Games, Movies & Media Applications

Accurate motion capture for 3D Character actors still requires tracking markers or physical augmentation–and even then, they aren’t perfect from one session to the next. With precise landmarks in 3D space for every image, our datasets assist in capturing actors’ performances with higher accuracy; even for landmarks that would normally be occluded.

Domain Adapt
As with all synthetic data, there’s a shift from our domain to the one captured by real cameras. Although there’s no universal domain adaptation approach for every use-case, we stand on the shoulders of giants to get great results.
Adaptive Batch Normalization
Adaptive Batch Normalization is a simple technique, can be easily applied to any network with batch normalization layers, and combined with all other techniques for surprisingly good results.
Adversarial Domain Adaptation
Adversarial domain adaptation and its modifications for particular tasks usually result in strong improvement. The downside is that it typically requires heavy pipeline modifications.
Image-2-image translation methods coupled with self-regularization loss allows dataset-level refinement. While these methods require additional pipeline to train, it is completely independent and does not require modifications of the main training pipeline.
Combined methods
For the best results all the methods above typically should be combined together.
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Scales out of the box

Our technology seamlessly scales in the cloud with our customers’ demands, from R&D phases with small amounts of data to production requirements of terabytes of data.rnrnWith everything available via an API, we integrate seamlessly with your workflows from day 1.

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Machine Learning Development Support

If your team needs a little more machine learning muscle, our experts are ready to jump in. We’ll help reduce your time to market, so don’t hesitate to reach out.

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Synthesis AI speaking at the MetaBeat conference on Oct 4th