Automatic Generation of Two Level Hierarchical Tutorials from Instructional Makeup Videos

Title of the Paper

Automatic Generation of Two-Level Hierarchical Tutorials from Instructional Makeup Videos

Paper Information

• Domain: Utilizing computer vision and natural language processing techniques to automatically generate hierarchical operational guides from instructional videos.
• Keywords: Tutorial videos, video navigation, video segmentation, natural language processing, computer vision

Research Background and Problem

• Problem or Challenge: While instructional makeup videos effectively demonstrate operational details that are difficult to convey through text or images, their lengthy and linear structure makes efficient navigation challenging. Users often spend significant time skipping and rewinding.
• Significance: With the growing demand for online learning, people increasingly prefer learning new skills through instructional videos. Optimizing the video learning experience to make it more efficient has become a research hotspot.
• Motivation and Related Work:
  • Insights from Cognitive Psychology: Hierarchical task decomposition (coarse-grained events focusing on objects, fine-grained events focusing on actions) helps viewers quickly understand steps.
  • Related Work: Some studies have explored extracting steps from tutorial videos, but existing methods are often limited to fine-grained event segmentation, lack hierarchical structures, and are difficult to adapt to the makeup video domain.

Proposed Solution

• Proposed Method or Solution:
  • Designed a multimodal segmentation algorithm tailored for makeup videos, dividing videos into a two-level hierarchy: coarse-grained facial regions and fine-grained specific operational steps.
  • Developed a hybrid media user interface that allows users to browse tutorials in text, image, and video formats and navigate using clicks and voice commands.
• Innovations:
  • Applied the two-level task decomposition theory from cognitive psychology to the domain of makeup videos.
  • Combined computer vision (face detection, object detection) and text analysis (dependency parsing, phrase detection) to achieve automated tutorial segmentation.
  • Provided a user-friendly navigation interface, enabling users to learn at their own pace more easily.
• Implementation Steps and Key Techniques:
  1. Used Google's Speech-to-Text API to generate time-aligned transcripts.
  2. Phase 1 (Over-segmentation and Labeling): Over-segmented the video into shot frames and text phrases, performing scene labeling for both visual and textual elements (e.g., makeup products, facial regions).
  3. Phase 2 (Fine-Grained Step Construction): Identified the boundaries of each fine-grained step in the video based on product descriptions and application patterns.
  4. Phase 3 (Facial Region Clustering): Clustered detailed steps into facial regions based on facial area labels.
  5. Provided a hierarchical tutorial navigation interface, featuring titles, an overview timeline, and a step panel to assist users in jumping to and refining operations.

Research Outcomes

• Specific Outcomes:
  • Automatically generated 40 hierarchical makeup tutorials, with segmentation results for 10 videos closely matching manual annotations (average fine-grained F1 score of 0.80, coarse-grained F1 score of 0.81).
  • User studies showed that the new hybrid media tutorial interface significantly improved learning efficiency, making it easier for users to locate relevant steps and feel more confident in completing makeup tasks.
• Advantages:
  • Compared to standard video interfaces (e.g., YouTube), the new interface offers more efficient navigation, supporting refinement to specific steps or facial regions.
  • User feedback indicated that hierarchical tutorials helped them better understand the content and flexibly adjust the sequence of tutorial steps.
• Experimental Results Analysis:
  • Experiments on 10 input videos demonstrated good consistency in automated segmentation and hierarchical structuring, while significantly reducing users' cognitive load in skipping and rewinding compared to standard interfaces.
• Limitations and Future Directions:
  • Does not support multi-person videos (e.g., makeup artist and model), requiring more complex scene understanding.
  • Makeup tutorials cannot fully adapt to users' facial features; future work may explore personalized tutorial generation through AR technology.
  • While the multimodal approach showed promising results, it may not automatically identify coarse-grained hierarchical structures in other domains, such as DIY or cooking videos, which require further expansion.

Note: If a deeper analysis of experimental data or charts is needed, further discussion is possible.