![]() FIXED Players report black/grey screens in Outer Wilds with 522.FIXED AVS4You monochrome video preview.FIXED Shadowplay recordings may appear over exposed when Use HDR is enabled from the Windows display settings.FIXED Portal RTX hang during resolution/mode change and GFE recording.Introduces support for the GeForce RTX 4070 Ti.The January NVIDIA Studio Driver provides optimal support for the latest new creative applications and updates announced at CES including a new Eye Contact feature for NVIDIA Broadcast and a myriad of new functionality for NVIDIA Omniverse. The client has more modest requirements, but should have a GPU that supports hardware accelerated H264 decoding.This new Game Ready Driver provides the best day-0 gaming experience for the latest new games supporting NVIDIA DLSS 3 technology including Conqueror’s Blade and Dakar Desert Rally.In turn, the animation system can now produce stable controllers for physically simulated tennis players that can accurately hit the incoming ball to target positions using a diverse variety of strokes, including serves (forehands, and backhands), spins (top spins and slices), and various playing styles (one or two-handed backhands, left and right-handed play).NVIDIA Game Ready & Studio Driver 528.02 has been released To address the low-quality motions extracted from broadcast videos, the team implemented a motion correction system with physics-based imitation that overrides erroneous aspects of the learned motion. The group’s initial attempts generated noisy, unstable player movements, making it difficult to simulate life-like behaviors. Given the complexity of components involved in their system, training the simulated player models from real footage of tennis games proved to be a challenging task. ![]() The system leverages a hierarchical control model that combines a low-level imitation policy and high-level motion planning policy to guide character motions learned from broadcast videos. ![]() The animation system developed by Peng’s team allows for character movements to be procedurally generated by learning from footage of real athletes playing in sports games. While popular sports video games typically use motion capture technologies to produce high-quality animations, their results are limited to the behaviors captured during specific recording sessions, limiting the diversity of movements a character can perform.Īccording to new developments involving Peng and researchers from Stanford University, the University of Toronto, Vector Institute and NVIDIA, it may soon become possible for video game designers to have their characters learn to move by mimicking footage of real-life athletes, and automatically simulate new variations and responsive behaviors on the fly. It’s supported on any NVIDIA GeForce RTX, TITAN RTX or Quadro RTX GPU, using their dedicated Tensor Core AI processors to help the app’s AI networks run in real-time, right alongside your games. When provided with a large dataset of video clips from professional tennis players, the system learns to perform complex tennis shots and realistically chains together multiple shots into extended two-player rallies, generating long-lasting matches with realistic racket and ball dynamics between two physically simulated characters. NVIDIA Broadcast is a universal plugin that works with most popular live streaming, voice chat and video conferencing apps. In many instances, OBS is better than Nvidia Shadowplay, becouse it offers so much flexibility of options, but the options for Nvidia GPUs are limited. Peng’s team will present its system and corresponding research paper at the 50 th SIGGRAPH conference, the premier global conference for computer graphics and interactive techniques, in Los Angeles, California, from August 6-10. ![]() SFU computing science assistant professor Jason Peng is leading a research team that is raising motion simulation technology to the next level-and using the game of tennis to showcase just how real virtual athletes’ moves can be.Īlongside his colleagues, Peng has created a machine learning system capable of learning diverse, simulated tennis skills from broadcast video footage. ![]()
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