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: https://onlinelibrary.wiley.com/doi/full/10.1111/cgf.13590

Fabric Engine and a Void in 3DCC Machine Learning

cg news

About three weeks ago Fabric Software abruptly ended the development of Fabric Engine without any following announcements. In this second post of a two-post series, I’ll try to go over why Fabric was a great fit for Machine Learning in the context of 3d Content Creation (animation, games and VFX). Bear in mind these are my own personal opinions.

AI, beyond the hype

Nowadays the acronym AI (artificial intelligence) seems to be everywhere. AI cars, AI stealing your job, processing AI in your GPU, and so on… Not unusually you see the buzzword AI alongside stuff that has no direct relation to artificial intelligence, like this tweet in which the WSJ uses VR goggles to depict AI.

WSJ twit on AI, with image of man using a VR headset

VR goggles used to depict the concept of AI (credit: WSJ, Twitter)

Most of the times when people talk about AI, they are really talking about a rapidly developing segment of AI called Machine Learning (ML). Machine Learning is a field in computer science that is interested in techniques that make it possible to program computers with data, instead of using explicit instructions. ML techniques are either based on a statistical approach or a connectionist approach. The connectionist approach is in vogue nowadays, especially a specific approach known as Deep Learning.

Gimme data

Data is what makes ML tick. This data can be of two kinds: labeled and unlabeled. Unlabeled data can be used to find patterns within the data itself. A very nice example of the use of ML with unlabeled data in a 3d content creation setting is the work of Loper et al. (2015). The authors used a dataset consisting of 2000 scanned human bodies (per gender) and, using a statistical technique called PCA, found that they could describe scanned bodies with less than 5% error using only 10 blendshapes. You can experiment with the results of this work in Maya, clicking here.

9 different virtual reproductions of the human body

The three principal components of the human shape (credit: Loper et al. 2015)