data Preprocessing |
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Input images are preprocessed to 128x128 resolution and normalized to a [-1, 1] scale. Frame embeddings are incorporated to help the model understand sequential order, with each frame index converted into a unique embedding vector. |
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## Sample GIF Generation |
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Given an input image, this example code generates a series of frames and stitches them into a GIF. |
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```python |
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import imageio |
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input_image = ... # Load or preprocess an input image as needed |
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generated_frames = [generator(input_image, tf.constant([i])) for i in range(10)] |
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# Save as GIF |
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with imageio.get_writer('generated_animation.gif', mode='I') as writer: |
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for frame in generated_frames: |
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writer.append_data((frame.numpy() * 255).astype(np.uint8)) |
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``` |
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## Evaluation Metrics |
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The model was evaluated based on: |
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- **MSE Loss (Pixel Similarity):** Measures pixel-level similarity between real and generated frames. |
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- **Perceptual Loss:** Captures higher-level similarity using VGG19 features for realism in generated frames. |
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- **Temporal Consistency:** Ensures frames flow smoothly by minimizing the difference between adjacent frames. |
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## Future Improvements |
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Potential improvements for the Ayo Generator include: |
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- **Enhanced Temporal Consistency:** Using RNNs or temporal loss to improve coherence. |
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- **Higher Resolution Output:** Modifying the model to support 256x256 or higher. |
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- **Additional Character Variation:** Adding data variety to improve generalization. |
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## Ethical Considerations |
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The **Ayo Generator** is intended for creative and educational purposes. Users should avoid: |
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- **Unlawful or Offensive Content:** Misusing the model to create or distribute harmful animations. |
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- **Unauthorized Replication of Identities:** Ensure that generated characters respect IP and individual likeness rights. |
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## Model Card Author |
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This Model Card was created by [Minseok Kim]. For any questions, please contact me at [email protected] or https://github.com/minnnnnnnn-dev |
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## Acknowledgments |
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I would like to extend my gratitude to [Junyoung Choi] https://github.com/tomato-data for valuable insights and assistance throughout the development of the **Ayo Generator** model. Their feedback greatly contributed to the improvement of this project. |
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Additionally, special thanks to the [Team **Six Guys**] for providing helpful resources and support during the research process. |
