Distortions in AI-generated videos can be technically referred to as artifacts or generative artifacts. These are unintended visual or auditory anomalies that occur due to limitations in the AI model, training data, or generation process. Common types of distortions include:
1. Visual Artifacts
- Blurring: Loss of detail or sharpness in certain areas.
- Ghosting: Duplication or trailing of objects or features.
- Pixelation: Blocky or distorted pixel patterns.
- Unnatural Textures: Surfaces or patterns that look unrealistic or inconsistent.
- Anatomical Inconsistencies: Errors in human or object proportions (e.g., extra fingers, distorted faces).
2. Temporal Artifacts
- Flickering: Inconsistent lighting or object appearance between frames.
- Jittering: Unstable or shaky motion.
- Temporal Incoherence: Lack of smooth transitions between frames, leading to unnatural movement.
3. Semantic Artifacts
- Illogical Content: Objects or scenes that defy real-world physics or logic.
- Contextual Errors: Mismatched or nonsensical elements in the scene.
4. Compression Artifacts
These can occur if the AI-generated video is compressed, leading to blocky or noisy visuals.
Causes of Distortions
These distortions arise due to factors such as:
- Model Limitations: Insufficient training data or model capacity.
- Training Data Biases: Gaps or biases in the dataset used to train the AI.
- Generation Process: Errors during the synthesis of frames or interpolation between frames.
- Post-Processing: Issues introduced during editing or compression.
In research and development, addressing these distortions often involves improving the model architecture, training techniques, or post-processing methods.