Pets, family, viral
Trends with names. Cuts that land. Lign sits in front of CapCut and does the alignment grind for you.
Eight weeks to five months. Same eyes, different ears.
Puppy-to-adult morphs that hold the face steady while the rest of your dog gets bigger.
Make the cutCapture loop landing soon
Real-device capture of this use case ships once test users finish their first runs.
The pain
Find a window you like, a spot in their room, or a location that means something to your family, and return to it. Consistency matters more than perfection.
How Lign solves it
The features that line up for this exact use case.
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Object Anchor: tap the sunglasses, watch Lign do the rest
The Object Anchor mode uses Apple Vision instance segmentation plus a feature print to find the same object across every photo. Tap the sunglasses on one frame; Lign rotates, scales, and translates the other frames until the lenses land on the same pixel.
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Animal pose detection for dogs and cats
For the puppy-to-adult morph: Animal Eyes is the primary landmark, with Animal Nose Tip and the pose joints as fallbacks. Apple Vision detects all of them on dogs and cats from iOS 18 onward. Works on rescue arcs, kitten-to-cat, anything four-legged.
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Beat-friendly timing baked into the export
Set per-photo duration in tenths of a second so the cut lands on the drop. Loop count to repeat the morph two or three times for Reels. Hold-last-photo to let the final shot sit while the audio resolves. Export 9:16 for vertical, 1:1 for the grid, or Original to keep wedding-photographer framing.
The pet glow-up trend on TikTok hits a hundred million-plus views a quarter under hashtags like #dogglowup, #puppytofullgrown, and #beforenafteradoption. The caption template is “8 weeks to 5 months” or “11 weeks to 46 weeks.” The visual template is a fast cut between photos taken months apart.
The reason most pet glow-ups feel half-finished is that the photos were taken when the dog felt like sitting, not at the same angle. The puppy’s face is in the upper-left of one shot, dead-center in the next, and tilted in the third. The morph cut feels like a slideshow. Family photographer Sara Pietras has been giving the same advice for years: “find a window you like, a spot in their room, or a location that means something to your family, and return to it. Consistency matters more than perfection.” Almost nobody can hold it for twelve months. Lign covers that gap in software.
Lign anchors the animal’s eyes (or any landmark you pick) to the same pixel across every photo. The body shifts underneath. The face stays put. Export square for the feed, 9:16 for Reels, and post the duration in the caption. That is the format that works.
Works for cats too.
Landmarks used
- Animal eyes (primary anchor)
- Animal nose tip
- Animal pose joints (for body shots)
See it line up
Two ways to see what alignment buys you.
Drag the slider for the still comparison; scrub the flipbook for the motion. The frames here are stand-ins drawn from the in-app pet morph; the use-case capture lands with Phase 8.5.
About that one thing
Can I do this without CapCut?
Yes. The whole point of Lign is that the photos arrive at CapCut already lined up, so the morph template actually works on the first try. If you want to skip CapCut entirely, export a 9:16 or 1:1 video from Lign directly; the loop count and per-photo duration are part of the export settings. Drop it into Reels, Stories, or the TikTok composer with audio on top.
Questions, answered.
- Does Lign work on dogs and cats?
- Both. Apple Vision detects animal pose (eyes, ears, nose, neck) on dogs and cats from iOS 18 onward. Lign uses those landmarks for alignment.
- My dog never sits the same way twice. Will the morph be janky?
- That is exactly the failure mode Lign solves. The morph reads as smooth when the face stays anchored to the same pixel; the body and pose can change underneath. Generic morph templates (CapCut, IG Reels) do not do landmark-anchored alignment, so your puppy's face drifts between every frame.
- What if the animal's face isn't detected in some photos?
- Use Manual mode. Tap two points (eyes) on each photo and Lign uses those as the anchor instead of the auto-detection.
Coming soon