Consistencia temporal y parpadeo en el videocuestionario de IA

Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

1. ¿Por qué ejecutar un modelo de imagen por separado en cada fotograma de un vídeo tiende a producir parpadeo?
   • The video's frame rate is too low for the model
   • Each frame starts from its own random noise and makes independent detail choices
   • Image models cannot process color information
   • The model removes motion blur from every frame
2. ¿Cuál es la razón principal por la que los generadores de vídeo modernos son más estables que las tuberías de imágenes fotograma por fotograma?
   • They generate frames jointly and use attention across time
   • They only generate black-and-white footage
   • They render at lower resolution
   • They always apply a deflicker filter afterward
3. ¿Qué tipo de contenido suele ser el más difícil de mantener estable de un fotograma a otro?
   • Large flat walls
   • A plain sky with no clouds
   • Fine high-frequency detail such as hair, foliage and text
   • A static solid-color background
4. ¿Qué puede arreglar de forma realista un filtro antiparpadeo en la postproducción?
   • A character's face gradually changing shape
   • An object that disappears behind a tree and returns different
   • Brightness pulsing between frames
   • A shirt that changes from three buttons to four
5. ¿Por qué puede desviarse la identidad en vídeos largos realizados mediante la extensión de clips más cortos?
   • The model sees only a limited window of recent frames
   • Longer videos always use a lower frame rate
   • Extensions switch the video to a different color space
   • Audio tracks interfere with the image model
6. What is a known weakness of factorized attention (spatial attention plus temporal attention at the same location)?
   • It cannot produce color
   • It is far more expensive than full 3D attention
   • It struggles with large motion because a given position holds different content across frames
   • It prevents the model from using text prompts
7. In training-free editing pipelines, what does flow-guided latent warping do?
   • Deletes frames that differ too much from the first
   • Moves the previous frame's latent along optical flow before denoising the next frame
   • Increases the random noise in each frame
   • Converts the video to a lower resolution
8. Which statement about flicker is a misconception according to the guide?
   • Occlusion can cause objects to come back looking different
   • Raising resolution will fix flicker on its own
   • Reference-image conditioning can help keep identity stable
   • Autoencoder compression can add artifacts where chunks join