mlsanityData to 14 Oct 2026

GFFE: G-buffer Free Frame Extrapolation for Low-latency Real-time Rendering

arXiv 2406.18551 ↗First posted 23 May 2024cs.CV cs.GRcited by 1

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Abstract (arXiv)

Real-time rendering has been embracing ever-demanding effects, such as ray tracing. However, rendering such effects in high resolution and high frame rate remains challenging. Frame extrapolation methods, which don't introduce additional latency as opposed to frame interpolation methods such as DLSS 3 and FSR 3, boost the frame rate by generating future frames based on previous frames. However, it is a more challenging task because of the lack of information in the disocclusion regions, and recent methods also have a high engine integration cost due to requiring G-buffers as input. We propose a G-buffer free frame extrapolation, GFFE, with a novel heuristic framework and an efficient neural network, to plausibly generate new frames in real-time without introducing additional latency. We analyze the motion of dynamic fragments and different types of disocclusions, and design the corresponding modules of the extrapolation block to handle them. After filling disocclusions, a light-weight shading correction network is used to correct shading and improve overall quality. GFFE achieves comparable or better results compared to previous interpolation as well as G-buffer-dependent extrapolation methods, with more efficient performance and easier game integration.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 43 references that are in the map. First posted on arXiv 23 May 2024.

Builds on

9 of 43 references.

References · 9

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  3. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  4. 2019Depth-Aware Video Frame Interpolationcited by 234
  5. 2017UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Losscited by 202
  6. 2022FILM: Frame Interpolation for Large Motioncited by 153
  7. 2022IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolationcited by 109
  8. 2023A Dynamic Multi-Scale Voxel Flow Network for Video Predictioncited by 53
  9. 2021A Case Study of First Person Aiming at Low Latency for Esportscited by 1

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