Character test fails before any real mocap job
Keep the character in setup and fix pose, binding, or rig issues before publishing.
Publishing a target too early and then misattributing every future mocap issue to source-video quality.
CUSTOM AVATAR
Custom character mocap from video
Use AIMoCap Studio to prepare a custom character target, then reuse it in future video mocap jobs.
For character teams that need video mocap results on their own model.
Short answer
Custom character mocap turns a source video into motion reviewed on a prepared character, but the character must first pass upload, binding, test, and publish steps.
When to use AIMoCap
Use AIMoCap when your team wants to reuse the same character across multiple Studio jobs and needs a clear setup workflow before motion is applied.
When not to use AIMoCap
Do not use a custom character target when the model is still a rough draft, the skeleton mapping is unknown, or the goal is only a generic Default output file.
Related AIMoCap resources
Custom character mocap searches are usually about identity: the motion needs to land on a particular character, not just on a generic skeleton.
AIMoCap treats the character as a target that must be prepared before processing. That means upload, A-pose adjustment if needed, skeleton binding, retarget testing, and publish.
Once the target is published, Studio users can focus future jobs on source-video quality, trim, target selection, and result review instead of repeating character setup.
A useful custom-character workflow should produce an acceptance record: which character version was used, which test clip passed, which motions still need cleanup, and when the target should be revised instead of reused.
Custom character mocap facts
- Custom character mocap is a target workflow, not only a file upload.
- Published characters can reduce repeated setup across future Studio jobs.
- Retarget quality depends on source asset structure and the source video's readability.
- A draft character should not be treated as production-ready until it is tested.
- Default output, custom character output, and robot output serve different downstream needs.
- The same source motion can look different depending on character proportions and skeleton mapping.
- Teams should keep a record of which published character was used for each job when comparing outputs.
- A custom character should be tested against the motion categories it will actually receive, not only a neutral walk or a simple idle clip.
- If repeated clips fail in the same way on one character, the avatar target is more suspicious than any one source video.
- For stylized characters, shoulder width, arm length, hand size, foot shape, and root height can make otherwise acceptable motion look wrong without cleanup.
- A custom character should have an acceptance packet before production reuse: source asset version, A-pose notes, binding result, retarget-test clip, publish decision, and known cleanup caveats.
- If a character is used for close-up shots, hand-heavy motion, dance, or stylized poses, its acceptance set should include those categories instead of only a neutral walk.
- A rejected custom-character result should be labeled as source-video issue, target setup issue, downstream cleanup issue, or unsupported motion category before another job is submitted.
Custom character mocap decision matrix
Use this matrix to decide whether the next action belongs to character setup, source-video capture, or downstream animation cleanup.
Scenario
Recommended action
Watch for
Character test passes but a job looks poor
Compare Default output, custom character output, and source-video readability before deciding what to rerun.
Assuming the avatar is broken when the source clip has occlusion, cropping, blur, or unclear contacts.
Many jobs will reuse the same character
Publish the tested target and record target version, accepted test result, and downstream cleanup notes.
Losing target-version context when comparing several source videos over time.
Stylized character proportions exaggerate motion errors
Run representative acceptance clips before publishing and document known issues such as hand reach, shoulder twist, foot contact, or scale offsets.
A character that looks fine in setup but makes normal source motion appear distorted during real jobs.
Different clips fail in the same way on the same target
Inspect avatar setup, A-pose, binding, root orientation, and scale before rerunning more source videos.
Burning credits on reruns when the repeated symptom points to target setup.
The character will be used in production shots
Create an acceptance packet with representative clips, downstream tool, cleanup owner, known caveats, and target-version notes.
Promoting a character from one successful test clip without checking the motion categories that production will actually use.
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 custom character mocap often expect a character file to work immediately, but custom character animation 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 custom character 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.
Reusable targets matter when teams repeat shots
For custom character animation, 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 custom character targets help repeated work
Use these facts to decide whether this workflow matches your output, integration, and cleanup needs.
Repeatability
A published character target lets teams run multiple clips against the same prepared asset instead of manually rebuilding the target each time.
Review clarity
Seeing motion on the intended character helps teams catch proportion, pose, and limb issues earlier than a generic preview alone.
Workflow separation
Character setup remains separate from mocap submission, which keeps failures easier to diagnose and reduces accidental production use of drafts.
Acceptance coverage
A reusable character should be validated with representative motion categories so a single flattering test does not hide shoulder, hip, hand, or foot issues.
Target-version trace
Recording the character version used for each result helps teams distinguish avatar changes from source-video differences.
Production acceptance packet
A useful custom-character page should name what must be saved before scale: source FBX version, target ID, test clips, caveats, cleanup owner, and verdict.
Failure ownership
Labeling failures by source video, target setup, downstream cleanup, or unsupported motion prevents repeated jobs from hiding the real fix.
Custom character mocap workflow
01
Create a reusable character target
Start in character management and prepare the model as a target rather than treating each video job as a one-off upload.
02
Validate motion on the character
Run a retarget test to check whether the skeleton mapping behaves correctly on limbs, torso, root, and timing.
03
Publish only after review
Publish the character when the test result is acceptable so the target can be selected in future video mocap jobs.
04
Run mocap jobs with the target selected
Use short, readable source clips and choose the published character target when the job should preview motion on that model.
05
Keep an acceptance record
Record avatar version, source clip, trim range, target selection, cleanup notes, and whether the result was accepted, rejected, or needs avatar setup changes.
06
Build a character acceptance packet
Store source FBX version, published target ID, representative test clips, known caveats, cleanup owner, and accepted/rejected decision before scaling usage.
Common questions
What is custom character mocap?
It is a workflow where a prepared character target is used for video mocap jobs so the resulting motion can be reviewed on that specific model.
Do I need to publish the character first?
Yes. Publishing should happen after upload, pose review, binding, and retarget testing so the character becomes a reusable target.
Can a custom character fix poor source video?
No. Character setup and source-video quality both matter. Occlusion, poor lighting, or unclear motion can still reduce result quality.
Is this the same as a robot target?
No. Custom characters are animation targets. Unitree G1 and robot-oriented outputs are separate target workflows.
When should I use Default output instead?
Use Default output when you mainly need a generic animation-oriented result or downloadable FBX motion rather than review on a specific character.
What should I test before reusing a custom character?
Test the motions your team actually needs, including turns, hand-heavy actions, foot-contact clips, and any stylized poses that stress the character proportions.
When is the avatar target the likely problem?
Suspect the target when multiple different source clips show the same scale, root, shoulder, hand, or foot-contact issue on that character.
What should a custom character acceptance packet include?
Include source FBX version, published target ID, A-pose notes, binding result, representative test clips, known caveats, cleanup owner, and accepted or rejected status.
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.
FBX avatar mocap workflow
Upload, bind, test, and publish FBX avatars so AIMoCap video mocap jobs can reuse them as targets.
Avatar retargeting workflow for video mocap
A practical overview of AIMoCap avatar upload, A-pose, skeleton binding, test, publish, and reuse.
Custom avatar motion capture
Prepare reusable custom avatar targets for AIMoCap so source video can drive your own character workflow.
Sources reviewed
These related AIMoCap resources document the workflow boundaries, output formats, and implementation details referenced on this page.