u/AIMoCap

Hi everyone,

This is an extended comparison based on the original motion capture test shared by u/Shateiel3D on X, which compared Unreal Engine 5.8 MetaHuman Animator vs QuickMagic using the same motion actor performance.

In this post, I added AIMoCap into the same benchmark using the identical motion actor performance video to ensure a consistent and fair comparison.

What I found interesting is that even with the same input motion, each system seems to interpret foot contact and motion stability very differently, especially in fast directional changes and transitions.

Workflow:

• Input: professional motion actor performance video
• Same source used across all systems
• Motion processed independently per system

Systems compared:

• Unreal Engine 5.8 MetaHuman Animator
• QuickMagic
• AIMoCap

Key aspects observed:

• Motion stability
• Foot sliding / ground contact behavior
• Overall perceived realism differences

This is intended as an experimental extension of an existing benchmark, not a ranking or “best tool” comparison.

I’m curious how others here would interpret the differences — especially from an Unreal Engine animation workflow perspective:

👉 Which result would actually require the least cleanup in production?

Hi everyone,

I’ve been experimenting with converting normal human motion videos into animation data that can be used in MMD-style workflows.

The goal is to see how far AI-based motion capture can go in generating usable character animation from a single video input.

Here is a short test result.

Workflow:
Video → motion extraction → retarget → MMD-style animation

I’m using a small tool I’m building for this experiment:
https://aimocap.net

I’m mainly interested in:

• How usable the motion feels in MMD
• Whether foot sliding / stability looks acceptable
• What issues you notice in the animation

This is still an early experiment, so any feedback from MMD users would be really valuable.

Happy to share more details or additional tests if anyone is interested.

I've been experimenting with converting ordinary third-person videos into humanoid motion data.

This demo includes several motion categories:

• Acting
• Sports
• Combat
• Dance

The motivation is not animation alone.

Recent humanoid robotics work increasingly relies on large-scale motion datasets and motion priors to improve movement quality, robustness, and generalization. Projects such as NVIDIA KIMODO also show the value of scaling high-quality motion data for downstream humanoid motion generation and control.

This made me wonder whether ordinary videos could become a low-cost source of motion data for humanoid systems.

There is already a massive amount of human motion available in online videos. If useful motion can be extracted reliably, it may help expand humanoid motion datasets beyond traditional mocap pipelines.

For this experiment, I focused on:

• Foot contact stability
• Reduced foot sliding
• Natural balance and movement dynamics
• Consistency across different motion styles

The long-term idea is:

Video → Motion Data → Motion Models → Humanoid Control

For anyone interested in testing their own clips, I made a public demo available here: huggingface demo

I'd love to hear thoughts from people working on humanoid robotics, motion generation, imitation learning, or robot locomotion.

Testing a video-to-FBX workflow across different motion types:

​

• Acting

• Sports

• Dance

​

For best results, the input videos are:

• Full-body

• Static camera

• Clear, high-quality video

​

I'm currently focusing on foot contact stability, root motion consistency, and retargeting quality.

​

Free demo at: huggingface space

​

I'd love feedback on difficult motions, foot sliding, root motion, and Blender retargeting workflows.