A Survey of Temporal Antialiasing Techniques: presentation notes

At Eurographics 2020 virtual conference, Lei Yang did a presentation of the Survey of Temporal Antialiasing Techniques report which included a good overview of TAA and temporal upsampling, its issues and future research.

I have taken some notes while watching it and I am sharing them here in case anyone finds them useful.


  • TAA is the defacto antialiasing technique
  • suitable for deferred renderers, replacing MSAA which is expensive with such architectures
  • it is like supersampling: multiple samples per pixel but spreading the samples across time instead of all in one frame
  • Only current frame sample can be trusted. Others may be occluded/dis-occluded/different lighting etc.
  • Recursive process: the output of the previous frame (history buffer) feeds into the current frame
    • history buffer sample is re-projected into the current frame to compensate for scene motion
    • renderer supplies per pixel motion vectors
    • reprojected samples are validated/rectified using samples from the current frame.
    • accumulate new samples into history buffer
  • a subpixel jitter offset is applied to projection matrix
  • needs a low discrepancy sequence (Halton)
  • We usually store a single colour in the history buffer to save space
  • We use an exponential filter to combine new sample into history buffer (1-a) * history_colour + a * new_colour
    • corresponds to a weighted sum of samples with smaller weights assigned to older samples
    • We typically use a small alpha value to get even weights over previous samples
    • a fixed alpha can reduce quality of antialiasing though, adaptive alpha (eg progressively decreasing from the harmonic series) can improve this.
  • Reprojection takes care of moving objects/camera
    • bilinear or bicubic filtering can be used to reconstruct the pixel colours
    • reprojected history colour can be wrong (occlusion, dissocclusion, lighting changes, wrong motion vectors)
    • We need to rejected or rectify it
    • Validation can be done comparing depth, normal, object/prim ID, colour
    • If invalid we can reject of fade out history colour setting alpha close to 1.
    • Rectification makes history colour more consistent with new colour samples
    • Compare it with pixels in 3×3 neighbourhood in the new colour buffer and use clipping or clamping against the neighbourhood colour AABB.
    • Variance clipping (fit AABB around mean and variance of the neighbourhood) avoids outlier colours
  • TAA is used for upsampling as well
    • Use a history buffer resolution higher than the rendered image resolution
    • has advantage over spatial upsampling techniques (more information)
    • Bins temporal samples to a higher resolution grid
  • Scaling-aware sample accumulation
    • Step 1: Upscale current frame samples to higher resolution with spatial interpolation. Produces blurry image.
    • Step 2: Blur the image with history buffer (already at higher resolution). We need adaptive blending based on sample location. Can use blurring kernel instead of binary decision.
  • Checkerboard rendering is a form of temporal upsampling. Fixed 1:2 upsampling rate, uses MSAA or target independent rasterisation — more complicated to implement.


  • Bluriness. Two main reasons:
    • History resampling due to reprojection. Quality improves with more expensive filters
    • History clipping/clamping. Can incorrectly removed detailed features in history [introducing flickering]. More pronounced with temporal upsampling.
    • Sharpening is often used to reduce bluriness
  • Ghosting
    • incorrect history clamping
    • often visible on disocclusion of highly detailed (contrast) background. A high contrast bg causes the clamping AABB to bloat and becomes ineffective in removing invalid history
  • Temporal instability and Moire
    • Occurs when frequency of a feature and the sampling frequency are correlated
    • Jittered position cause alternate values and flickering
    • History clamping exposes the flickering result
  • Undersampling artifacts
    • Newly disoccluded regions with not enough samples in the history buffer
    • Appears overly sharp/aliased or contain unstable noise
    • Can be improved with spatial AA techniques
  • Inflexible history rectifiction techniques prevent us from getting higher quality images.

Future research

  • Use machine learning to replace heuristics (DLSS 2.0)
  • Can produce more detailed results

Paper covers more TAA related topics (HDR and colour space, performance, variable rate shading, temporal denoising)

Questions from audience

  • What colour spaces can we use for rectification?
    • Any would do, some people use in Ycocg or YUV that produce tighter AABBs
    • Still not ideal, colour clamping can be problematic in areas of high contrast (large AABBs), leaking colours from previous frames.
  • How do HDR colour spaces affect TAA?
    • we want to do TAA after HDR resolve
    • postprocessing happens in HDR and sometimes need TAA beforehand
    • workaround is to do fake (reversible) tonemap, do TAA and then reverse it before any further postprocessing with antialiased result. Can reduce effectiveness of TAA sometimes.
  • Will DLSS replace TAA?
    • today it can be a replacement for TAA + it offers upsampling
  • Can maintaining a history of the AABBs can maybe help solve the flickering problem?
    • potentially but will also increase the amount of data that need reprojecting every frame.
  • Any new info about DLSS 2.0 – no plans for further publications
A Survey of Temporal Antialiasing Techniques: presentation notes

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