ID-V2V Identity-Preserving Video Restylization

A source video's first frame (and optionally further anchor frames) is restyled into a new look by an image-editing model, then a dedicated video-to-video model, ID-V2V, regenerates the full clip in that new look while explicitly holding facial likeness, expression, eye gaze and full-body performance constant, casting identity preservation as a video-relighting problem rather than leaving it to the same model that authored the style change.

Properties

family: character-consistency
modality: source video + edited keyframe -> identity-preserved restyled video
whyMultiModel: The keyframe restyle and the identity-preserving propagation are trained as two separate systems with different objectives: the image-editing model (NanoBanana / NanoBanana Pro) authors the new look at inference time but, per the project's own documentation, often cannot hold the exact pose or expression when restyling a frame, so the stylized first frame ends up misaligned with the source video's first frame; ID-V2V is a distinct video-to-video generative framework trained specifically to invert a relighting transform, using relit facial regions and facial normal maps to anchor identity while the edited keyframe and depth sequences drive the visual edit. Neither model can do the other's job: the image editor has no temporal or video-native identity-preservation training, and ID-V2V does not author style decisions itself, it only propagates an already-edited keyframe. This is distinct from the tracked 'Restyled First-Frame Propagation' pattern (Flux Kontext -> Wan 2.2 Fun), which uses generic depth/canny/pose maps purely as a geometry-drift guardrail and has no facial-relighting-based identity-preservation training objective.
steps: [object Object], [object Object]
controls: Number and placement of edited/anchor keyframes; whether NanoBanana or NanoBanana Pro (with relighting) is used for the keyframe edit; relit-facial-region and facial-normal-map conditioning strength; depth-sequence conditioning strength for the propagated edit.
exampleStack: NanoBanana Pro (relit, restyled first frame) -> ID-V2V (video-to-video, identity- and performance-preserving propagation) -> restyled output video.
useCases: [Shoot-first, restyle-later VFX workflows where the performance is locked but the look is decided later][Converting live-action footage into a new visual genre while keeping the actor's exact performance intact][Multi-person interaction scenes that need a new look without identity swap or drift between subjects]
pitfalls: Image-editing models like NanoBanana often can't hold the exact pose or expression when restyling a frame, so downstream quality depends on ID-V2V's relighting-based correction rather than trusting the keyframe outright; because training pairs are constructed via self-supervised relighting of a single video, restyles that go far outside that relighting distribution (extreme genre or geometry changes) can still degrade identity preservation.
source: ID-V2V: Identity-Preserving Video Restylization (SIGGRAPH Asia 2026, arXiv:2607.22830)

Provenance

Source: https://eyeline-labs.github.io/ID-V2V/

ID-V2V Identity-Preserving Video Restylization

Properties

family

character-consistency

modality

source video + edited keyframe -> identity-preserved restyled video

whyMultiModel

The keyframe restyle and the identity-preserving propagation are trained as two separate systems with different objectives: the image-editing model (NanoBanana / NanoBanana Pro) authors the new look at inference time but, per the project's own documentation, often cannot hold the exact pose or expression when restyling a frame, so the stylized first frame ends up misaligned with the source video's first frame; ID-V2V is a distinct video-to-video generative framework trained specifically to invert a relighting transform, using relit facial regions and facial normal maps to anchor identity while the edited keyframe and depth sequences drive the visual edit. Neither model can do the other's job: the image editor has no temporal or video-native identity-preservation training, and ID-V2V does not author style decisions itself, it only propagates an already-edited keyframe. This is distinct from the tracked 'Restyled First-Frame Propagation' pattern (Flux Kontext -> Wan 2.2 Fun), which uses generic depth/canny/pose maps purely as a geometry-drift guardrail and has no facial-relighting-based identity-preservation training objective.

steps

[object Object], [object Object]

controls

Number and placement of edited/anchor keyframes; whether NanoBanana or NanoBanana Pro (with relighting) is used for the keyframe edit; relit-facial-region and facial-normal-map conditioning strength; depth-sequence conditioning strength for the propagated edit.

exampleStack

NanoBanana Pro (relit, restyled first frame) -> ID-V2V (video-to-video, identity- and performance-preserving propagation) -> restyled output video.

useCases

[Shoot-first, restyle-later VFX workflows where the performance is locked but the look is decided later][Converting live-action footage into a new visual genre while keeping the actor's exact performance intact][Multi-person interaction scenes that need a new look without identity swap or drift between subjects]

pitfalls

Image-editing models like NanoBanana often can't hold the exact pose or expression when restyling a frame, so downstream quality depends on ID-V2V's relighting-based correction rather than trusting the keyframe outright; because training pairs are constructed via self-supervised relighting of a single video, restyles that go far outside that relighting distribution (extreme genre or geometry changes) can still degrade identity preservation.

source

ID-V2V: Identity-Preserving Video Restylization (SIGGRAPH Asia 2026, arXiv:2607.22830)