u/MetaEmber

Disclosure: founder of Amoura.io, the swipe-based mutual match AI relationship simulator we shared last time with our video model.

Since you all responded so well and our mod was kind enough to feature us, we wanted to share something we just shipped!

Characters can now send you photos mid-conversation. Not just profile pictures, but actual in-the-moment selfies that fit whatever you're talking about. She's out somewhere and sends you a mirror selfie. You're having a late night conversation and she sends something that matches the vibe. That kind of thing.

But... We really want to make sure our quality is there. Right now we are experimenting with models and prompts, but I wanted to share our most recent examples to see if it is of quality. I think this community would be an amazing gage as we love your taste in quality.

The hardest part of building this: getting her to look like herself across every photo. Not just beautiful, but... consistent. Same face, same energy, different moment. We're attaching four photos of the same character so you can see how well her identity holds up across different shots.

Does she look like the same person in all four? What feels off? What feels natural? That's genuinely what we want to know before we roll this out more broadly.

Made with NanoBanana and Seedream 4.5

Disclosure: founder of mutual match dating sim called Amoura.io
Posted here a while back about Kling for character clips. Here's what our additional testing added.

The counterintuitive finding: less description/motion = more identity
Every time we added complex motion or description "head turns, walking, significant gestures," identity drift increased. The clips that held up best were almost still: a slight weight shift, a breath, a contained expression change. The less we asked the model to do, the more the person stayed consistent.

This was the opposite of what we expected.

The loop point is where faces go wrong
The last 3-4 frames before a loop resets are where drift concentrates. We stopped trying to smooth it and started cutting clips right before drift begins. A 4-second clip becomes 2.8 seconds sometimes. The audience doesn't notice the length. They notice the face change.

Motion type hierarchy (best to worst for identity):

1. Facial microexpressions
2. Subtle head settle (under 5 degrees)
3. Body language -- breathing, weight shift
4. Head turns -- drift starts past about 15 degrees
5. Anything involving shoulders/torso -- face usually different by the end

PROMPT FOR KLING 3.0:
She gently adjusts her hair then starts checking herself out in the mirror, followed by a subtle cheeky cute shy giggle and smile

The implied subject works for video too
Specifying who is filming just by saying "he" or "she" tends to take their personality from a single image and fill in the gaps, more accurately than sometimes, I can write.

What's the highest complexity motion anyone's gotten to feel genuinely natural?

Full disclosure: I'm the founder of Amoura.io, a swipe-based AI relationship simulator. Our characters can now send photos to users mid-conversation. Not profile photos, but contextual ones that can be sent on the fly. A character might send a mirror selfie getting ready, something from wherever they are, a photo that fits the specific moment in the conversation.

This is a harder problem than profile generation and I want to share what we've found so far.

Why contextual generation is different

Profile photos have one job: establish identity. You generate them in a controlled session with full attention on the face.

In-chat photos have two jobs simultaneously: match the established character identity AND reflect whatever context the conversation has set up — what she's wearing, where she is, what the vibe is. The more specific the conversational context, the more variables you're asking the generation to hold at once. And more variables means more drift.

What we've settled on so far

The structure that's holding best:

Identity anchor (always first, always verbatim from the character's reference prompt): "SAME EXACT PERSON as reference — [the 2-3 hyper-specific micro-features from her original profile prompt, copied exactly]"

Conversational context layer (what the chat has established): "[What she's wearing based on conversation context] — [where she is] — [what she's doing or what just happened]"

Shot style that matches the moment: "[Mirror selfie / front camera / someone else took this — whatever fits the scene] — iPhone-style, vertical, candid, natural blur"

Texture lock (always last): "Realistic skin texture, visible pores, natural proportions, no AI smoothing"

Technical path:

The 2nd photo is our source image we made for reference so you can see the base image. And then the 3rd image is another gen of the first image to show you the subtle variations the generation does on its own.

Prompt for first photo:

Ultra-realistic waist-up mirror selfie taken with a handheld iPhone of the same exact woman from the reference image. Strict identity preservation — match her facial structure, eye spacing, nose shape, lips, skin tone, hairline, and overall proportions exactly. No identity drift. No beautification, no idealization.

Shot through a real household mirror with subtle believable imperfections — faint smudges, soft surface marks, realistic reflected depth, natural interaction between the subject, phone, and reflected environment. The mirror should feel real and used, not spotless.

She wears a black bikini consistent with her established style. Everyday casual, nothing aspirational or styled for a shoot.

Change to a different, natural believable varied facial expression/pose.

Slightly imperfect amateur selfie framing. Subtle tilt, minor asymmetry, natural handheld composition, not perfectly centered. Feels pulled from a real camera roll.

Natural real-world lighting with slightly uneven exposure, soft directional shadows, realistic tonal falloff. No studio lighting, no ring light halo, nothing symmetrical or polished.

Authentic phone camera rendering — mild sensor noise, slight motion softness, realistic depth, natural dynamic range compression, subtle JPEG micro-artifacts. No over-smoothing.

Visible pores, fine skin texture, natural micro-imperfections. No beauty filter, no skin smoothing. Skin reads like a real photograph.

Realistic everyday indoor setting. Believable ambient detail, natural clutter, nothing staged or arranged.

Hyper-photorealistic, strictly photographic. No HDR grading, no cinematic color, no synthetic glow. True-to-life color temperature and natural exposure.

Aspect ratio 3:4, maximum resolution, natural proportions, no text, no logos, no watermarks.

What's breaking consistency for us

The bigger the gap between the profile photo context and the in-chat photo context, the more drift we see. A profile shot in neutral indoor lighting holds fine. The same character in a dim bar or outdoor evening light — face starts to shift.

Outfit changes are worse than location changes. Something about specifying clothing in detail seems to compete with the identity anchor in a way that location doesn't.

And the hardest case: when users ask for something specific mid-conversation. "Can you send me a photo from the gym" when the character's profile photos are all indoor casual. The context jump is too big and the face pays for it.

What we haven't solved

Maintaining micro-expression consistency specifically. The eye shape that's locked perfectly in profile photos drifts subtly when the character is described as mid-laugh or looking down. Small angle changes in expression seem to affect identity more than small angle changes in camera position.

Also curious whether anyone is using a different style reference image injection method rather than purely prompt-based anchoring for contextual generation? If so, how you're handling the reference when the lighting/context is significantly different from the source image.

What approaches have people found for maintaining identity when the generative context shifts significantly from the reference?

How does this quality hold up against others you've seen/tried/tested?