Training Data Preprocessing for Text-to-Video Models

Training Data Preprocessing for Text-to-Video Models

Text-to-video models like Runway and Sora rely on high-quality video-text datasets, where preprocessing reduces noise and improves generation accuracy by up to 40%. The process involves splitting raw videos into coherent clips, labeling them with precise captions, and filtering out low-quality content.

Why This Matters

The quality of training data directly determines the output of text-to-video models, as the “garbage in, garbage out” principle applies rigorously. Poorly preprocessed datasets can lead to models that fail to generalize, producing low-quality or irrelevant outputs. For example, unfiltered datasets may contain broken clips or misaligned captions, which can degrade model performance by up to 40% in real-world applications like film production and advertising. The cost of such failures is significant, with production errors potentially wasting hundreds of thousands of dollars in creative workflows.

Key Insights

• “Scene splitting with PySceneDetector reduces clip length to 15-30 seconds for model training.” (from context)
• “Visual filtering using OpenCV and optical flow analysis removes 30% of low-quality clips.” (from context)
• “CogVLM2-Video used by companies for automated video labeling.” (from context)

Working Example

from scenedetect import detect, ContentDetector, split_video_ffmpeg
path_to_video = "path/to/your/video"
scene_list = detect(path_to_video, ContentDetector(threshold=27, min_scene_len=15), start_in_scene=True)
split_video_ffmpeg(path_to_video, scene_list, "output_dir")

from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("THUDM/cogvIm2-llama3-caption")
videoframes = load_frames(path)  # load every nth frame of the video
caption = model.generate(prompt="Please describe this video in detail.", images=videoframes)

import cv2
import numpy as np
from skimage.exposure import is_low_contrast
def frame_is_blured(image: np.ndarray, threshold: float) -> bool:
    variance = cv2.Laplacian(image, cv2.CV_64F).var()
    return variance < threshold

Practical Applications

• Use Case: “Runway Gen-2 uses scene splitting and filtering to generate ad campaigns.”
• Pitfall: “Over-reliance on automated captioning without manual review leads to misaligned text-video pairs.”

References:

• https://www.infoq.com/articles/training-data-preprocessing-for-text-to-video-models/