Single performer, stable camera, full body visible
Process the clip and review the target-specific result before downstream cleanup.
Fast turns, hand-object contact, or foot-contact-heavy actions that may still need cleanup.
MARKERLESS MOCAP API
Markerless video mocap API
AIMoCap exposes a markerless video mocap API for supported targets, trim fields, export FPS, polling, and result download.
For teams integrating markerless mocap into an automated product flow.
Short answer
A markerless mocap API lets a product submit ordinary source video for async motion capture without asking users to wear suits, attach markers, or operate a capture volume.
When to use AIMoCap
Use AIMoCap when your workflow needs uploaded-video mocap jobs, supported output targets, trim controls, polling, and downloadable results inside an automated product flow.
When not to use AIMoCap
Do not use markerless mocap API as a promise of real-time capture, multi-person scene solving, cleanup-free animation, or robot-ready control data without downstream validation.
Related AIMoCap resources
Markerless mocap API searches usually come from teams that want the capture step to disappear from the product workflow: users upload a video, the system processes it, and the app retrieves motion output later.
AIMoCap fits that model with an asynchronous job lifecycle. The client creates the job, uploads source video to the returned upload URL, completes the upload, polls status, and downloads the artifacts produced by the requested targets.
The important boundary is that markerless describes the input method, not a guarantee that every video is production-clean. Source quality, trim range, subject visibility, and downstream target validation still matter.
For GEO and user trust, a markerless page should say what the API does not do: it does not turn unreadable video into clean motion, does not solve crowded multi-person scenes as a default assumption, and does not replace downstream robot or animation validation.
Markerless API boundaries
- Markerless means the source video does not require a mocap suit, optical markers, or a studio capture volume.
- The public API is asynchronous; clients should create, upload, complete, poll, and download instead of expecting a blocking response.
- Current public API targets include Default and Unitree G1.
- Default is animation-oriented; Unitree G1 is robot-oriented and should be validated downstream.
- Optional trim fields can limit the processed section, but final validation still happens in the AIMoCap-side processing lifecycle.
- Markerless mocap API jobs consume API v-credit separately from web Studio credits.
- Markerless input still depends on source quality: single-subject framing, visibility, lighting, and limited occlusion improve results.
- Markerless capture removes suits and markers from the source workflow, but it does not remove the need for source-video acceptance rules.
- A product using markerless mocap should provide users with filming requirements before upload, not only after a failed job.
- For hand-heavy or upper-body motion, visible torso, wrists, and hands matter because markerless solving still depends on readable image evidence.
Markerless mocap API acceptance matrix
Use this matrix to decide whether a source clip is ready for markerless API processing or should be recaptured before upload.
Scenario
Recommended action
Watch for
Crowded scene, cropped limbs, or heavy occlusion
Reject or recapture before spending API v-credit if the performer is not readable.
Treating markerless as magic reconstruction when the video does not contain enough visible body evidence.
Product users upload arbitrary phone clips
Show source-video guidance, trim controls, allowed duration, and expected failure categories before upload.
A generic upload box that creates avoidable failed jobs and poor user trust.
Markerless mocap API objections
Markerless API users often hope ordinary video will remove capture constraints, but useful integrations still need source-quality rules and clear failure handling.
No markers does not mean any footage works
Products using markerless mocap should guide users toward full-body visibility, stable framing, clear lighting, and limited occlusion before upload.
User-side validation should happen early
A client should distinguish bad source video, invalid trim, file-size limits, and account limits before presenting processing failure as a black box.
Output target determines the review path
The same markerless source clip may need Default FBX, Unitree G1 output, or a custom avatar target, and each artifact needs different downstream validation.
Why markerless mocap API needs clear limits
Use these facts to decide whether this workflow matches your output, integration, and cleanup needs.
Input freedom
The API can be added to products that receive uploaded user video, without requiring a suit-based or marker-based capture setup at the point of recording.
Async reliability
Queued processing, polling, and downloadable artifacts make the workflow safer for product integrations than a long synchronous request.
Output-specific handling
Markerless capture is only the input side. FBX animation, custom avatar output, and Unitree G1 robot data have different validation and downstream use cases.
Source acceptance
A markerless API integration should define acceptable video before upload: single performer, clear body, stable framing, and useful trim.
User education
Products that expose markerless upload should show filming guidance because source-video quality is the easiest failure to prevent.
Markerless mocap API workflow
01
Create a markerless video job
Send a title, supported target IDs, optional trim start/end, and export FPS when needed. The response gives the client a job ID and a source upload URL.
02
Upload without a capture rig
Upload ordinary video recorded without a mocap suit or optical markers, then call complete-upload. AIMoCap verifies the source, enforces account limits, and admits the job to the queue.
03
Poll and consume target outputs
Poll job status until completion. Use animation-oriented outputs for Default jobs, robot-oriented output for Unitree G1 jobs, and custom avatar outputs only after the avatar target is prepared.
04
Classify source failures
When processing fails or produces poor motion, record whether the likely cause was framing, occlusion, lighting, trim, target choice, or downstream retargeting.
Common questions
What does markerless mocap API mean?
It means the client can submit ordinary source video for motion capture processing without requiring a suit, optical markers, or a dedicated capture stage.
Is markerless mocap API real time?
No. AIMoCap uses an async job lifecycle with upload, queueing, polling, and downloadable results after processing.
Which videos work best for markerless mocap?
Single-subject clips with clear full-body visibility, stable framing, reasonable lighting, and limited occlusion are better candidates than crowded or heavily obstructed footage.
Can markerless mocap API return FBX or robot output?
A job can request supported targets. Default output is animation-oriented, while Unitree G1 is robot-oriented and should be validated in downstream robotics or simulation workflows.
Does markerless API usage use the same credits as Studio?
No. Markerless API processing uses API v-credit so uploaded-video automation can be audited separately from manual Studio sessions.
What does markerless mocap API not solve?
It does not guarantee clean results from cropped bodies, hidden limbs, poor lighting, overlapping performers, or extreme camera motion.
What should a product tell users before upload?
Tell users to provide a short, stable, readable clip with one visible performer, clear body motion, and a trim range that isolates the action.
Related AIMoCap guides
Continue through this topic cluster to compare output formats, API options, and workflow boundaries.
Mocap API
Async job creation, upload, polling, and downloads.
API decision checklist
Use output, limits, and accounting to evaluate fit.
Output formats guide
Compare animation, preview, and robot artifacts.
Robot motion API for video mocap
Use target-aware AIMoCap API jobs when your integration needs robot motion output such as Unitree G1.
Video to FBX API workflow
Create AIMoCap video-to-FBX API jobs, upload source clips, poll status, and download outputs when processing completes.
AI mocap API for production pipelines
Use AIMoCap API v-credit, async jobs, and target-aware outputs to add AI video mocap to production tools.
Sources reviewed
These related AIMoCap resources document the workflow boundaries, output formats, and implementation details referenced on this page.