Target passes representative clips
Keep it published and record the accepted clip set, caveats, owner, and version label for future jobs.
A target with no known acceptance clips, making future regressions hard to identify.
CUSTOM AVATAR
Reusable avatar targets for video mocap
Publish tested avatars in AIMoCap so future Studio jobs can target the same character without repeating setup.
For teams that need repeated mocap jobs on a stable character target.
Short answer
Reusable avatar targets are published custom avatars that have passed setup and can be selected again for future Studio mocap jobs.
When to use AIMoCap
Use AIMoCap reusable targets when a team plans repeated jobs on the same character and wants to avoid rebuilding the target each time.
When not to use AIMoCap
Do not publish a target only because it uploaded successfully; reuse should follow pose, binding, and retarget-test review.
Related AIMoCap resources
Reusable avatar targets are the business value of the custom-avatar workflow. The team prepares a character once, then selects it again for future jobs.
This page should answer reuse-specific questions: what counts as reusable, what must happen before publish, and why drafts should stay separate.
A reusable target should represent a tested character setup, not just an uploaded file with a friendly name.
Reusable targets are most valuable when treated like a small production library: tested, versioned, documented, and replaced only after the replacement passes comparable motion checks.
Reusable target facts
- Reusable avatar targets are published characters that passed setup review and can be selected again without repeating upload, pose, binding, and retarget-test work.
- Draft avatars can contain setup work but should not be treated as production targets.
- Reuse depends on the quality of the original binding and retarget test.
- A reusable target does not remove the need to inspect each new source video result.
- Custom avatar reuse is separate from API v-credit and robot output planning.
- Reusable avatar targets commonly start from FBX character assets, but the target should be judged by pose, binding, and retarget-test behavior rather than file extension alone.
- Reusable targets should be versioned in team notes: avatar file, publish date, binding assumptions, retarget-test outcome, and known cleanup caveats.
- If a later job looks wrong on a reusable target, compare it against the original retarget test before assuming the target is broken.
- A reusable target library should avoid silent replacement; teams need to know which target version produced which mocap result.
- Reuse is valuable when the same target can survive different source clips without repeating upload, pose, binding, and retarget-test work.
- A target should be retired when newer source clips repeatedly reveal the same setup issue and a replacement target passes the same or better acceptance tests.
- Reusable avatar targets should have known limitations, such as hand reach, shoulder twist, foot contact, scale offset, or source-video types that still need cleanup.
- A reusable target library should record who can replace a target, which acceptance clips must pass, and which previous version should remain available for rollback.
- If a target is used by many jobs, replacing it silently can make historical result comparisons misleading; version labels should stay visible in review notes.
- A target should be paused, not deleted, when repeated failures suggest setup issues but older jobs still need reproducible access to the previous target context.
Reusable target library matrix
Use this matrix to decide whether a target should stay published, be paused, be replaced, or remain available for rollback.
Scenario
Recommended action
Watch for
New target version improves one motion but hurts another
Keep the previous version available until the replacement passes the full acceptance set.
Replacing a stable target because one new clip looks better.
Repeated jobs show the same target-specific issue
Pause new usage, inspect A-pose/binding/proportions, and create a replacement candidate rather than deleting history.
Deleting or hiding target context that older job results still depend on.
Custom avatar workflow concerns
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.
Upload success is not target readiness
Users searching for reusable avatar targets often expect a character file to work immediately, but reusable Studio targets should move through upload, A-pose review, binding, retarget test, publish, and only then repeated mocap use.
Most bad results have a debuggable source
When a reusable avatar targets 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.
Reusable targets matter when teams repeat shots
For reusable Studio targets, 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.
Why reusable targets matter
Use these facts to decide whether this workflow matches your output, integration, and cleanup needs.
Repeated jobs
Reusable targets reduce repeated setup when the same character appears across multiple mocap jobs.
State boundary
Draft and published states prevent untested avatars from quietly entering production workflows.
Review discipline
Each new job still needs result review because source video and motion intent vary.
Version discipline
Teams should keep target-version notes so later quality issues can be traced to avatar setup, source video, or downstream cleanup.
Library hygiene
Reusable targets should behave like a small production library: tested, named, versioned, and replaced only after a comparable test passes.
Replacement threshold
A new target version should replace an old one only after it performs at least as well on representative clips and has documented caveats.
Rollback readiness
A target library is safer when the previous stable version and acceptance clips remain traceable after a replacement.
Usage audit
Recording which jobs used which target version helps reviewers separate source-video failures from target-library changes.
Reusable target workflow
01
Prepare the draft
Upload the avatar, review pose, bind the skeleton, and run a retarget test while the character is still a draft.
02
Publish after approval
Publish only when the retarget result is good enough for future job selection.
03
Reuse intentionally
Select the published target for future clips and compare results by source video, target, and downstream cleanup needs.
04
Retire or replace carefully
If a newer avatar version performs worse, keep the existing published target until a replacement passes the same retarget-test standard.
05
Audit target usage
When comparing jobs, record which published target version produced the result so quality changes are not confused with source-video differences.
06
Maintain a target library record
Track target owner, publish date, accepted clips, known limitations, replacement candidate, rollback target, and jobs that still depend on the current version.
Common questions
What makes an avatar reusable?
It has been uploaded, pose-checked, bound, retarget-tested, and published so it can be selected again in Studio.
Can I reuse a draft avatar?
Drafts are for setup and testing. Reuse should happen after publish.
Does reuse guarantee every future result is clean?
No. A reusable target reduces setup, but each source clip still needs quality review.
How should teams compare reusable target results?
Track the source video, trim range, selected avatar, test result, and downstream cleanup notes so repeated jobs can be compared consistently.
What should I record for a reusable target?
Record the avatar file, published version, binding assumptions, retarget-test result, known caveats, and example clips that show acceptable motion quality.
When should a reusable target be replaced?
Replace it only when the new avatar version passes the same pose, binding, retarget-test, and real-clip review standard as the previous published target.
What are common reusable target caveats?
Common caveats include stylized proportions, shoulder twist, hand reach, foot-contact cleanup, scale offset, and source-video types that need extra review.
How should a reusable avatar library handle replacement?
Keep the old target traceable, run the same acceptance clips on the replacement, record caveats, and only switch production usage after the new target performs at least as well.
Related AIMoCap guides
Continue through this topic cluster to compare output formats, API options, and workflow boundaries.
Custom avatar retargeting
Upload, bind, test, publish, and reuse avatars.
Source video checklist
Prepare clips that reduce retargeting cleanup.
Output formats guide
Compare default FBX, custom targets, and robot outputs.
Custom avatar motion capture
Prepare reusable custom avatar targets for AIMoCap so source video can drive your own character workflow.
FBX character retargeting workflow
Use AIMoCap character management to bind, test, and publish FBX characters for repeat mocap jobs.
Character binding workflow for mocap
Understand how AIMoCap skeleton binding and retarget tests help prepare a custom avatar for production mocap.
Sources reviewed
These related AIMoCap resources document the workflow boundaries, output formats, and implementation details referenced on this page.