README
ID-V2V: Identity-Preserving Video-to-Video Generation
Paper: ID-V2V: Identity-Preserving Video Restylization (SIGGRAPH Asia 2026)
Yuancheng Xu, Mingming He, Pablo Salamanca, Li Ma, Yash Kant, Emmett Steven, Paul Debevec, Ning Yu
Project page: https://eyeline-labs.github.io/ID-V2V/
Code: https://github.com/Eyeline-Labs/ID-V2V
ID-V2V restyles a video while preserving the original characters' identity and performance. Given a source video and a stylized keyframe (plus optional extra keyframes and a text prompt), it generates a new video whose scene, lighting, and style follow the keyframe(s), while the source subjects' identity, expressions, gaze, and motion are kept intact — a shoot first, restyle later workflow. It is a research artifact, released for demonstration and inspiration only.
This repository hosts the two finetuned ID-V2V checkpoints. Both share the same architecture (Wan 2.1 image-to-video with VACE control). See the code repository for how to use this model.
Checkpoints
| File | Variant | Control conditions |
|---|---|---|
idv2v.pth | Default (recommended) | 1 — foreground-on-gray pixels (subject segmented by SAM3) |
idv2v_with_normal_depth.pth | Alternate | 3 — foreground-on-gray pixels + surface normals + depth |
idv2v.pth— the default single-condition model. It preserves the segmented subject (relit to match the new scene) and regenerates the rest of the frame from the prompt.idv2v_with_normal_depth.pth— additionally conditions on the source video's surface normals (DAViD) and depth (DepthAnything-V2), giving tighter geometric control over the regenerated regions.
The two are different, non-interchangeable weights with an identical architecture, so loading the wrong one does not raise an error — it silently degrades the output. Pair each checkpoint with its matching script, as documented in the code repository.
Contributors
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Used in code
README
ID-V2V: Identity-Preserving Video-to-Video Generation
Paper: ID-V2V: Identity-Preserving Video Restylization (SIGGRAPH Asia 2026)
Yuancheng Xu, Mingming He, Pablo Salamanca, Li Ma, Yash Kant, Emmett Steven, Paul Debevec, Ning Yu
Project page: https://eyeline-labs.github.io/ID-V2V/
Code: https://github.com/Eyeline-Labs/ID-V2V
ID-V2V restyles a video while preserving the original characters' identity and performance. Given a source video and a stylized keyframe (plus optional extra keyframes and a text prompt), it generates a new video whose scene, lighting, and style follow the keyframe(s), while the source subjects' identity, expressions, gaze, and motion are kept intact — a shoot first, restyle later workflow. It is a research artifact, released for demonstration and inspiration only.
This repository hosts the two finetuned ID-V2V checkpoints. Both share the same architecture (Wan 2.1 image-to-video with VACE control). See the code repository for how to use this model.
Checkpoints
| File | Variant | Control conditions |
|---|---|---|
idv2v.pth | Default (recommended) | 1 — foreground-on-gray pixels (subject segmented by SAM3) |
idv2v_with_normal_depth.pth | Alternate | 3 — foreground-on-gray pixels + surface normals + depth |
idv2v.pth— the default single-condition model. It preserves the segmented subject (relit to match the new scene) and regenerates the rest of the frame from the prompt.idv2v_with_normal_depth.pth— additionally conditions on the source video's surface normals (DAViD) and depth (DepthAnything-V2), giving tighter geometric control over the regenerated regions.
The two are different, non-interchangeable weights with an identical architecture, so loading the wrong one does not raise an error — it silently degrades the output. Pair each checkpoint with its matching script, as documented in the code repository.