What Is Identity Locking in AI? Definition + Examples
Identity locking is the technique of keeping the same character or face consistent across multiple AI-generated shots. Plus how it works, examples, and where to use it in AI workflows.
Identity locking is the technique of keeping the same character or face visually consistent across multiple AI-generated shots without re-shooting or manually correcting each frame.
It solves a specific and common problem: AI video models generate each clip from scratch. Run the same prompt twice and you'll get two slightly different faces, two different hairlines, two different jawlines. For any campaign with a recurring persona, a spokesperson, or a branded character, that drift makes the output unusable. Identity locking pins a reference at the model level so the character stays the same person from shot to shot.
How identity locking works
The core mechanism is a reference image fed into the model before generation begins. The model encodes that image into a latent representation, then treats it as a hard constraint during the denoising process. Instead of sampling a face freely from its training distribution, the model must satisfy the visual identity encoded in the reference.
More advanced implementations use persistent embeddings. Rather than re-injecting the reference image into every generation call, the model stores an embedding of the identity that can be reused across sessions. This is closer to how LoRA-trained character models work, though inference-time identity locking doesn't require training.
Multi-reference conditioning extends the technique further. You provide more than one reference image (a front-facing shot, a profile, a specific outfit) so the model can triangulate the identity under different angles and lighting conditions. This reduces the chance of the identity drifting when the camera angle or scene light changes significantly between shots.
When you use identity locking
You need it any time you're generating more than one clip with the same character and the clips will be cut together or viewed in sequence.
The most direct use case is talking-head or spokesperson content. If you're producing a series of product explanation clips, a brand character needs to look the same in clip one as in clip five. Identity locking makes that possible without a video production pipeline.
It also applies to narrative or campaign work with a fictional character. If you're building a short series or a social campaign around a recurring persona, you need shot-to-shot consistency. Identity locking gives the model enough to anchor to.
The technique matters less for abstract or non-character content, and for one-off clips where there's no second shot to be consistent with.
Examples
Higgsfield Soul 2.0 (talking heads and spokesperson content): Higgsfield Soul 2.0 is the current strongest model for identity locking on human subjects. You feed it a face reference and it holds that person's identity through motion, expression changes, and head turns. The output stays recognizably the same person from a static portrait to a full talking-head clip. See Higgsfield Soul 2.0 prompts for talking heads for tested prompt structures and reference image formats.
Seedance multi-reference (character plus environment): Seedance supports multi-reference conditioning, which you can use to lock a character's identity alongside a second reference for the environment or costume. This is useful when the character needs to stay consistent across scenes with different backdrops. Feed a face reference and a wardrobe reference, and Seedance anchors both while still following the motion described in the text prompt.
Related concepts
Multi-reference conditioning is the broader technique that identity locking often builds on. Where identity locking focuses specifically on character or face consistency, multi-reference conditioning can also pin products, environments, and brand assets. The two concepts overlap heavily in practice.
LoRA training is a different path to the same goal. Rather than conditioning at inference time with a reference image, LoRA fine-tunes the model on a set of images of the target character. The result is a custom model weight that generates the character reliably. It's more consistent than inference-time locking but requires training time and data.
For a full breakdown of which AI video models handle character consistency best, see best AI video generator 2026.
Want to run identity locking across Higgsfield Soul 2.0, Seedance, and 14 other leading models from one place? See the best AI video generators on 8frame.