Style Invariant Locomotion Classification for Character Control

publications, research and development

Abstract: We present a real‐time system for character control that relies on the classification of locomotive actions in skeletal motion capture data. Our method is both progress dependent and style invariant. Two deep neural networks are used to correlate body shape and implicit dynamics to locomotive types and their respective progress. In comparison to related work, our approach does not require a setup step and enables the user to act in a natural, unconstrained manner. Also, our method displays better performance than the related work in scenarios where the actor performs sharp changes in direction and highly stylized motions while maintaining at least as good performance in other scenarios. Our motivation is to enable character control of non‐bipedal characters in virtual production and live immersive experiences, where mannerisms in the actor’s performance may be an issue for previous methods.

Full paper:

Decoupling Expressiveness and Body-Mechanics in Human Motion


Presented at the 31st Conference on Graphics, Patterns and Images (SIBGRPI) – Awarded with an Honorable Mention

Summary: Modern motion capturing systems can accurately store human motion with high precision. Editing this kind of data is troublesome, due to the amount and complexity of data. In this paper, we present a method for decoupling the aspects of human motion that are strictly related to locomotion and balance, from other movements that may convey expressiveness and intentionality. We then demonstrate how this decoupling can be useful in creating variations of the original motion, or in mixing different actions together.

Full paper

What was and what was not Fabric Engine?

cg news

A couple of weeks ago Fabric Software abruptly ended the development of Fabric Engine without any following announcements. In this two-post series, I’ll try to go over what Fabric Engine was, the different positionings it assumed through the years, and what voids does it leave in the CG community. Bear in mind these are my own personal opinions.

Update, check the second post in this series: Fabric Engine and a Void in 3DCC Machine Learning.

In the beginning, there was KL

From the start, Fabric Engine was a “high-performance computation platform” ( it was not supposed to be a plug-in for Maya or other DCCs ( Anything one would want to compute in Fabric needed to be coded in its own special language called KL. KL was an object-oriented scripting language with JavaScript syntax. Its big plus over something like Python, for example, was its just in time compilation and parallel computing capabilities. So, it was fast.


Fabric Engine’s main components

However, why was it any good for CG folks?