Wild Capture's Cohort Fashion

PROJECT YEAR

2022

Technologies

Cohort Fashion

Wild Capture's Cohort Fashion technology for Digital Humans and Volumetric Video

With it's ongoing development and demonstration of its new Cohort Fashion for volumetric video, Cohort Fashion is being offered as part of Wild Capture's Digital Human Platform. Cohort Fashion applies CG Cloth to volumetric video assets as an interactive CG cloth with lifelike performance and physics. Digital dresses, pants, shoes and jewelry can all be utilized in 3D spaces and then modified for customization onto each digital character.
Cohort Fashion helps create next level volumetric solutions by creating CG fabric in 3D virtual spaces and solving the necessary volumetric character interaction to create lifelike realism. Also adding logos or designs for art direction and product placement and the ability to adjust cloth dimensions and attributes, your unique character models can be exported to your preferred simulation pipeline, like Maya, Houdini or Unreal. All digital fabric is created from traditional CG Cloth design tools, then applied and adjusted for collisions and real world physics.

Stream Fashion Assets on Your Web Browser

Stream Wild Capture's volumetric fashion demo straight to your mobile or desktop with a sharable URL. Pinch scroll and zoom your way around a 3D character to your own direction with the Universal Volumetric streaming web player "UVol" at your fingertips. This early test shows how Wild Capture can take a pre-existing alembic mesh input and a 2D pattern from Marvelous Designer or CLO3D and automatically pair it to the body for an automated result. Volumetric + CG Cloth automation. 2D video demos are below.

E-commerce Possibilities with Streaming Volumetric Video and Digital Fashion Tools

Wild Capture produced volumetric content and an ecommerce shopping tool with their UVol streaming volumetric media player. All of this for a range of models, all ranging in figure, to provide digital humans assets as well as the art & technical direction to get the CG Cloth designs onto the bodies. Implemented with a complete virtual environment for the demonstration final stages.
Wild Capture's Digital Fashion Toolkit creates CG Cloth in virtual spaces to solve the necessary volumetric character interaction that creates lifelike realism. Features to add logos or designs for art direction and product placement for branding activations are currently being used by Wild Capture's service clients. As is the ability to adjust cloth application, allowing for unique modeling effects for every volumetric character. Slow motions, for instance can be applied to models within the live physical demonstration. All digital fabric is created from traditional CG Cloth designs, then applied and adjusted for collisions and real world physics in the virtual world.

Cohort Fashion for New Media Productions

Wild Capture has a complete pipeline for high quality volumetric video, known as the Digital Human Platform. The data workflow for Cohort Fashion is now servicing new media activations in VFX, Gaming, and web streaming activations. and can be used inside virtual production pipelines with Dishuise and other virtual production technologies.
As the use of virtual production stages evolves, the need for customizable background characters will be essential to saving production time and money. Cohort Fashion achieves this with a lifelike digital human pipeline that uses USD to reduce render load with non destructive reference data to produce background crowds with minimal data needed to process and stream.

Digital Fashion for In-Person Events and Storefronts

Cohort Fashion helps create unique characters for physical installations and storefront displays and devices. New marketing opportunities are available with kiosks and digital marketing advertisements with digital humans and camera control built into your display. Leverage you messaging power and your ability to capture the audience's attention with lifelike characters engaging something that they have never seen before.

Original Publishing Date January 5th 2022

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