MOCAP API
Use AIMoCap API v-credit, async jobs, and target-aware outputs to add AI video mocap to production tools.
For product teams evaluating an AI mocap API with public documentation.
An AI mocap API should turn uploaded source video into structured motion outputs while keeping target selection, usage accounting, and result downloads explicit.
Use AIMoCap when a product needs upload-based video mocap for Default animation output, Unitree G1 robot output, or both, with API v-credit separated from Studio credits.
Do not use AIMoCap's API when the integration needs real-time live capture, undocumented private targets, or a one-call synchronous solve that blocks until all artifacts are ready.
People searching for an AI mocap API are usually evaluating whether a video-to-motion capability can become part of a product workflow, not just whether a demo page exists.
AIMoCap's public API is intentionally target-aware. A client can request animation-oriented Default output, Unitree G1 robot-oriented output, or both when the account and job limits allow it.
The important boundary is product fit. API v-credit, supported targets, trim fields, export FPS, queue behavior, and downloadable artifacts should be reviewed before the API becomes a production dependency.
Use this matrix to decide whether the integration is ready for production API jobs or still needs workflow design.
Developer-facing mocap searches are rarely about slogans. They are about whether uploads are reliable, billing is auditable, failed jobs are explainable, and returned artifacts fit the actual pipeline.
Teams evaluating a AI mocap API usually worry about long-running jobs and retries, so the integration should store the job ID for production mocap pipelines, upload the source video, call complete-upload, and poll instead of waiting on one fragile request.
For AI mocap API, developers often need to explain automated job cost to finance or product teams. AIMoCap keeps API v-credit separate from web Studio credits so production mocap pipelines traffic can be audited independently.
In production mocap pipelines, API users should test whether they need Default FBX, Unitree G1 robot output, or a prepared custom avatar target before scaling traffic; the same source video can produce different AI mocap API artifacts for different downstream teams.
For AI mocap API evaluation, include an accepted clip, a cropped-body rejection, a trim mistake, a queue wait, a multi-target result, and a ledger check so product and support teams see the full operating envelope.
An AI mocap API product should store the job ID, account plan, target list, v-credit event, terminal status, and artifact family because automated users usually report job symptoms rather than raw API responses.
Use these facts to decide whether this workflow matches your output, integration, and cleanup needs.
A production integration should know whether it needs animation output, robot output, custom-avatar workflows, or a mix of target types.
API v-credit makes automated jobs visible separately from web Studio usage, which matters for billing, quota, and account ledgers.
An AI mocap API client should treat upload, validation, queue admission, processing, and result download as separate states so retries and terminal failures stay explicit.
A production integration is easier to debug when each job stores source metadata, target selection, status transitions, ledger entry, and downloaded artifact checksums.
A credible AI mocap API page should say how to test the integration before launch, including success, recoverable timeout, terminal failure, and artifact-download paths.
AI mocap API developers need a clear mapping between backend job state, v-credit usage, artifact readiness, and user-facing UI state so queued work is not mislabeled as failed processing.
For AI mocap API evaluation, keep accepted, rejected, multi-target, trim-error, queue-wait, and ledger-check cases so the product team understands normal behavior beyond one successful demo clip.
The API client should persist job ID, target list, API plan, v-credit event, terminal status, and artifact family before retrying so automated users can report a concrete failure.
Product copy should distinguish model processing, source validation, queue wait, insufficient v-credit, target artifact failure, and completed results instead of collapsing all errors into processing failed.
Decide whether the integration needs Default FBX-oriented motion, Unitree G1 robot-oriented artifacts, or a combined job for review and robotics workflows.
Plan around API v-credit, trim duration, file size, export FPS, and concurrency before sending user video into an automated pipeline.
Create the job, upload source video, complete upload, poll status, and download artifacts only after the job reaches a terminal completed state.
Persist job ID, target IDs, upload-complete state, terminal status, downloaded artifact type, and v-credit ledger reference so support can trace the full run.
Before production launch, test a representative clip set across upload, complete-upload, queueing, polling, download, ledger checks, and downstream artifact import.
The API exposes an upload, complete-upload, status polling, and result download workflow so a product can automate supported video mocap jobs instead of requiring manual Studio-only operation.
Use Default for animation-oriented FBX review and Unitree G1 for robot-oriented motion output. Request both only when the integration needs both artifact families from the same source clip.
No. API jobs use API v-credit so automated product usage can be tracked separately from web Studio credits.
No. AIMoCap processes source video into motion artifacts, but source quality, occlusion, framing, and downstream cleanup still matter.
Store job ID, requested target IDs, upload completion, terminal status, artifact downloads, v-credit ledger reference, source-video notes, and any retry or failure stage.
Test create, upload, complete-upload, polling, completed result download, source-quality failure, timeout recovery, ledger trace, and downstream artifact import on representative clips.
Continue through this topic cluster to compare output formats, API options, and workflow boundaries.
Async job creation, upload, polling, and downloads.
Use output, limits, and accounting to evaluate fit.
Compare animation, preview, and robot artifacts.
Create AIMoCap video-to-FBX API jobs, upload source clips, poll status, and download outputs when processing completes.
AIMoCap exposes a markerless video mocap API for supported targets, trim fields, export FPS, polling, and result download.
Understand the async job lifecycle for AIMoCap API jobs: create, upload, complete, poll, and download.
These related AIMoCap resources document the workflow boundaries, output formats, and implementation details referenced on this page.
