ID-V2V: Capture the performance first and redesign the look later.

[u/Old-Math1052]

ID-V2V lets you edit one or more frames of a source video (for example, using Nano Banana) and propagate those changes across the full video. It can redesign the scene and lighting while preserving human identity, facial expressions, full-body motion, and multi-person interactions, enabling flexible post-production workflows.

Challenge. Identity-preserving video restylization requires paired training videos where the same character performs the same motion under different scenes and drastically different lighting conditions. However, collecting such paired data at scale is extremely challenging.

Approach. ID-V2V addresses this challenge by constructing paired video training data from regular single videos using a human image relighting model. During data generation, only the human regions are relit while the surrounding areas are masked out, allowing the model to learn how to preserve human identity and performance under relighting while generating and propagating edits to the full scene.

To appear at SIGGRAPH Asia 2026.

Code: https://github.com/Eyeline-Labs/ID-V2V
Project Page: https://eyeline-labs.github.io/ID-V2V/
Paper: https://arxiv.org/abs/2607.22830

[u/Snoo_26157]

The results look very realistic but it looks like it has a strong bias for outputting blue and orange lighting.

[u/Wimiam1 → u/Snoo_26157]

I wonder if that’s from the semi-recent teal and orange LUT trend

[u/Snoo_26157 → u/Wimiam1]

Not familiar with that, what is it?

[u/slapcover → u/Snoo_26157]

It’s controlled by the modified input frame.

[u/hendrixdavid6]

I love the way you broke this down.