Hybrid MBlur: A Systematic Approach to Augment Rasterization with

Ray Tracing for Rendering Motion Blur in Games

Yu Wei Tan

, Xiaohan Cui

and Anand Bhojan

School of Computing, National University of Singapore, Singapore

Keywords:

Real-time, Motion Blur, Ray Tracing, Post-processing, Hybrid Rendering, Games.

Abstract:

Motion blur is commonly used in game cinematics to achieve photorealism by modelling the behaviour of

the camera shutter and simulating its effect associated with the relative motion of scene objects. A common

real-time post-process approach is spatial sampling, where the directional blur of a moving object is rendered

by integrating its colour based on velocity information within a single frame. However, such screen space

approaches typically cannot produce accurate partial occlusion semi-transparencies. Our real-time hybrid ren-

dering technique leverages hardware-accelerated ray tracing to correct post-process partial occlusion artifacts

by advancing rays recursively into the scene to retrieve background information for motion-blurred regions,

with reasonable additional performance cost for rendering game contents. We extend our previous work with

details on the design, implementation, and future work of the technique as well as performance comparisons

with post-processing.

1 INTRODUCTION

We showcase a novel real-time hybrid rendering tech-

nique for the Motion Blur (MBlur) effect that com-

bines ray tracing and post-processing, exploiting ray-

traced information within a selection mask to reduce

partial occlusion artifacts in post-processed MBlur.

We provide a thorough illustration and evaluation of

hybrid MBlur, extending our published work (Tan

et al., 2020b) with the following additions:

• Extensive discussion of the design and implemen-

tation details of hybrid MBlur.

• Visual quality and performance evaluations of our

technique in comparison to post-processing.

• Explanation of presented extensions and future

work to the approach.

1.1 Background Information

1.1.1 Hybrid Rendering

Ray tracing is a common approach to produce realis-

tic effects like glossy reﬂections, depth of ﬁeld, and

https://orcid.org/0000-0002-7972-2828

https://orcid.org/0000-0002-6152-1665

https://orcid.org/0000-0001-8105-1739

motion blur. However, its high computation time has

limited its use mostly to ofﬂine rendering. On the

other hand, many post-process techniques have been

devised to approximate these effects on rasterized im-

ages to meet the constraint of interactive frame rates

for games, albeit with limited visual quality.

Given recent developments in hardware accelera-

tion, it is now the time to marry ray tracing with ras-

terization for games. Ray tracing can be employed

to correct certain visual ﬂaws originating from pure

post-process approaches, while still adhering to the

performance budgets of real-time rendering. This re-

sults in more realistic and convincing graphics while

keeping the gaming experience interactive, enhancing

the overall immersion of the player in the game.

1.1.2 MBlur

MBlur is the streaking or smearing effect of objects

in the direction of relative motion with respect to the

camera. It is employed in cinematics to emphasize the

relative speed of moving objects, producing a blurring

effect as shown in Figure 1. The MBlur effect has

been used to express the speed of rapid movement, the

disoriented state of mind of a character, or the dream-

like quality of a scene.

In cameras, MBlur is produced with the choice of

exposure time or shutter speed, which is the duration

Tan, Y., Cui, X. and Bhojan, A.

Hybrid MBlur: A Systematic Approach to Augment Rasterization with Ray Tracing for Rendering Motion Blur in Games.

DOI: 10.5220/0010984700003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 1: GRAPP, pages

115-125

ISBN: 978-989-758-555-5; ISSN: 2184-4321

115

Figure 1: MBlur effect. Image from authors.

for which the camera shutter is open per second. This

duration directly correlates to the amount of time the

camera sensor is exposed to light. Hence, when the

exposure time is long, every resulting image does not

represent an instantaneous moment but rather, an in-

terval consisting of successive moments in time. An

object moving in relation to the camera will therefore

appear on multiple areas of the sensor over time, re-

sulting in a directional blur effect of the object corre-

sponding to its motion.

As described in Jimenez (2014), moving objects

blur both outwards and inwards at their silhouettes,

making the region around their silhouettes appear

semi-transparent. Outer blur represents an object’s

blur into its neighbouring background while inner

blur applies to the blur produced within the silhouette

of the object itself. According to Cook et al. (1984),

accurate motion blurring takes into account areas of

background geometry occluded by any blurred fore-

ground. Hence, realistic MBlur should have accu-

rate partial occlusion by recovering true background

colour in inner blur regions instead of approximat-

ing this colour like many post-process approaches.

Background colour recovery in MBlur also helps to

prevent inaccuracies between real and approximated

backgrounds for sharp and blurred regions respec-

tively. Hence, we employ ray tracing in our approach

to retrieve the exact colour of occluded background.

2 RELATED WORK

2.1 Hybrid Rendering

The use of ray tracing to complement rasterization

techniques to achieve high graphics quality while

maintaining interactive frame rates has been at-

tempted for several visual effects over the years. In

Beck et al. (1981) and Hertel et al. (2009), shadow

mapping is employed to mark shadow boundaries

and reduce the actual number of shadow rays to be

traced, of which only rays from regions deemed po-

tentially inaccurate by shadow mapping are traced

in Lauterbach and Manocha (2009) to generate pre-

cise and alias-free shadows. For realistic reﬂections,

Macedo et al. (2018) invokes ray tracing only on pix-

els with reﬂections that cannot be solved with just

screen space information. In Marrs et al. (2018), tra-

ditional temporal anti-aliasing (TAA) is also extended

with ray-traced supersampling for regions with a high

chance of TAA failure.

In Cabeleira (2010), diffuse illumination is com-

puted via rasterization while reﬂections and refrac-

tions are handled by ray tracing. In more general

pipeline approaches, Chen and Liu (2007) substitutes

the generation of primary rays in recursive ray tracing

(Whitted, 1979) with rasterization for improved per-

formance. This approach is extended in Andrade et al.

(2014) which adheres to a render time limit by tracing

rays only for objects of the highest importance.

2.2 MBlur

Current implementations of MBlur include the use of

an accumulation buffer. Within the exposure time

when the camera shutter is set to be open, a series

of discrete snapshots rendered at multiple time offsets

from the actual time value of the image is integrated to

produce MBlur such as in Korein and Badler (1983)

and Haeberli and Akeley (1990). However, as dis-

cussed in McGuire et al. (2012), this approach is com-

putationally expensive in terms of shading time. Fur-

thermore, in the case of undersampling, this method

can lead to a series of disjoint ghosting artifacts in the

direction of motion instead of a continuous blur.

Stochastic sampling (Cook, 1986) for MBlur is

a Monte Carlo technique based on Halton (1970)

in which sampling is performed in a randomized

and nonuniform manner along the time dimension.

McGuire et al. (2010) incorporates stochastic sam-

pling with ray tracing by conservatively estimating

the total convex hull bound for the area of each

moving triangle during the exposure time, leverag-

ing hardware rasterization. Stochastically distributed

rays are then shot into the scene within this bound for

shading. This approach makes use of distributed ray

tracing (Cook et al., 1984), where rays are distributed

in time and the colour at each hit point is averaged to

produce the ﬁnal image.

Cook et al. (1987) translates micropolygons for

each sample according to a jittered time. On the

other hand, Fatahalian et al. (2009) and Sattlecker and

Steinberger (2015) achieve efﬁciency in performance

by extending the bounding box of each micropolygon

to consider its pixels and their associated velocities in-

GRAPP 2022 - 17th International Conference on Computer Graphics Theory and Applications

116

stead. Hou et al. (2010) is an approach that makes use

of 4D hyper-trapezoids to perform micropolygon ray

tracing. Methods of approximating the visibility func-

tion to sample have also been devised such as in Sung

et al. (2002). However, as explained in Sattlecker and

Steinberger (2015), at low sample rates, these meth-

ods will also exhibit ghosting artifacts whereas in-

creasing the number of samples would lead to noise.

To produce the right amount of blur, some real-

time approaches like Rosado (2007), Ritchie et al.

(2010) and Sousa (2013) make use of per-pixel ve-

locity information by accumulating samples along the

magnitude and direction of velocities in the colour

buffer. Other techniques in Korein and Badler (1983),

Catmull (1984) and Choi and Oh (2017) accumulate

the colours of visible passing geometry or pixels with

respect to a particular screen space position while

Gribel et al. (2011) makes use of screen space line

samples instead. Potmesil and Chakravarty (1983)

represents the relationship between objects and their

corresponding image points as point-spread functions

(PSFs), which are then used to convolve points in

motion. Leimk

uhler et al. (2018) splats the PSF of

every pixel in an accelerated fashion using sparse

representations of their Laplacians instead. Time-

dependent edge equations, as explained in Akenine-

oller et al. (2007) and Gribel et al. (2010), and 4D

polyhedra primitives (Grant, 1985) have also been

used for MBlur geometry processing. Recently, a

shading rate-based approach involving content and

motion-adaptive shading in Yang et al. (2019) has also

been developed for the generation of MBlur.

In particular, attempts to simulate nonlinear

MBlur include Gribel et al. (2013) and Woop et al.

(2017). Our hybrid technique only considers linear

inter-frame image space motion for now, but we in-

tend to provide support for higher-order geometry

motion in the future. We also assume mainstream

ray tracing acceleration architecture widely available

in modern gaming workstations. The GA10x RT

Core of the newest NVIDIA Ampere architecture

provides hardware acceleration for ray-traced motion

blur (NVIDIA Corporation, 2021) but is only found in

the premium GeForce RTX 30 Series graphics cards.

3 DESIGN

Our hybrid MBlur approach, as illustrated in Fig-

ure 2, compensates for missing information in post-

processed MBlur with the revealed background pro-

duced by a ray trace-based technique.

A Geometry Buffer (G-Buffer) is ﬁrst gener-

ated under a deferred shading set-up, rendering tex-

tures containing per-pixel information such as cam-

era space depth, screen space velocity and rasterized

colour. The same depth, velocity and colour infor-

mation for background geometry is produced by our

novel ray reveal pass within a ray mask for pixels in

the inner blur of moving foreground objects. A tile-

dilate pass is then applied to these 2 sets of buffers

to determine the sampling range of our gathering ﬁl-

ter in the subsequent post-process pass. Both the ray-

revealed and rasterized output are blurred by this post-

process pass and lastly composited together.

3.1 Post-process

The McGuire et al. (2012) post-process MBlur ren-

ders each pixel by gathering sample contributions

from a heuristic range of nearby pixels. We adapt

this approach to produce a motion-blurred effect sep-

arately for rasterized and ray-revealed information.

As suggested in Rosado (2007), motion vectors

are given by ﬁrst calculating per-pixel world space

positional differences between every frame and its

previous frame, followed by a translation to screen

space. By using the motion vector between the last

frame and the current frame, we simulate the expo-

sure time of one frame. Then, we follow the approach

of McGuire et al. (2012) in calculating the displace-

ment of the pixel within the exposure time by scal-

ing the inter-frame motion vector with the frame rate

of the previous frame as well as the exposure time.

Considering the full exposure as one unit of time,

this displacement can be interpreted as a per-exposure

velocity vector. This approach uses the assumption

that the motion vector of each pixel between the pre-

vious frame and the current frame remains constant

throughout the exposure time.

Jimenez (2014) is a technique that is based on

McGuire et al. (2012). As described in Jimenez

(2014), the major problems to be addressed when

producing a post-processed MBlur are the range of

sampling, the amount of contribution of each sam-

ple as well as the recovery of background geometry

information for inner blur. With our method for cal-

culating per-exposure velocities, we adopt McGuire

et al. (2012)’s approach in determining the magni-

tude of the sampling range and representing different

amounts of sample contribution, as illustrated in the

Appendix for completeness. As shown in Figure 3,

McGuire et al. (2012) centers the sampling area at

the target pixel, creating a blur effect both outwards

and inwards from the edge of the object. Although

this produces a more uniform blur for thin objects and

the specular highlights of curved surfaces, it poses the

problem of having to smoothen the transition between

Hybrid MBlur: A Systematic Approach to Augment Rasterization with Ray Tracing for Rendering Motion Blur in Games

117

Figure 2: Hybrid MBlur rendering pipeline.

the inner and outer blur. Hence, we let the pixel be

at the end of the sampling area instead, so inner and

outer blur can be considered separately without much

change to the pipeline. We also enhance the inner blur

with ray-revealed background information.

3.2 Ray Reveal

McGuire et al. (2012) approximates the inner blur

region of objects with colour information of nearby

samples. This is based on the reasoning that approx-

GRAPP 2022 - 17th International Conference on Computer Graphics Theory and Applications

118

Figure 3: Range of samples for each pixel.

imate background information is still better than the

absence of any background. With our ray reveal algo-

rithm, we ray trace to obtain true background infor-

mation for the inner blur.

3.2.1 Ray Mask

We adopt selective rendering (Chalmers et al., 2006)

in generating a ray mask that determines regions of

inner blur corresponding to geometry edges and only

shoot rays within the mask for better performance like

in adaptive frameless rendering (Dayal et al., 2005).

For every pixel with a nonzero speed, we ﬁrst

translate it by its velocity for one magnitude of its

estimated displacement within the exposure time to

predict its next screen space position at the end of

the exposure duration and compare their mesh ID and

depth. A target pixel is ﬁltered out of the mask if the

following conditions are not met:

6= m

(1)

− z

> SOFT Z EXTENT (2)

and m

refer to the corresponding mesh ID of

the pixel’s next and current positions respectively. We

assume that every object has uniform speed through-

out its geometry, so we reject the case when the m

and m

are equal. z

and z

refer to the camera space

depth of the pixel’s next and current position respec-

tively. For a target pixel to be in the inner blur region

of a foreground object, it would have to be shallower

than its next position. Hence, z

has to be deeper

than z

by a value greater than SOFT Z EXTENT, a

scene-dependent variable with a positive value as in-

troduced in the McGuire et al. (2012) paper for calcu-

lating sample contribution.

For pixels that are not ﬁltered out, they are passed

through a 5 × 5 Sobel convolution kernel which es-

timates how extreme an edge is. The kernel is ap-

plied to the G-Buffer to obtain an approximate deriva-

tive of the gradient associated with each pixel, based

on the depth and surface normal information of its

vicinity readily accessible from the deferred shading

stage. The depth derivatives identify the divide be-

tween overlapping objects where the colour of one

object might be revealed through the other, while the

normal derivatives can locate pronounced differences

in the orientation of primitive faces within objects

themselves near their silhouettes. The output of each

pixel from this ﬁlter is hence computed as follows:

x = δ

+ δ

(3)

= saturate(1 −

x + 1

) (4)

Here, δ

and δ

refer to the depth derivative and

length of normal derivatives respectively from the So-

bel operator. The normalized x, x

, is subsequently

evaluated against an edge threshold e, which is set

to be high in order to effectively eliminate non-edges

from the mask. Only pixels that pass the threshold

test will be marked in the edge mask.

Lastly, a range check pass is applied. Each pixel

with a nonzero speed then samples along the direction

of its velocity. If any of its samples is marked in the

edge mask, the pixel passes into the ﬁnal ray mask.

3.2.2 Recursive Ray Tracing

To obtain more accurate information behind fore-

ground objects for compositing inner blur, our ray

tracing approach, as illustrated in Figure 4, adapts the

idea of recursive ray tracing (Whitted, 1979) where

we shoot rays and iteratively advance them deeper

into the scene until a different object is found. At the

end of the recursion, the occluded background is then

revealed with the ﬁnal hit point. Revealing more lay-

ers deeper into the scene with this method increases

the accuracy of our motion-blurred background but

at diminishing returns. Hence, we limit the ray re-

veal process to only one background layer and post-

process (i.e. approximate using neighbour informa-

tion) it instead.

For every pixel marked in the ray mask, we shoot

a ray from the camera into its world space position

and immediately spawn a second ray along the same

direction after the ﬁrst hit. Currently, we use a sim-

ple indicator, the difference in luminance, to identify

different objects. Hence, we terminate the recursion

when the latest hit point reads a luminance different

from the ﬁrst hit or the maximum number of recur-

sions is reached.

4 IMPLEMENTATION

To implement our hybrid MBlur approach, we made

use of the NVIDIA Falcor real-time rendering frame-

work (Benty et al., 2020) on DirectX 12 for ray trac-

ing acceleration. The scenes used for testing our ap-

proach are THE MODERN LIVING ROOM (commonly

Hybrid MBlur: A Systematic Approach to Augment Rasterization with Ray Tracing for Rendering Motion Blur in Games

119

Figure 4: Ray tracing approach.

referred to as PINK ROOM) (Wig42, 2014), UE4 SUN

TEMPLE (Epic Games, 2017) and the interior scene

of AMAZON LUMBERYARD BISTRO (BISTRO INTE-

RIOR) (Amazon Lumberyard, 2017).

For our post-process tile-dilate pass, we followed

McGuire et al. (2012)’s approach of taking a neigh-

bourhood size of n = 3 but found that a tile size of

m = 40 generally worked better than m = 20 for us in

our test scenes with fast-moving foreground objects.

This is because the tile length scales the amount of

blur we can observe in the ﬁnal image, and we re-

quire a substantial amount of blur to expose and in-

spect the quality of semi-transparencies of foreground

silhouettes through which background geometry is re-

vealed. Hence, we also tested our scenes at long ex-

posure times or low shutter speeds of 1/60 s, against

high frame rates of 100 to 300 fps so as to scale our

per-exposure velocities effectively. As for our sample

count, we took 15 samples along the magnitude of the

dominant neighbourhood velocity as recommended in

McGuire et al. (2012). Our SOFT Z EXTENT was

3 cm (PINK ROOM and SUN TEMPLE) and 5 mm

(BISTRO INTERIOR), which are also within the sug-

gested range of values from McGuire et al. (2012).

As for our ray mask, we employed a strict edge

threshold e of 0.9 (PINK ROOM), 0.98 (SUN TEM-

PLE) and 0.96 (BISTRO INTERIOR) which were effec-

tive in detecting the intricate edges of scene objects

while rejecting non-edges well. For the ray tracing

pass, we limited the recursion level to 5. However,

for complex scenes with concave objects that lead to

a signiﬁcant amount of self-occlusion, this level can

be increased.

When the target pixel is deeper than its sample by

at least SOFT Z EXTENT in the post-process pass,

we artiﬁcially magnify w

by 30 for a smooth colour

transition from the object’s edge to its outer blur. On

the other hand, we deem the pixel to be in the in-

ner blur region based on whether it is marked in the

ray mask. If the pixel is in the mask, we also op-

tionally magnify its overall background colour weight

(w in background colour) and diminish its foreground

colour weight (w in foreground colour) accordingly

by the same magnitude of 3, so background geometry

can be observed more clearly through the silhouette

of foreground objects with partial occlusion.

5 RESULTS

Our hybrid MBlur approach (d) aims to improve

the visual quality of the post-process technique in

McGuire et al. (2012) with reasonable additional per-

formance cost. Besides the original rasterized colour

(a) and an adapted version of the post-process tech-

nique at the new sampling range as shown in Fig-

ure 3 (b), we also evaluate our approach against

state-of-the-art post-process MBlur from Unreal En-

gine 4 (UE4) (c), which adopts a similar technique

to McGuire et al. (2012). UE4 also produces MBlur

by comparing the velocity of each pixel to that of its

neighbours (Epic Games, 2020). The ground truth

is given by the distributed ray tracing approach from

Cook et al. (1984) (e). Our demo video (link) pro-

vides more detailed comparisons of the results.

5.1 Graphics Quality Comparison

Post-process MBlur inaccurately reconstructs the

background by reusing neighbouring information

available in the initial sharp rasterized image, which

creates artifacts such as mismatched patterns when

the background contributing to the inner blur is not

of a uniform colour. In Figure 5, we choose a shot

GRAPP 2022 - 17th International Conference on Computer Graphics Theory and Applications

120

(a) (b) (c) (d) (e)

Figure 5: Comparison of partial occlusion quality.

from PINK ROOM with a moving red vase in front of

a white sofa with a pink cushion on it. For the adapted

post-process MBlur from McGuire et al. (2012), the

silhouette of the cushion incorrectly terminates at the

edge of the vase. While the pink colour from the

cushion should extend towards the bottom right di-

rection into the geometry of the vase, it is blocked by

a horizontal division created by motion vector-based

sampling. This artifact is more noticeable for UE4,

where the background colour within the inner blur

is practically a linear translation from the left of the

edge, as shown in the yellow circle. However, our

hybrid method samples what is behind the inner blur

of moving objects, hence we can obtain the desired

background, which in this case is the extension of the

cushion silhouette in the green circle corresponding

to the ground truth.

However, it can be seen that hybrid MBlur does

not achieve as much semi-transparency as compared

to the ground truth, where the background colour

is more visible within the silhouette of the vase.

Nonetheless, the rendering time of the ground truth

is 13 fps at 200 rays per pixel (rpp), which is a lot

slower than 205 fps for hybrid MBlur. In practice, it

is not common to use distributed ray tracing to ren-

der MBlur in real-time as it is difﬁcult to meet inter-

active frame rates, while reducing the rpp to achieve

ideal performance will introduce a signiﬁcant amount

of sampling noise.

Against the ground truth, our hybrid MBlur

method performs well in objective visual metrics

PSNR and SSIM when compared to other state-of-

the-art post-process methods, as presented in Table 1.

It is also interesting to note that the improved MBlur

method implemented in the latest version of UE4 does

not fare as well as the basic adapted post-process

method from McGuire et al. (2012).

Table 1: Comparison of similarity to ground truth.

PSNR SSIM

Adapted Post-Process 24.20 0.91

UE4 Post-Process 12.32 0.68

Hybrid MBlur 25.69 0.92

5.2 Performance

Although the Falcor framework helped facilitate the

implementation of our technique by taking care of

scene loading, pipeline set-up and the creation of

ray tracing acceleration structures, the abstraction of

many low-level details from Direct3D makes it dif-

ﬁcult to optimize rendering. However, even without

substantial optimization, we have managed to achieve

relatively interactive frame rates and pass durations,

as shown in Table 2 based on shots of varying ge-

ometric complexity in Figure 6. Our measurements

are taken with the Falcor proﬁling tool on an Intel

Core i7-8700K CPU at 16GB RAM with an NVIDIA

GeForce RTX 2080 Ti GPU.

Table 2: Pass durations (in ms) and frame rates.

Shot Processor PR ST BI

CPU 0.26 0.54 1.74

G-Buffer

GPU 1.19 3.54 6.43

CPU 0.05 0.05 0.06

Ray Mask

GPU 0.33 0.38 0.33

CPU 1.13 2.27 2.99

Ray Trace

GPU 0.59 0.65 0.62

CPU 0.05 0.06 0.08

Tile-Dilate

GPU 1.48 1.44 1.43

CPU 0.03 0.04 0.04

PP-Composite

GPU 0.85 0.82 0.82

CPU 3.14 4.67 3.48

Others

GPU 0.39 0.42 0.29

CPU 4.66 7.63 8.39

Total Duration

GPU 4.83 7.25 9.92

Frame Rate 205 148 112

Hybrid MBlur, which contains an additional ray

reveal process, is expectedly slower than the adapted

post-process McGuire et al. (2012) approach that has

a frame rate of 295 fps for PR. As seen in Table 2,

the creation of the ray mask and the ray trace pass

are the major components of the ray reveal algorithm.

The ray trace pass increases in duration on the CPU

with the geometric complexity of each shot. As such,

it is expected that the G-Buffer pass aligns with this

Hybrid MBlur: A Systematic Approach to Augment Rasterization with Ray Tracing for Rendering Motion Blur in Games

121

(a) PINK ROOM (PR) (b) SUN TEMPLE (ST) (c) BISTRO INTERIOR (BI)

Figure 6: Shots used for proﬁling.

trend as it is where deferred shading is carried out.

However, the durations of the other passes appear to

be independent of this geometric complexity.

5.3 Limitations and Future Work

Since our work is focused on the main idea of real-

time hybrid MBlur, some common spatial sampling

constraints in MBlur are used for simplicity con-

siderations. These constraints include linear inter-

frame motion and stable lighting within the expo-

sure time, as well as moderate screen space veloc-

ity for a reasonable tile size. However, it is possible

to accommodate nonlinear MBlur at low cost with a

curve-sampling scatter approach, as demonstrated in

Guertin and Nowrouzezahrai (2015).

Additionally, the ideal indicator for termination in

our ray reveal algorithm should be the difference in

velocity and not luminance. However, it is inefﬁcient

to check the velocity of a hit point during the ray

tracing process. Hence, as a future improvement to

our approach, we hope to incorporate GeometryIndex

from DirectX Raytracing Tier 1.1, which will enable

the ray tracing shader to distinguish geometries. This

will be more suitable than luminance in scenes with

multiple overlapping moving objects of the same lu-

minance value.

Moreover, it is possible to improve the efﬁciency

of our pipeline by restricting the computation of ve-

locities to a user-deﬁned depth for the scene, if ge-

ometry movement is localized. We also intend to

integrate hybrid MBlur with other real-time hybrid

rendering effects, such as depth of ﬁeld (Tan et al.,

2020a) where we mitigate similar problems of partial

occlusion from post-processing approaches.

6 CONCLUSION

We present a real-time hybrid motion blur rendering

technique that produces more accurate partial occlu-

sion semi-transparencies on the silhouettes of moving

foreground geometry as compared to state-of-the-art

post-processing techniques. By leveraging hardware-

accelerated ray tracing within a ray mask, we have

achieved relatively interactive frame rates for real-

time rendering in games without extensive optimiza-

tion. We aim to integrate and optimize multiple hy-

brid rendering techniques including depth of ﬁeld into

our hybrid rendering engine, which will be open-

sourced for the beneﬁt of the research community and

industry.

ACKNOWLEDGEMENTS

We thank Wyman (2018) for the Falcor scene ﬁle

of THE MODERN LIVING ROOM (CC BY) as well

as the NVIDIA ORCA for that of UE4 SUN TEM-

PLE (CC BY-NC-SA) and AMAZON LUMBERYARD

BISTRO (CC BY). This work is supported by the

Singapore Ministry of Education Academic Research

grant T1 251RES1812, “Dynamic Hybrid Real-time

Rendering with Hardware Accelerated Ray-tracing

and Rasterization for Interactive Applications”.

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APPENDIX

Post-process Sampling Range

McGuire et al. (2012) uses the dominant velocity

within a neighbourhood of pixels as a heuristic. It ﬁrst

produces a per-exposure velocity from an inter-frame

motion vector, then applies a tile-dilate pass to select

the dominant velocity in the neighbourhood with the

largest magnitude. Here, the per-pixel velocities are

clamped at the length of each tile for the tile-dilate

pass to capture the maximum velocity from all possi-

ble contributions, preventing tiling artifacts.

For the tile-dilate pass, tile-dominant velocities

are ﬁrst selected for tiles of length m in pixel units,

where m is a common factor of the width and height

of the image buffer resolution. Then, neighbour-

dominant velocities are selected for kernels of length

n in tile units. For example, if we take m = 40, the

size of each tile is hence 40×40 = 1600 pixels. If we

then dilate each tile with n = 3 based on its 8 direct

neighbours, our dilated tile will take the maximum

pixel velocity from 9 tiles × 1600 pixels/tile = 14400

pixels in total.

Compared to approaches that use per-pixel veloc-

ities directly for gathering like Ritchie et al. (2010),

this dominant neighbour velocity approach prevents

issues such as sharp silhouettes as well as moving ob-

jects shrunk in size, as illustrated in McGuire et al.

(2012). Following its idea, the direction of sampling

at each pixel is along the dominant velocity of its

neighbourhood, and the range of samples is within

one magnitude of this velocity.

Post-process Sample Contribution

McGuire et al. (2012) performs a weighted average w

of all corresponding samples for each target pixel.

w = w

+ w

(5)

= saturate(1 + z) ×saturate(1 −

∆s

) (6)

= saturate(1 − z) ×saturate(1 −

∆s

) (7)

= cylinder(v

, ∆s) × cylinder(v

, ∆s) × 2

(8)

where

z =

− z

SOFT Z EXTENT

(9)

cylinder(v, s) = 1 − smoothstep(0.95v, 1.05v, s)

(10)

In the above formulae, z represents the extent that

the target pixel is deeper into the scene than its sam-

ple (negative when the pixel is shallower), while ∆s

GRAPP 2022 - 17th International Conference on Computer Graphics Theory and Applications

124

refers to the distance between the screen space po-

sitions of the target pixel and its sample. v

and v

are the screen space speeds of the target pixel and its

sample respectively, while z

and z

are their camera

space depths. SOFT Z EXTENT is a positive-valued

scene-dependent variable as introduced in McGuire

et al. (2012) used for the classiﬁcation of samples into

the foreground or background of the target pixel.

Case 1: Blurry Sample in Front of Any Target

considers the ﬁrst case where outer blur is present:

a shallower sample of speed larger or equal to its off-

set from the target pixel in screen space can contribute

to the pixel’s colour as part of the foreground. The

sample contributes as foreground by covering the tar-

get pixel within the exposure time. On the other hand,

since a sample deeper than the target pixel stands a

higher chance to be blocked during the exposure time,

the ﬁrst factor in Equation 6 smoothly ﬁlters out con-

tribution from deeper samples. Given that faster pix-

els also contribute less for each unit (in our case it is

the length of one pixel) in their trail, a comparison be-

tween the sample’s speed and its distance to the target

pixel is performed as the second factor of Equation 6.

Case 2: Any Sample behind Blurry Target

As for inner blur, the target pixel is moving and

deeper samples within its velocity range are selected

to ﬁll the transparency left by it. Hence, for w

the depth comparison is done reversely to ﬁlter out

shallower samples. Since Jimenez (2014) illustrates

that the edge of a motion-blurred object should be

a smooth gradient, a comparison between the target

pixel’s speed and its distance to the sample is also per-

formed as part of the second factor of Equation 7 to

assign a lower weight to further samples.

(a) w

excluded (b) w

included

Figure 7: Specular highlight in McGuire et al. (2012) post-

processed MBlur (one-directional).

Case 3: Simultaneously Blurry Target and Sample

A sample at any depth can contribute to the target

pixel’s colour as part of the foreground or background

when both the sample and the pixel are located in

each other’s velocity range. w

blurs the target pixel

and its sample into each other as a supplement to

the cases not handled well by the ﬁrst two terms in

Equation 5. Since further samples have been assigned

lower weights in w

and w

, the area of the resulting

MBlur for regions of high variance appears to shrink

as the target pixel gets further from these regions. For

instance, in the case of specular highlights, as shown

in Figure 7, the resulting blur appears to be triangular-

shaped with the base at the exact specular highlight in

the sharp rasterized image, while the desirable case

should be rectangular-shaped. Since w

will produce

a positive value when the distance between the target

pixel and its sample is not signiﬁcantly higher than the

speed of either of them, it creates a more reasonable

blur effect by enlarging the shrinking region.

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125