Intel Frame Generation Technology For XeSS Could Be Coming Soon: ExtraSS With Frame Extrapolation To Boost Game FPS

17.12.2023 - 01:43 / wccftech.com

Intel could be the third major PC player to debut its frame generation technology for its XeSS framework known as ExtraSS as showcased during SIGGRAPH Asia 2023 in Sydney.

Intel's XeSS technology is an upscaling technology that is hardware agnostic, meaning that it works with GPUs from NVIDIA and AMD too. It is also often comparable to NVIDIA's DLSS upscaling tech & sits ahead of AMD's FSR. Like NVIDIA's DLSS, Intel's XeSS leverages AI accelerators to deliver better image quality than other upscaling methods and the results speak for themselves which can be seen in the several XeSS titles that have been released to date. XeSS is also an open-source tech which means that it can easily be integrated within games and almost all games that feature XeSS support other upscaling methods too such as FSR and DLSS.

However, NVIDIA's DLSS and AMD's FSR technologies have further been extended with support for frame generation, a method that inserts a frame using interpolation technology. This can be used to boost the FPS while maintaining good image quality. NVIDIA's Frame Generation techniques are getting updated with DLSS 3.5 further enhancing the image quality while AMD just released its new FSR 3.03 update which addresses frame pacing issues and also offers more fidelity than before.

This is one area where Intel XeSS has so far lacked but it looks like Intel has its solution in the works as an extension to XeSS known as ExtraSS. In the "ExtraSS: A Framework for Joint Spatial Super Sampling and Frame Extrapolation" presentation, Intel's VP of graphics research, Anton Kaplanyan, & various other authors propose the idea of frame extrapolation to be used for frame generation. The description of the presentation is listed below:

We introduce ExtraSS, a novel framework that combines spatial super sampling and frame extrapolation to enhance real-time rendering performance. By integrating these techniques, our approach achieves a balance between performance and quality, generating temporally stable and high-quality, high-resolution results.

Leveraging lightweight modules on warping and the ExtraSSNet for refinement, we exploit spatial-temporal information, improve rendering sharpness, handle moving shadings accurately, and generate temporally stable results. Computational costs are significantly reduced compared to traditional rendering methods, enabling higher frame rates and alias-free high resolution results.

Evaluation using Unreal Engine demonstrates the benefits of our framework over conventional individual spatial or temporal super sampling methods, delivering improved rendering speed and visual quality. With its ability to generate temporally stable high-quality results, our framework creates new possibilities for