CUSTOM AVATAR
Upload, bind, test, and publish FBX avatars so AIMoCap video mocap jobs can reuse them as targets.
For teams searching for mocap on their own FBX avatar.
FBX avatar mocap is a two-stage workflow: prepare the FBX character as a reliable AIMoCap target, then run video mocap jobs against that published target.
Use AIMoCap when you already have an FBX character and need a browser workflow for upload, A-pose review, skeleton binding, retarget testing, publish, and repeated mocap jobs.
Do not treat an arbitrary FBX file as automatically mocap-ready. Rig structure, rest pose, skeleton mapping, and test results still need review before production reuse.
Users searching for FBX avatar mocap usually need more than a video-to-FBX export. They want source motion to appear on a specific character that their team already owns.
AIMoCap handles that as a reusable target workflow: upload the FBX avatar, check the pose, bind the skeleton, run a retarget test, and publish only after the result is usable.
This separation matters because the avatar asset and the source video fail in different ways. A clean video cannot fix a poorly prepared rig, and a good rig still needs readable motion input.
Use this matrix to decide whether a character is ready to become a reusable AIMoCap target or should stay in setup.
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 FBX avatar mocap often expect a character file to work immediately, but FBX avatar retargeting should move through upload, A-pose review, binding, retarget test, publish, and only then repeated mocap use.
When a FBX avatar mocap 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 FBX avatar retargeting, 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.
FBX avatars can differ in bone names, hierarchy, rest pose, scale, and mesh organization, so a preparation step is safer than blind retargeting.
The setup cost pays off when the same published character is used across many mocap jobs instead of being fixed from scratch each time.
AIMoCap separates character setup from video processing, which makes it easier to diagnose whether a problem comes from the avatar or from the source clip.
A known passing clip gives teams a stable reference when a new FBX export, pose edit, or binding change changes the character output.
Treat a published FBX avatar like a target version; record the file, pose notes, binding result, and accepted test before using it across jobs.
A reusable avatar target is safer when the downstream DCC or engine has already exposed scale, root, loop, and contact issues before wide use.
Create the character profile, upload the FBX asset, and confirm that the uploaded model is the character you intend to reuse.
Use A-pose preparation and binding review to align the avatar with AIMoCap's retargeting expectations before any mocap job depends on it.
Test the character with sample motion so arm, leg, spine, and root behavior can be inspected before the avatar is published.
After approval, publish the avatar so future Studio mocap jobs can select the same target without repeating setup.
Save one or two representative clips that passed on the avatar, so later FBX revisions can be compared against the same arm swing, hip turn, foot plant, and hand reach checks.
Store the source FBX hash or version, A-pose edits, binding notes, retarget-test result, publish decision, and known cleanup caveats before using the avatar broadly.
When the artist exports a new FBX, compare it against the same acceptance clips instead of silently replacing a working published avatar.
Not automatically. The avatar should be uploaded, pose-checked, skeleton-bound, retarget-tested, and published before it becomes a reliable reusable target.
No. Video to FBX focuses on downloadable animation output. FBX avatar mocap focuses on driving a specific prepared character target.
The test helps catch skeleton mapping, pose offset, and motion quality issues before the avatar is reused in future mocap jobs.
Yes. Publishing is the handoff that makes the tested avatar selectable for repeated Studio mocap jobs.
No. It creates a target-aware mocap result, but production teams should still inspect and clean up animation in their downstream tools.
Compare the Default output, the published-avatar output, the original acceptance clip, and the current source video before deciding whether the FBX rig or the clip is the problem.
No. A new FBX version should pass the same pose, binding, and retarget-test checks before replacing the current published target.
Include source FBX version, A-pose edits, binding notes, retarget-test clip, publish decision, downstream import check, known caveats, and rollback target.
Static previews do not stress shoulder twist, hip rotation, root motion, hand reach, foot plants, or target proportions under real motion.
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 Studio to prepare a custom character target, then reuse it in future video mocap jobs.
A practical overview of AIMoCap avatar upload, A-pose, skeleton binding, test, publish, and reuse.
Prepare reusable custom avatar targets for AIMoCap so source video can drive your own character workflow.
These related AIMoCap resources document the workflow boundaries, output formats, and implementation details referenced on this page.
