RoboVIP: Multi-View Video Generation with Visual Identity Prompting

Kari Jaaskelainen

10 Jan 2026 — 1 min read

Robots learn from videos of their own actions—but collecting lots of diverse, multi-camera footage is slow and costly.

RoboVIP is a generative approach that “films” new training scenes without touching the hardware.

Visual identity prompting: Instead of vague text, the model is guided by example images of the robot, objects, and background, so scenes match the desired setup.
Multi-view, time-coherent videos: It generates synchronized, consistent views across cameras and frames—what modern policies actually need.
Scalable identity pool: A pipeline curates exemplar images from large robotics datasets to cover many environments.

Training vision-language-action and visuomotor policies on these augmented videos yields consistent gains in simulation and on real robots.

Bottom line: higher-quality, scene-faithful synthetic videos → better manipulation skills with less real-world data collection.

Paper: https://arxiv.org/abs/2601.05241

Paper: https://arxiv.org/abs/2601.05241v1

Register: https://www.AiFeta.com

#Robotics #AI #ComputerVision #GenerativeAI #DiffusionModels #RobotLearning #DataAugmentation

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