If you're on 1440p you're good with 3070. Whereas a 3080 has 2x the throughput of the 2080. Good way to think about it is that a 3070 is faster than 2080 Ti. In response to whether or not the gap in performance between the GeForce RTX 3070 and RTX 3080 will be proportional to their $200 difference in price, the company had this to say: The RTX 30-Series are incredibly powerful GPUs and certainly could be bottlenecked by a slow CPU, particularly at low resolution or quality settings, so when testing performance it’s best to use the fastest CPU you can get your hands on at 1440p or 4K resolution, with settings maxed in order to isolate GPU performance.
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When asked about the general performance of the new stack of cards, and more specifically whether or not older processors like the Intel Core i7-8700K would bottleneck the new RTX 30 Series cards, the company responded: Given how little hard performance data was actually presented during the company's keynote yesterday, many were eager to hit the forum and start slinging their most pressing concerns. 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).First up, there were the questions about how the cards perform. 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.
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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. 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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