CUSTOM AVATAR
Prepare reusable custom avatar targets for AIMoCap so source video can drive your own character workflow.
For teams searching for motion capture on a custom avatar.
Custom avatar motion capture means applying solved video motion to a prepared and published avatar target, not uploading a character during every job.
Use AIMoCap when a team needs repeated mocap jobs on the same prepared character and wants Studio review before download or downstream cleanup.
Do not use this path when the avatar is still a draft, when binding has not been tested, or when Default output is enough.
Custom avatar motion capture is about target reuse. The same prepared character can be selected again after upload, pose review, binding, retarget testing, and publish.
This is different from generic video-to-FBX output because the motion is evaluated on the team's own avatar.
The page should remain focused on repeatable character workflows rather than repeating the entire mocap product pitch.
For teams, the useful question is operational: is this avatar ready for the motions we actually record, and do we know what to inspect when a job looks wrong?
Use this matrix to decide whether to accept, recapture, fix the avatar, or clean up downstream.
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 custom avatar motion capture often expect a character file to work immediately, but custom avatar motion capture should move through upload, A-pose review, binding, retarget test, publish, and only then repeated mocap use.
When a custom avatar motion capture 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 custom avatar motion capture, 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.
Teams can inspect motion on the intended character rather than only on a generic target.
For custom avatar motion capture, publishing turns upload, pose, binding, and retarget-test work into a reusable target for future Studio jobs.
Custom avatar motion capture quality depends on both the source video and the prepared avatar setup, so bad results should be debugged from both sides.
Bad custom-avatar motion should be debugged by separating avatar setup, source-video readability, and downstream cleanup needs.
The page should tell users what to check after a custom-avatar job, not only that the avatar can be selected.
Comparing source video, Default output, and custom-avatar output gives reviewers a practical way to locate the failure layer.
Recording accepted, recapture, avatar-fix, or cleanup-needed verdicts makes repeated custom-avatar jobs easier to improve over time.
Keeping Default output next to custom-avatar output gives teams a practical control sample for every disputed custom-avatar result.
A job note should identify the exact published target version so a later avatar replacement is not confused with a source-video change.
Use character management to upload, pose-check, bind, retarget-test, and publish the avatar.
When submitting a mocap job, choose the published avatar if the result should be reviewed on that specific character.
Review whether the source clip and avatar proportions produce acceptable motion before downstream cleanup.
Compare the same source on Default output and the custom avatar when needed, then decide whether the avatar setup, source clip, or cleanup layer is the problem.
For each reusable-avatar job, keep source clip, trim, selected target, export FPS, result verdict, and whether the fix belongs to avatar setup, recapture, or downstream cleanup.
When a custom-avatar result is questioned, compare it with Default output from the same source so reviewers can separate solved-motion quality from target-specific retargeting issues.
Yes, after the avatar is uploaded, bound, tested, and published as a reusable target.
Yes. Default output is generic animation-oriented output; a custom avatar job reviews motion on a prepared character.
No. Poor lighting, occlusion, or unclear motion can still reduce result quality.
The avatar should pass pose review, skeleton binding, a retarget test, and publish so future jobs use a stable target rather than a draft.
Check avatar pose, skeleton binding, scale, root orientation, source-video clarity, trim range, and downstream cleanup notes separately before changing the whole workflow.
Suspect the avatar setup when different source clips show the same limb swap, scale, root, shoulder, or foot-contact problem on the same published target.
Record source clip, trim range, selected avatar version, export FPS, result verdict, and whether the next fix is recapture, avatar setup, or downstream cleanup.
Run representative acceptance clips first and pause repeated jobs if the same shoulder, hand reach, foot contact, or root problem appears across clips.
Default output is the control sample. If Default is good but the custom avatar is poor, inspect the target setup. If both are poor, inspect the source clip or base solve first.
Record source clip, trim, export FPS, capture type, avatar target version, Default baseline verdict, custom target verdict, and whether the next action is recapture, avatar fix, or cleanup.
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.
Use AIMoCap character management to bind, test, and publish FBX characters for repeat mocap jobs.
These related AIMoCap resources document the workflow boundaries, output formats, and implementation details referenced on this page.
