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The real difference here is that our solving is not based on a non-exhaustive set of expressions anymore. We can totally imagine transferring this process onto a completely different rig, another character (human or not) in the future. “It is demonstrated right now for a use case in which you want to animate a digital double, an exact clone of the actor based on scans. “We are able to extract the rig knowledge from the Maya scene and use it to build a custom solver that will find out how the rig works from tracking data,” Barrielle added. This step depends on the rig logic and still needs to be tested and approved on a wide variety of rigs. To this end, the team developed a system that exports and reproduces the compute graph of the rig logic and duplicates all the nodes type into the Dynamixyz system in order to run simulations. The Dynamixyz R & D team pushed forward the proof of concept, thinking that the true usable outcome was to solve directly on the controllers of the rig, rather than solving on the scans. Solving Directly to the Rig Taking Advantage of its Logic
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Tracking from scanned data reduces the error-prone and time-consuming step of manual annotation and is far more accurate as it comes from the specific face information of the actor included in the scans. It also gives the opportunity to have a high volume of expressions.” “It brings higher precision as it has been generated with very high-quality scans. “With these images generated as if they were taken with a head mounted camera and geometry information, we were able to build a Performer tracking profile as if it had been annotated manually,” R & D engineer and author of the scan-based solver technology, Vincent Barrielle, explained. The tracking was processed automatically based on geometric and textures information, almost completely replacing the annotation step. The scanned data the Dynamixyz team received from PixelGun enabled them to recreate a synthetic double with illumination recovery and extract key poses from the digital double as if they were frames recorded with a head gear. Pixelgun delivered registered scanned data with textures covering 80 expressions taken with 63 cameras trained on the head for expression capture, and 145 cameras trained on the subject for body capture.Īs lighting conditions are key when training the tracking, manual annotation is still required for 2 to 3 frames extracted from the production shots to retrieve the illumination pattern. Scanned data provided information on geometry and textures (like wrinkles or other very peculiar face features) that enabled the retrieval of very accurate information for both morphology and appearance. The stereo head mounted camera and motion capture for the performance were conducted at the same time as the scanning session by Pixelgun Studio.
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The two companies joined forces to work on a scan-based facial mocap workflow. Visual Concepts, a Dynamixyz client, introduced them to Pixelgun Studio, which provides scans for the game, NBA2K, among other titles. Studios have developed pipelines including photogrammetry or 3D scans and can now handle the process and deliver clean data efficiently. While scanned data was very hard to collect just a few years ago, scanning technology has become more accessible and affordable. This step brings flexibility as the underlying machine learning process makes it possible to retarget to any character and any rig.ĭynamixyz’s R & D team has always focused on both reducing time spent on training the system and increasing the quality of the results to save animators time during production.ĭealing with massive amounts of scans through the years, it became obvious to the team that the data should be reused to assist in specializing the tracker to the actor’s morphology. While taking less time than building the tracking profile, building the retargeting profile is also done manually.
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Building the Retargeting Profile: This second step requires that the animator adjusts controllers in the 3D software to retarget those key poses to the dedicated character.Building the Tracking Profile: this first step consists in manually annotating key poses of an actor (from a video source) to give the software examples.Performer – Dynamixyz’s core markerless tracking software, relies on a specific tracking technology, based on machine learning, that requires two essential steps in its workflow: Dynamixyz and Pixelgun scan-based solving Demo from Dynamixyz on Vimeo.įrom Facial Mocap Specific Tracking Technology