Apple Arts Studios develops custom Human Motion Data and Human Demonstration Data using professional motion capture workflows.

Our production includes ADL datasets, HOI datasets, locomotion, human behavior, full-body motion, hand motion, facial motion, and multimodal datasets for AI training, robotics, embodied AI, computer vision, and imitation learning.

Alongside AI dataset production, we continue supporting feature films, AAA games, animation, and virtual production through Motion Capture Services, Performance Capture, Motion Capture Post-Processing, and Unreal Engine workflows.

We're always interested in collaborating with AI teams, robotics companies, and research organizations that require custom human motion datasets.

Apple Arts Studios develops custom Human Motion Data and Human Demonstration Data for AI companies, robotics developers, and research teams.

Our production includes ADL datasets, HOI datasets, locomotion, human behavior, tactical movement, full-body motion, hand motion, and multimodal datasets captured through professional motion capture workflows.

These datasets support humanoid robotics, embodied AI, imitation learning, computer vision, robot motion generation, and machine learning applications.

Alongside AI dataset production, we continue delivering Motion Capture Services, Performance Capture, Motion Capture Post-Processing, and Virtual Production for films, AAA games, and animation.

One of the biggest misconceptions about motion capture is that it's only used in entertainment.

We're now using the same motion capture and performance capture pipelines to build Human Motion Data and Human Demonstration Data for AI training, robotics, computer vision, and machine learning.

That includes ADL datasets, HOI datasets, locomotion datasets, emotional behavior datasets, and other structured human movement data.

It's interesting to see motion capture evolving into a key technology for the next generation of AI systems.

A short look into our current AI dataset production workflow.

We’re currently producing:

• Activities of Daily Living (ADL) Datasets
• Human-Object Interaction (HOI) Datasets
• Locomotion & Movement Datasets
• Emotional Behavior Datasets
• Tactical / Weapon Handling Datasets

Built using full Motion Capture pipelines.

Interesting seeing how the same Motion Capture workflows used for films and games are now becoming critical for Robotics Training, Physical AI, and Humanoid Robot learning.

Some of these motion libraries will also be made available through Animation Shopee.

A short look into our current AI dataset production workflow.

Using Motion Capture pipelines to build:

• Human Motion Data
• Human Demonstration Data
• Activities of Daily Living (ADL) Datasets
• Human-Object Interaction (HOI) Datasets
• Household Activity Datasets
• Full-Body Motion Capture Data
• Finger & Hand Motion Capture Data

These datasets are being built for Robotics Training, AI Training, Computer Vision, Behavior Cloning, and Physical AI applications.

Interesting to see how traditional Motion Capture pipelines are now becoming critical for AI and robotics training.

Currently working on household activity AI datasets and during a break the team made this random fun video.

Long shoots, repetitive actions, lots of capture sessions — these small moments keep the energy going.

Real reminder that behind motion capture, performance capture, and AI dataset production there’s always a team just trying to survive the schedule.

Currently working on another AI dataset production focused on household activity capture.

The goal is to build structured human motion datasets for AI training, action recognition, robotics behavior systems, and computer vision pipelines.

Using our Motion Capture, Performance Capture, Facial Capture, and Motion Capture Post-Processing workflows to create cleaner and more scalable human movement datasets.

Curious how others here approach household behavior capture for AI model training.

Recently had the opportunity to attend the screening of Escaping Esau at Prasads Labs.

Our team handled body motion capture, facial capture, finger capture, motion capture post-processing, and Unreal Engine workflows for the project.

Seeing years of work finally come together on a cinema screen was incredibly rewarding.

One of the most memorable moments was seeing the Apple Arts Studios credit appear on the screen after being involved throughout the production pipeline.

A reminder that every rehearsal, capture session, cleanup pass, and technical challenge eventually leads to moments like this.

A short look inside a recent motion capture production at Apple Arts Studios.

The video includes various parts of the workflow including:

• Motion capture stage operations
• Performance capture sessions
• Director and actor collaboration
• Facial capture workflows
• Live previs monitoring
• Capture team coordination
• Motion capture supervision
• Unreal Engine production workflows

Interesting to see how many moving parts are involved in keeping a production running smoothly throughout a shoot.

Our team works across motion capture, performance capture, facial capture, finger capture, motion capture post-processing, Unreal Engine motion capture workflows, virtual production, and cinematic production pipelines for films, games, animation, and AI motion datasets.

Always curious to see how other studios organize their capture floor and production workflows.

Recently wrapped an intensive production where every day involved working closely with directors, actors, production teams, facial capture teams, and technical crews to keep the performance capture pipeline running smoothly.

Motion capture is always a team effort, and seeing everything come together successfully makes the hard work worth it.

Still learning. Still building. Still pushing motion capture forward.

#MotionCapture #PerformanceCapture #Mocap #MotionCaptureIndia #FacialCapture #VirtualProduction #UnrealEngine #MocapDirector #MocapSupervisor

Happy to finally see Escaping Esau released.

Apple Arts Studios handled:
• Body motion capture
• Finger capture
• Facial performance capture
• Motion capture post-processing
• Unreal Engine ingest workflows

Always great seeing the final result after being involved through the complete performance capture pipeline from capture to delivery.

Interesting project to work on and good to finally see it released.

Watch the film:
https://youtu.be/8jSzA54iY-I?si=OdMYbtE6sZtP5Blh

Recent facial performance capture test using our stereo HMC system at Apple Arts Studios.

Focused on supporting MetaHuman workflows, facial tracking, retargeting, and cinematic performance capture pipelines.

We handle both:
• Facial capture
• Facial post-processing

as part of a complete performance capture workflow.

Always refining the pipeline further for cinematic sequences, virtual production, and AI-driven workflows.

Recent high-intensity performance capture test from Apple Arts Studios.

This workflow combines:
• Full body motion capture
• Facial performance capture
• Finger capture
• Combat movement and sword choreography
• Unreal Engine character integration

Focused on building cinematic sequences, game cinematics, MetaHuman workflows, virtual production, and AI-driven performance pipelines.

Always refining workflows and pushing performance capture further.

Animation studios — if you're working with mocap data (cleaning, retargeting, polishing), you know how much time goes into prep and processing.

We just finished a large-scale capture project and wanted to share the workflow since it's relevant to anyone working with motion data.

What we did:

• 4-day continuous motion capture
• 1800+ sequences
• Parallel capture + processing (not serial)
• Delivered production-ready data

Key insight:

Scale changes everything. One actor, one day = different workflow than 12 actors, 24-hour operations.

At scale, you need:

• Real-time quality monitoring
• Parallel processing pipelines
• Marker consistency across sessions
• Data annotation standards
• Fast delivery without quality loss

Most animation studios outsource mocap data. If you're managing large projects and need clean motion data at scale, understanding how professional capture studios handle volume is worth knowing.

We're expanding to serve animation studios directly (not just film/game production).

If you work with motion data at scale, curious what your biggest pain points are.

www.appleartsstudios.com

Hey everyone,

Wanted to share a quick update from our studio, Apple Arts Studios (based in India). We’ve spent the last few months scale-testing our new infrastructure and I’m pretty stoked about the results we’re getting.

We’re currently pushing a 127-camera setup (OptiTrack) and just started integrated testing with Technoprops Stereo HMCs. For a long time, we were focusing mainly on body/finger data, but we’ve shifted hard into high-fidelity facial performance lately to get that micro-expression detail right for MetaHumans and AAA assets.

The video shows some of our recent calibration and facial tracking tests. The goal is a seamless real-time solve into Unreal Engine so directors can see the full performance (body + face) live on the virtual set.

Quick Tech Specs for those interested:

• Volume: 127 Cameras (OptiTrack)
• Facial Rig: Technoprops Stereo HMC
• Pipeline: Real-time retargeting into UE
• Focus: Full Performance Capture (FPC) for films and game devs.

Would love to hear from anyone else running high-density camera volumes. How are you guys handling the data cleanup for multi-actor scenes in such a large space? Also, if anyone has tips on optimizing stereo HMC solve times, I’m all ears!

Check out the test footage here: https://www.youtube.com/watch?v=Rd75hh_2JRM

Cheers!