TransVLM: Detecting Any Shot Transition with Vision-Language Models

TransVLM is a vision-language approach to Shot Transition Detection that identifies complete temporal segments for cuts, dissolves, fades, wipes, and effects rather than treating transitions as isolated frame-level boundary points. Developed by HeyGen Research and the University of Melbourne and accepted at ECCV 2026, it combines color frames with optical-flow information at the vision encoder level, enabling stronger detection of fine-grained temporal changes without increasing language-model token counts. The system also uses synthetic training data generated from 59 FFmpeg transition effects, re-annotated public data, and sliding-window inference with temporal merging to process arbitrarily long videos. Its accompanying benchmark contains 5,215 videos, more than 100 hours of footage, and 45,239 segment-labeled transitions. TransVLM reported segment-level F1 scores of 78.3% on public data and 89.5% on synthetic data, with a 0.11-second boundary error and practical real-time performance, outperforming conventional shot-boundary detectors and general-purpose vision-language models, particularly on gradual and complex transitions.

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