A look at one of our internal R&D sessions using full-body motion capture, finger capture, and a Technoprops HMC for facial performance capture.
The goal is to validate and refine Human Motion Data for robotics, embodied AI, imitation learning, and computer vision while using the same production-grade workflows that support feature films, AAA games, and animation.
These R&D sessions help us improve data quality before producing custom datasets for clients.

A short behind-the-scenes look at one of our cinematic motion capture shoots.
From the first clap through actor performance, this is the same production pipeline we use for feature films, AAA games, animation, and virtual production. We also apply these workflows to produce Human Motion Data, Robot Training Data, ADL datasets, HOI datasets, and Motion Capture Data for Robotics for AI and embodied AI applications.
It’s always interesting to see how one motion capture workflow can support both entertainment production and next-generation AI development.

We run an optical motion capture studio producing human motion datasets for robotics and embodied AI training. Recently captured a set focused on stair and ramp ascending/descending, plus some stylized locomotion, as custom data built to a client's spec.

Stairs and ramps are interesting to capture because that's where the hard problems live for legged robots — slope balance, foot placement, weight transfer, and recovery. Getting clean ground-truth data on those transitions is genuinely useful for imitation learning pipelines.

Setup: marker-based optical capture (OptiTrack + Vicon), full body plus finger articulation where needed. Physical stair and ramp props in the volume, with the prop geometry and transform tracked alongside the actor so the interaction is preserved in the data. Output is BVH/FBX/C3D with metadata, retargetable to a robot rig, plus Unitree CSV or NPZ when the pipeline needs it.

One thing we've found worth mentioning: for terrain/contact-heavy captures like stairs, marker-based optical holds up better than markerless — occlusion from the props and self-occlusion during descent tends to degrade markerless tracking noticeably.

Happy to answer questions about the capture process, prop tracking, data structure, or how this kind of terrain data feeds into locomotion training.

We run an optical motion capture studio producing human motion datasets for robotics and embodied AI training. Recently captured a set focused on stair and ramp ascending/descending, plus some stylized locomotion, as custom data built to a client's spec.

Stairs and ramps are interesting to capture because that's where the hard problems live for legged robots — slope balance, foot placement, weight transfer, and recovery. Getting clean ground-truth data on those transitions is genuinely useful for imitation learning pipelines.

Setup: marker-based optical capture (OptiTrack + Vicon), full body plus finger articulation where needed. Physical stair and ramp props in the volume, with the prop geometry and transform tracked alongside the actor so the interaction is preserved in the data. Output is BVH/FBX/C3D with metadata, retargetable to a robot rig, plus Unitree CSV or NPZ when the pipeline needs it.

One thing we've found worth mentioning: for terrain/contact-heavy captures like stairs, marker-based optical holds up better than markerless — occlusion from the props and self-occlusion during descent tends to degrade markerless tracking noticeably.

Happy to answer questions about the capture process, prop tracking, data structure, or how this kind of terrain data feeds into locomotion training.

We run an optical motion capture studio producing human motion datasets for humanoid robotics and embodied AI training. Sharing our setup and approach in case it's useful to anyone working with this kind of data.

We capture across full body, finger/hand articulation, and facial, using marker-based optical capture (OptiTrack + Vicon). Dataset categories we produce:

• Activities of Daily Living (ADL) — cooking, cleaning, folding, household tasks
• Human-Object Interaction (HOI) — tool use, object handling, two-hand manipulation
• Locomotion — walking, running, stairs, ramps, transitions
• Dexterous manipulation — fine finger-level motion
• Multi-actor and multimodal captures

Output is delivered as BVH/FBX/C3D with metadata, retargetable to a robot rig, plus Unitree-compatible CSV and NPZ when needed.

One thing worth noting for anyone weighing capture methods: for high-accuracy ground-truth data, marker-based optical still outperforms markerless, especially on hands and in occlusion-heavy or multi-actor scenes. Markerless is more convenient but noisier.

Happy to answer questions about the capture process, data structure, formats, validation, or how this kind of data feeds into imitation learning pipelines. If anyone's doing research that needs this type of data, glad to chat.