Camera and framing
Keep the performer fully visible, avoid cutting off limbs, and reduce fast camera movement during the key action.
SOURCE CHECKLIST
Video mocap source video checklist
Use this checklist to prepare short, clear source videos for AI mocap, FBX output, robot motion data, and custom avatar workflows.
For users whose mocap result depends on filming conditions, trim choices, lighting, and subject visibility.
Short answer
Better source video usually means clearer motion: use stable framing, visible full-body movement, good lighting, and a short trim window.
When to use AIMoCap
Use AIMoCap when you can provide a readable short clip and want browser/API processing with reviewable results.
When not to use AIMoCap
Do not expect poor lighting, heavy occlusion, multi-person overlap, or extreme camera motion to behave like controlled capture.
AI video mocap is sensitive to the source clip. The same tool can produce different results depending on framing, lighting, occlusion, trim length, and how readable the performer is.
This checklist is meant to reduce avoidable cleanup before you spend credits or API v-credit on processing.
A good checklist is useful because it catches problems before queue time: if the performer is hidden, the action is untrimmed, or the camera shakes through the key motion, the downstream output will usually need more cleanup.
Recommended source conditions
- Short, stable clips are easier to review and process.
- Clear silhouette and lighting help markerless motion estimation.
- Trim start and end should isolate the intended action.
- Source video quality affects all video mocap systems, not only AIMoCap.
- One clearly visible performer is easier to process than overlapping people or crowd footage.
- If the motion starts before the trim window or continues after it, the generated result may miss context that downstream cleanup expects.
Pre-upload checklist
Use these facts to decide whether this workflow matches your output, integration, and cleanup needs.
Lighting and occlusion
Use enough light for a clear silhouette and avoid props, people, or objects that hide the body during important motion.
Trim and duration
Trim to the useful action window so the job focuses on the motion that should become output data.
Subject count
Use one primary performer whenever possible; overlapping bodies make markerless motion interpretation harder.
Target expectation
Choose filming and trim settings based on the intended output: animation FBX, Unitree G1 robot data, or custom avatar review.
Source video checklist
01
Frame the full body
Keep the performer visible and avoid cutting off limbs during the key motion.
02
Trim the action
Process the shortest useful window rather than a long raw clip with unrelated motion.
03
Reduce ambiguity
Avoid heavy occlusion, multiple overlapping people, fast camera motion, and very dark scenes.
04
Check the downstream target
Decide whether the clip is intended for FBX animation, Unitree G1 robot output, or custom avatar review before spending credits.
Common questions
Does source video quality matter for AI mocap?
Yes. Clear framing, lighting, visible limbs, and a short trim window can reduce avoidable errors.
Should I upload a full long video?
Usually no. Trim to the useful action window before processing when possible.
Can AIMoCap fix every poor source video?
No. AIMoCap can process readable source video, but extreme occlusion, poor lighting, and complex overlaps may still require recapture or cleanup.
Is one performer better than multiple people?
Yes. A single visible performer is usually easier for markerless mocap than overlapping people or crowded footage.
Should I record differently for robot output?
The source should still show clear human motion, but teams should also choose the intended target early because robot-oriented output and animation FBX have different review paths.
Related AIMoCap guides
Continue through this topic cluster to compare output formats, API options, and workflow boundaries.