Vidformer is a drop-in rendering accelerator for video data visualization enabling instantaneous video playback.
2-3×
faster full rendering
~400×
faster time-to-playback
<1s
to first frame, any video length
Python and OpenCV are the de facto tools for video data visualization.
Just change import cv2 to import vidformer.cv2 as cv2 for practically instantaneous playback, no matter how long your video is.
Try it out below! You can run code, see an annotated video, and jump to any part of it instantly.
Click Run to render a video
First run loads Python (~10s)
For more complex examples, try the playground or
Vidformer uses a declarative data model to combine three techniques:
An API shim uses symbolic computation to losslessly lift imperative code into a declarative specification. The code actually runs, but with mock frames.
A rendering engine optimizes and parallelizes rendering of the declarative specification. It can handle worst-case access patterns, not just typical ones.
Videos are served through a VOD protocol where segments are rendered on-demand. The video stream grows as the script runs.
Combining LLMs with Vidformer enables truly interactive conversational video querying. Ask natural language questions about your video data and receive newly-rendered video results in seconds.
@misc{winecki2026_vidformer,
title={Vidformer: Drop-in Declarative Optimization for Rendering Video-Native Query Results},
author={Dominik Winecki and Arnab Nandi},
year={2026},
eprint={2601.17221},
archivePrefix={arXiv},
primaryClass={cs.DB},
url={https://arxiv.org/abs/2601.17221},
}