How to Use V-JEPA 2.1 with Video and Sensor Data
Last Updated on September 25, 2026 by Editorial Team
Author(s): Kishor Datta Gupta
Originally published on Towards AI.
How to Use V-JEPA 2.1 with Video and Sensor Data
I started with a simple experiment: take a daylight road scene, add rain and darkness, and see how V-JEPA 2.1 responds. The visual difference is easy to spot. What I wanted to measure was the change in the model’s representation of that scene.
The article explains how to compare video clips and multi-sensor views using V-JEPA 2.1, starting from encoding short clips into embeddings and computing cosine distances between them. It then walks through using the DSERT-RoLL V-JEPA Studio to apply weather/lighting prompts, render and encode separate sensor streams (including RGB, event, thermal, LiDAR, and radar), and aggregate embeddings to quantify how representations shift under controlled augmentations. The author provides practical setup details (hardware/software, code revisions, and scripts), describes how to reproduce a specific “Heavy rain at night” example, and shows where to find generated outputs and metrics. Finally, it emphasizes experimental discipline (keep sampling/cropping/checkpoints fixed, vary one factor at a time), notes limitations of the sensor visualizations and lack of physical calibration, and concludes by encouraging traceable comparisons tied to a clip, prompt, and saved results.
Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.
Related posts
Recent Posts
