Performance faciale : 3 conseils essentiels pour les protocoles de saisie des données

Lorsque vous concevez des protocoles de capture faciale, vous devez tenir compte d'un grand nombre d'éléments afin d'éviter la production de données de mauvaise qualité et la lassitude des participants.

Des facteurs tels que...

les poses que vous choisissez
l'enchaînement des poses
la façon dont vous expliquez ou montrez les poses, etc.

...font une énorme différence dans la façon dont vos sessions peuvent se dérouler.

Working with major game and tech companies to refine their facial performance capture pipelines, it’s quite obvious that people are recycling similar, dated protocols. Considering the present purpose of many captures, such old protocols are often clunky with illogical pose combinations, redundant expressions, and inefficient flow.

La liste des choses à faire et à ne pas faire en matière de capture faciale est sans fin, mais voici quelques conseils généraux :

1. Be use case-minded!

Are you defining machine learning training data for real-time applications? Are you gathering extreme poses for high-intensity fight scenes in film or games? Think about your end goal.

If your purpose is to capture facial data for an avatar product aimed toward co-working, you’d likely want to prioritize prosocial, collaborative, and natural facial expressions. Stop wasting time, energy, and production budget by bloating your session with every possible unsightly “scream” pose or hyper-compressed “lemon” face. Save those for Diablo et Planet of the Apes.

2. Design for logical flow.

Group similar expressions together, e.g. brow-based FACS poses (action units like – inner brow raiser, outer brow raiser, brow lowerer) and eye-based FACS poses (action units like – upper lid raiser, lid tightener, cheek raiser, eye closure, blink, wink) together.
Go from easy to difficult within each section. If you move from easy to difficult across ALL poses, you will end up forcing your user to leap from eyes to mouth to brows to jaw.
Grouping expressions strategically not only helps with user fatigue and comprehension, but it also opens up opportunity to order your poses in a way that allows you to describe and build off previous ones.

3. Make sure your example imagery and descriptions match the target pose and intended blendshapes.

Too many times, I see prompts like “raise brows without widening eyes” – yet the actor in the example shot is clearly widening their eyes. A large percentage of users will do as they see, not as they hear or read. So, don’t give conflicting instructions, and make sure you rigorously review the example poses!

📝 Encore un conseil: Dans la vidéo, vous remarquerez que j'invite les participants à relever le menton en leur disant "relevez votre lèvre inférieure". Il est facile de s'enfermer dans la description des poses en se basant sur leurs noms formels, mais en utilisant des descripteurs plus accessibles, vous pouvez augmenter la probabilité que votre participant prenne la pose.