MACHINE LEARNING IN CONTENT CREATION
The use of machine and deep learning techniques in the creation of CG creatures and materials is still relatively new, but incredibly promising, which is why several companies have been dipping their toes in the area. Ziva Dynamics, which offers physically-based simulation software called Ziva VFX, has been exploring machine learning, particularly in relation to its real-time solver technology.
“This technology,” explains Ziva Dynamics co-CEO and co-founder James Jacobs, “makes it possible to convert high-quality offline simulations, crafted by technical directors using Ziva VFX, into performant real-time characters. We’ve deployed this tech in a few public demonstrations and engaged in confidential prototyping with several leading companies in different sectors to explore use cases and future product strategies.”
“The machine learning algorithms [enable artists] to interactively pose high-quality Ziva characters in real-time,” adds Jacobs. “The bakes produced from offline simulations are combined with representative animation data through a machine learning training process. From that, our solvers rapidly approximate the natural dynamics of the character for entirely new positions. This results in a fast, interactive character asset that achieves really consistent shapes, all in a relatively small file.”
Allegorithmic, which makes the Substance suite of 3D texturing and material creation tools, has also been exploring the field of A.I. to combine several material-related processes such as image recognition and color extraction, into a single tool, called Alchemist.
Alchemist’s A.I. capabilities are, in particular, powered by NVIDIA GPUs (NVIDIA itself is at the center of a great deal of computer graphics-related machine learning research). For one side of the Alchemist software, the delighter – which was created to help artists remove baked shadows from a base color or reference photo – a neural network was created from Substance’s material library to train the system. Artists need their images to be free of such shadows in order to get absolute control over the material. The A.I.-powered delighter detects the shadows, removes them, and reconstructs what is under the shadows.
In the motion-capture space, a number of companies are employing machine learning techniques to help make the process more efficient. DeepMotion, for instance, uses A.I. in several ways: to re-target and post-process motion-capture data; to simulate soft-body deformation in real time; to achieve 2D and 3D pose estimation; to train physicalized characters to synthesize dynamic motion in a simulation; and to stitch multiple motions together for seamless transitioning and blending.
“These applications of A.I. solve a variety of problems for accelerating VFX processes, enabling truly interactive character creation, and expanding pipelines for animation and simulation data,” says DeepMotion founder Kevin He. “Machine learning has been used for years to create interesting effects in physics-based animation and the media arts, but we’re seeing a new wave of applications as computations become more efficient and novel approaches, like deep reinforcement learning, create more scalable models.”
Meanwhile, RADiCAL is also utilizing A.I. in motion capture and, in particular, challenging the usual hardware-based approach to capture. “Specifically,” notes RADiCAL CEO Gavan Gravesen, “our solution uses input from conventional 2D video cameras to produce 3D animation that requires little to no cleanup, coding, investment or training.”
“To do that,” adds Gravesen, “we’re not relying on hardware-driven detections of tons of small data points that are aggregated into larger data sums that, after some intensive cleaning up, collectively resemble human activity. Rather, we deliver learning-based, software-driven reconstructions of human motion in 3D space.”
This content was originally published here.