CUSTOM AVATAR
Understand how AIMoCap skeleton binding and retarget tests help prepare a custom avatar for production mocap.
For users searching for a character binding workflow before mocap retargeting.
AIMoCap character binding maps the uploaded avatar skeleton before retarget testing decides whether the character is safe to publish as a reusable mocap target.
Use AIMoCap when a character's skeleton mapping needs to be reviewed before video mocap jobs depend on that avatar.
Do not skip binding review when the avatar has unusual hierarchy, scale, limb naming, or pose assumptions.
Character binding is the middle of the custom avatar workflow. Upload gets the asset into AIMoCap, but binding is where the skeleton becomes meaningful for retargeting.
This page should answer binding-specific questions instead of repeating the full custom-avatar page.
A binding workflow is valuable when it makes avatar setup issues visible before real mocap jobs depend on that character.
The practical value is diagnostic: binding review helps separate asset problems from source-video problems before a team spends time debugging real mocap jobs.
A good binding workflow also creates a rollback point. If a new binding or pose edit makes results worse, the team should know which older published target was still acceptable.
Use this matrix to decide whether a poor custom-avatar result comes from binding, pose, source video, or downstream cleanup.
Avatar-retargeting searches usually come from people who already hit a rig, rest-pose, scale, or cleanup problem. The page should explain how to diagnose target readiness instead of promising one-click character motion.
Users searching for character binding workflow often expect a character file to work immediately, but character binding and testing should move through upload, A-pose review, binding, retarget test, publish, and only then repeated mocap use.
When a character binding workflow result looks wrong, the next question should be whether the source clip, rest pose, skeleton mapping, scale, or retarget test caused the problem instead of rerunning blindly.
For character binding and testing, the workflow becomes valuable when the same character is used across many clips; publishing a tested target prevents setup work from being repeated for every job.
Use these facts to decide whether this workflow matches your output, integration, and cleanup needs.
In a character binding workflow, an avatar skeleton can upload successfully but still map poorly once motion stresses shoulders, hips, knees, ankles, or root orientation.
Separating binding from video processing helps identify whether failure comes from the avatar or the source clip.
Publishing should happen after binding and retarget testing, not immediately after upload.
Binding quality is best evaluated with motion that exercises shoulders, hips, knees, ankles, root orientation, and contact behavior rather than a static pose alone.
Binding records let teams return to the last known good target if a new mapping or pose edit makes later mocap jobs worse.
A useful binding review distinguishes motion-critical joints from decorative or helper nodes that should not drive retarget decisions.
Publishing is safer when the binding has passed more than one easy clip, especially if the target will receive turns, steps, hand motion, or stylized poses.
A character binding workflow packet should contain required bones, optional helper bones, root axis, scale assumption, A-pose version, acceptance clip, publish decision, and rollback target.
The page should tell teams what makes a binding unsafe to publish, not only what a successful bind looks like.
Review whether the uploaded avatar skeleton can be mapped cleanly before running motion tests.
Complete binding work before the retarget test so motion behavior can be evaluated against the mapped skeleton.
Publish only after the retarget test shows the binding works well enough for future jobs.
Track whether failures come from hierarchy, scale, left/right limb mapping, rest pose, root orientation, or source-video quality.
A pose that looks acceptable in a still preview can still fail when arms swing, feet plant, hips rotate, or the root moves under a retarget test.
Save avatar version, binding notes, test clip, approval result, and known caveats so the team can compare new bindings against the last stable target.
It is the step where the avatar skeleton is mapped so motion can be retargeted and tested.
That is risky. The retarget test is the practical check that binding behaves under motion.
No. Binding addresses the avatar target; source-video readability is a separate input.
Inspect hierarchy, limb mapping, rest pose, scale, root orientation, and whether the retarget test exposes offset or foot-contact issues.
Save the avatar version, rest pose notes, mapped skeleton assumptions, test motion result, known caveats, and whether the avatar is safe to publish.
Redo binding when repeated clips show the same limb, root, scale, shoulder, or foot-contact issue, or when the source FBX or rest pose changes.
No. Binding should focus on motion-critical joints first; helper, cosmetic, or mesh-related nodes should not distract from root, hips, spine, shoulders, arms, legs, and feet.
At minimum, use a clip that stresses arms, hips, feet, and root motion. For production reuse, keep representative acceptance clips that match the motions the avatar will receive.
Do not publish if required bones are unclear, left/right mapping is uncertain, root orientation drifts, scale is inconsistent, or the target fails representative movement tests.
Include required bones, optional helper bones, root axis, scale assumption, A-pose version, test clip, publish or reject decision, known caveats, and rollback target.
Continue through this topic cluster to compare output formats, API options, and workflow boundaries.
Upload, bind, test, publish, and reuse avatars.
Prepare clips that reduce retargeting cleanup.
Compare default FBX, custom targets, and robot outputs.
Use AIMoCap character management to bind, test, and publish FBX characters for repeat mocap jobs.
Publish tested avatars in AIMoCap so future Studio jobs can target the same character without repeating setup.
Use A-pose editing as part of AIMoCap's custom avatar preparation flow before binding and test runs.
These related AIMoCap resources document the workflow boundaries, output formats, and implementation details referenced on this page.
