Avatar Generation

Avatar generation builds a drivable face or body from photos so one identity can smile, talk, and turn while still reading as the same person.

Identity kept within 0.25 distance while smile opens 18 mm shows a driven avatar that stays the same person

Why Does This Exist?

Games, calls, and training need faces that talk without filming every line. Scanning rigs cost too much for everyday users. Naive face swaps break identity or freeze expression.

Avatar generation exists for drivable likeness. Input is one or few portraits. Output is a 3D face or body that new expressions and views can drive. Pose priors come from 3D pose; the display home is AR and VR. Image priors borrow from image classification backbones.

Use it when one identity must perform many motions. Use video capture when the exact performance matters more than reuse.

Think of It Like This

A puppet carved from one photo

A carver studies one school photo, carves a puppet that looks like the kid, then fits strings for smile, blink, and turn. Good strings move the mood without recarving the nose.

Identity is the carving. Blendshapes are the strings. The analogy stops at skin: wood forgives stretch while pixels reveal teeth and ear errors the moment the head turns far.

How It Actually Works

Split identity from motion: encode lasting shape and texture once, then drive with expression codes or blendshape weights plus head pose. Contract is portraits in, drivable mesh plus texture out. Render new views and check identity distance stays low while expression matches the driver. A smile that opens lips $18$ mm with identity drift $0.22$ reads as same person happy. Drift past $0.60$ reads as a sibling.

Worked example

Source portrait drives to smile, frown, and profile. Smile opens $18$ mm, frown compresses $9$ mm, profile turns $35$ degrees. Identity distance to source stays $0.22$ , $0.25$ , and $0.31$ , all under a $0.60$ same person cut. Expression error to driver stays under $2$ mm on lip corners. Teeth at profile smear by $4$ mm, the visible limit that caps turn angle before retraining.

Watch Out For

Identity leak from the driver

Strong driver features bleed into the avatar: jaw narrows toward the actor. The symptom is avatars converging across users. Fix it with explicit identity versus motion splits and by testing same driver across many identities.

Far views that expose missing data

One frontal photo hides ears and teeth backs. Turns past $35$ degrees smear them. The symptom is clean fronts plus melted profiles. Fix it with multi view inputs or by limiting yaw and saying so in the UI.

The Quick Version

Avatars split lasting identity from drivable expression and pose.
Contract is portraits in, drivable mesh plus texture out.
The fixture holds identity under $0.31$ while smile opens $18$ mm.
Blendshape weights drive talk without recarving identity.
Driver leak and single view turns are the first failures to check.