Video Loop Segment Detection
Problem Definition
Given a video (or animation), the goal is to automatically detect loopable segments, defined by start and end frame indices, such that the segment can be played repeatedly with minimal visible discontinuity at the loop point.
Inputs
- Video file or sequence of frames
- (Optional) Preprocessing: downscaling, grayscale conversion, etc.
Outputs
- List of candidate loop segments: each with start index, end index, and a score indicating loop quality
Approach
1. Similarity-Based Loop Detection
- For each possible segment (start, end):
- Compute similarity between the start and end frames (e.g., MSE, cosine similarity, SSIM)
- Optionally, use motion energy or perceptual features to adjust similarity
- Apply a length penalty to discourage very short or very long loops
- Rank candidates by a combined score
2. Human Feedback
- Present detected loop segments to a human annotator
- Allow manual review, correction, and annotation of loop quality
- Use feedback to refine detection or train supervised models
Evaluation
- Quantitative: similarity score, motion energy, length penalty
- Qualitative: human annotation (good/bad, corrected indices)
Related Work
- "Seamless Video Looping" (Liao et al., SIGGRAPH 2013): Patch-based optimization for seamless loops
- Deep features (CNN, ViT, CLIP) can be used for perceptual similarity, but no SOTA end-to-end loop detector exists as of 2025
Future Directions
- Use deep video features for similarity
- Train supervised models with human-annotated loop segments
- Explore generative models for loop synthesis
References
- Liao, S., et al. "Seamless Video Looping." SIGGRAPH 2013.
- Other recent works on video similarity, perceptual metrics, and generative video models