Repeatability
A published character target lets teams run multiple clips against the same prepared asset instead of manually rebuilding the target each time.
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.
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.
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.
Why custom character targets help repeated work
Use these facts to decide whether this workflow matches your output, integration, and cleanup needs.
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.
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.
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.
Related AIMoCap guides
Continue through this topic cluster to compare output formats, API options, and workflow boundaries.
Sources reviewed
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