Animation Compression Library: Unreal 4 Integration

As mentioned in my previous post, I started working on integrating ACL into Unreal 4.15 locally. Today I can finally confirm that not only does it work but it rocks!

Matinee fight scene

In order to stress test ACL in a real game engine with real content, I set out to test it on the Matinee fight scene that can be found on the Unreal 4 Marketplace.

ACL in action

This is a very complex sequence with fast movements and LOTS of data. The main character (the white trooper) has over 540 bones because the whole cloth motion is baked. The sequence lasts about 66 seconds. The secondary characters move in and out of view and overall spend the overwhelming majority of the sequence completely idle and off screen.

Here is a short extract of the sequence using ACL for every character. This marks the first visual test and confirmation that ACL works.

The data

The video isn’t too interesting but once again the numbers tell a story of their own. Packaged As Is is the default settings used when you first open it up in the editor, as packaged on the marketplace. For ACL, the integration is somewhat dirty for now and uses the same settings as for the CMU database: the error is measured 3cm away from the bones, the error threshold is 0.1mm, and the segments are 16 frames long.

Matinee fight scene stats

ACL completely surpassed my own expectations here. The whole sequence is 59.5% smaller! The main trooper is a whopping 64.5% smaller! That’s nearly 3x smaller! Compression time is also entirely reasonable sitting at just over 1 minute. While the packaged settings are decent here sitting at around 5 minutes, the automatic compression setting is not practical with almost 3 hours. The error shown is what Unreal 4 reports in the dialog box after compression, it thus uses the Unreal 4 error metric and here again we can see that ACL is superior.

However, ACL does not perform as good on the secondary characters and ends up significantly larger. This is because they are mostly idle. Idle bones compress extremely well with linear key reduction but because ACL uses short segments, it is forced to retain at least a single key per segment. With some sort of automatic segment partitioning the memory footprint could reduce quite a bit here or even by simply using larger segments.

What happens now?

The integration that I have made will not be public or published for quite some time. Until we reach version 1.0, I wouldn’t want to support actual games while I am still potentially making large changes to the library. Once ACL is production ready and robust, I will see with Epic how we can go about making ACL a first-class citizen in their engine. In the meantime, I will maintain it locally and use it to test and validate ACL on the other platforms supported by Unreal.

For the time being, all hope is not lost! For the past 2 months, I have been working with Epic on improving the stock Unreal 4 animation compression. Our primary focus has been to improve decompression speed and reduce the compression time without compromising the already excellent memory footprint and accuracy. If all goes well these changes should make it in the next release and once that happens, I will update the relevant charts and graphs published here as well as in the ACL documentation.

Animation Compression Library: Release 0.4.0

This marks the fourth release of ACL. It contains a lot of good stuff but most notable is the addition of segmenting support. I have not had the chance to play with the settings much yet but using segments of 16 key frames reduces the memory footprint by about 13% with variable quantization under uniform sampling. Adding range reduction on top of it (per segment), further reduces the memory footprint by another 10%. This is very significant!

Some optimizations also made it in to the compression time, reducing it by 4.3x with no compromise to quality.

You can see the latest numbers here as well as how they compare against the previous releases here. Note that the documentation contains more graphs than I will share here.

This also represents the first release where graphs have been generated allowing us an unprecedented view into how the ACL and Unreal algorithms perform. As such, I will detail what is note-worthy and thus this blog post will be a bit long. Grab a coffee and buckle up!

TL;DR:

  • ACL compresses better than Unreal for nearly every clip in the CMU database.
  • ACL is much smaller than Unreal (23.4%), is more accurate (2x+), and compresses much faster (4.68x).
  • ACL performs as expected and optimizes properly for the error threshold used, validating our assumptions.
  • A threshold of 0.1cm is good enough for production use in Unreal as the overwhelming majority (98.15%) of the samples have an error smaller than 0.02cm.

Why compare against Unreal?

As I have previously mentioned, Unreal 4 has a very solid error metric and good implementations of common animation compression techniques. It most definitely is well representative of the state of animation compression in game engines everywhere.

NOTE: In the images that follow, the results for an error threshold of UE4 @ 1.0cm were nearly identical to 0.1cm and were thus omitted for brevity

Performance results

ACL 0.4 compresses the CMU database down to 82.25mb in 50 minutes single-threaded and 5 minutes multi-threaded with a maximum error of 0.0635cm. Unreal 4.15 compresses it down to 107.94mb in 3 hours and 54 minutes single-threaded with a maximum error of 0.0850cm (1.0cm threshold used). Importantly, this is achieved with no compromise to decompression speed (although not yet measured, is estimated to be faster or just as fast with ACL).

Compression ratio VS max error per clip

As can be seen on the above image, ACL performs quite well here. The error is very low and the compression quite high in comparison to Unreal.

Compression ratio distribution

Here we see the full distribution of the compression ratio over the CMU database. UE4 @ 0.01cm fails to do better than dropping the quaternion W and storing everything as full precision most of the time which is why the compression ratio is so consistent. UE4 @ 0.1cm performs similarly in that key reduction fails very often on this database and as a result simple quantization is most often selected.

Compression ratio distribution (bottom 10%)

Here is a snapshot of the bottom 10% (10th percentile and lower). We can see some similarities in shape at the bottom and top 10%.

Compression ratio by clip duration

We can see on the above image that Unreal performs consistently regardless of the animation clip duration but ACL performs slightly better the longer the clip is. This is most likely a direct result of using range reduction twice: once per clip, and once per segment.

Compression ratio by clip duration (shortest 100)

Both algorithms perform similarly for the shortest clips.

How accurate are we?

Max error distribution

The above image gives a good view of how accurate the algorithms are. We can see ACL @ 0.01cm and UE4 @ 0.01cm quickly reach the error threshold and only about 10% of the clips exceed it. UE4 @ 0.1cm is less accurate but still pretty good overall.

The biggest source of error in both ACL and Unreal comes from the usage of the simple quaternion format consisting of dropping the W component to later reconstruct it at runtime. As it turns out, this is terribly inaccurate when that component is very small. Better formats exist and will be implemented later.

ACL performs worse on a larger number of clips likely as a result of range reduction sometimes causing a precision loss for some clips. At some point ACL should be able to detect this and turn it off if it isn’t needed.

Max error by clip duration

There does not appear to be any correlation between the max error in a clip and its duration, as expected. One thing stands out though, the longer a clip is, the noisier the error appears to be. This is because the longer a clip is the more likely it is to contain a bad quanterion W that fails to reconstruct properly.

Over the years, I’ve read my fair share of animation compression papers and posts. And while they all measure the error differently the one thing they have in common is that they only talk about the worst error within a clip (or whole set of clips). As I have previously mentioned, how you measure the error is very important and must be done carefully but that is not all. Using the worst error within a given clip does not give a full picture. What about the other bones in the clip? What about the other key frames? Do I have a single bone on a single key frame that violates my threshold or do I have many?

In order to get a full and clear picture, I dumped the error of every bone at every key frame in the original clips. This represents over 37 million samples for the CMU database.

Distribution of the error for every bone at every key frame

The above image is amazing!

Distribution of the error for every bone at every key frame (top 10%)

The above two images clearly show how terrible the max clip error is at giving insight into the true error. Here are some numbers visible only in the exhaustive graphs:

  • ACL crosses the 0.01cm error threshold at the 99.85th percentile (only 0.15% of our values exceed the threshold!)
  • UE4 @ 0.01cm crosses 0.01cm at the 99.57th percentile, almost just as good
  • UE4 @ 0.1cm crosses 0.01cm at the 49.8th percentile
  • UE4 @ 0.1cm crosses 0.02cm at the 98.15th percentile

This clearly shows why 0.1cm might be good enough for production use in Unreal: half our values remain at or below 0.01cm and 98% of the values are below 0.02cm.

The previous images also clearly show how aggressive ACL is at reducing the memory footprint and at maximizing the error up to the error threshold. Therefore, the error threshold must be very conservative, much more so than for Unreal.

Why ACL is re-inventing the wheel

As some have commented in the past, ACL is largely re-inventing the wheel here. As such I will detail the rational for it a bit further.

Writing a whole animation blending middleware such as Granny or Morpheme would not have been practical. Just to match production quality implementations out there would have taken 1+ year part time. Even assuming I could have managed to implement something compelling, the cost of switching to a new animation runtime for a game team is very very high. Animators need to learn new tools and workflow, the engine integration might be tightly coupled, and there is no clear way to migrate old assets to the new format. Middlewares are also getting deprecated increasingly frequently. In that regard, the market has largely spoken: most games released today do so either with one of the major engines (Unreal, Unity, Lumberyard, Stingray, etc.) or large studios such as Activision, Electronic Arts, and Ubisoft routinely have in-house custom engines with their own custom animation runtime. Regardless of the quality or feature set, it would have been highly unlikely that it would ever have been used for something significant.

On the other hand, animation compression is a much smaller problem. Integration is easy: everything is pure C++ headers and most engines out there already support more than one animation compression algorithm. This makes migrating existing assets a trivial task providing the few required features are supported (e.g. 3D scale). Any engine or middleware could integrate ACL with few to no issues to be expected once it is production ready.

Animation compression is also a wheel that NEEDS re-inventing. Of all my blog posts, a single post receives the overwhelming majority of my traffic: animation compression in Unity. Why is it so popular? Because as I mention in said post, accuracy issues will be common in Unity and the memory footprint large for high accuracy settings as a direct result of their error metric. Unity is also not alone, Stingray and Lumberyard both use the same metric. It is a VERY common error metric and it is terrible. Academic papers on this topic are often using different and poor error metrics and show very little to no data to back their results and claims. This makes evaluating these papers for real world usage in games very problematic.

Take this paper for example. They use the CMU database as well. Their error metric uses the leaf bone positions in object/world space as a measure of accuracy. This entirely ignores the rotational error of the leaf bone. They show a single graph of their results and two short tables. They do not detail the data further. Compare this with the wealth of information I was able to pull out and publish here. Even though ACL is much stricter when measuring the error, it is obvious that wavelets fail terribly to compete at the same level of accuracy (which barely makes it in their published findings). Note that they make no mention of what is an acceptable quality level that one might be able to realistically use.

Here is another recent paper published by someone I have met and have great respect for. The paper does not mention which error metric was used to compared against what they had prior nor does it mention how competitive their previous implementation was. It does not publish any concrete data either and only claims that the memory footprint reduces by 65% on average against their previous in-house techniques. It does provide a supplemental video which shows a small curated list of clips along with some statistics but without further information, it is impossible to objectively evaluate how it performs and where it lies on the spectrum of published techniques. Despite these shortcomings, it looks very promising (David knows his stuff!) and I am certainly looking forward to implementing this within ACL.

ACL does not only strive to improve on existing techniques; it will also establish a much-needed baseline to compare against and set a standard for how animation compression should be measured.

Next steps

The results so far clearly show that ACL is one step closer to being production ready. The next few months will focus on bridging that gap towards reaching v1.0.0. In the coming releases, scale support will be added as well as support for other leading platforms. This will be done through a rudimentary Unreal 4 integration to make sure it is tested in a real engine and thus real world settings.

No further effort on my part will be made towards improving the above results until our first production release is made. However, Cody Jones is working on integrating curve key reduction in the meantime.

Special thanks to Cody and Martin Turcotte for their constant feedback and contributions!

Math accuracy: Normalizing quaternions

While investigating precision issues with ACL, I ran into two problems that I hadn’t seen documented elsewhere and that slightly surprised me.

Dot product

Calculating the dot product between two vectors is a very common operation used for all sorts of things. In an animation compression library, it’s primary use is normalizing quaternions. Due to the nature of the code, accuracy is very important as it can impact the final compressed size as well as the resulting decompression error.

SSE 4 introduced a dot product instruction: DPPS. It allows the generated code to be more concise and compact by using fewer registers and instructions. I won’t speak to its performance here but sadly; its accuracy is not good enough for us by a tiny, yet important, sliver.

For the purpose of this blog post, we will use the following nearly normalized quaternion as an example: { X, Y, Z, W } = { -0.6767403483, 0.7361232042, 0.0120376134, -0.0006215832 }. This is a real quaternion from a real clip of the Carnegie-Mellon University (CMU) motion capture database that proved to be problematic. With doubles, the dot product is 1.0000001612809224.

Using plain C++ yields the following code and assembly (compiled with AVX support under Visual Studio 2015 with an x64 target):

  • The result is: 1.00000024. Not quite the same but close.

Using the SSE 4 dot product instruction yields the following code and assembly:

  • The result is: 1.00000024.

Using a pure SSE 2 implementation yields the following assembly:

  • The result is: 1.00000012.

These are all nice but it isn’t immediately obvious how big the impact can be. Let’s see how they perform after taking the square root (note that the SSE 2 SQRT instruction is used here):

  • C++: 1.00000012
  • SSE 4: 1.00000012
  • SSE 2: 1.00000000

Again, these are all pretty much the same. What happens when we take the square root reciprocal after 2 iterations of Newton-Raphson?

  • C++: 0.999999881
  • SSE 4: 0.999999881
  • SSE 2: 0.999999940

With this square root reciprocal, here is how our quaternions look after being multiplied to normalize them and their associated dot product.

  • C++: { -0.676740289, 0.736123145, 0.0120376116, -0.000621583138 } = 0.999999940
  • SSE 4: { -0.676740289, 0.736123145, 0.0120376116, -0.000621583138 } = 1.00000000
  • SSE 2: { -0.676740289, 0.736123145, 0.0120376125, -0.000621583138 } = 0.999999940

Here is the dot product calculated with doubles:

  • C++: 0.99999999381912441
  • SSE 4: 0.99999999381912441
  • SSE 2: 0.99999999384079208

And the new square root:

  • C++: 0.999999940
  • SSE 4: 1.00000000
  • SSE 2: 0.999999940

Now the new reciprocal square root:

  • C++: 1.00000000
  • SSE 4: 1.00000000
  • SSE 2: 1.00000000

After all of this, our delta from a true length of 1.0 before (as calculated with doubles) was 1.612809224e-7 before normalization. Here is how they fare afterwards:

  • C++: 6.18087559e-9
  • SSE 4: 6.18087559e-9
  • SSE 2: 6.15920792e-9

And thus, the difference between using SSE 4 and SSE 2 is just 2.166767e-11.

As it turns out, the SSE 2 implementation appears the most accurate one and yields the lowest decompression error as well as a smaller memory footprint (by a tiny bit).

Normalizing a quaternion

There are two mathematically equivalent ways to normalize a quaternion: taking the dot product, calculating the square root, and dividing the quaternion with the result, or taking the dot product, calculating the reciprocal square root, and multiplying the quaternion with the result.

Are the two methods equivalent with floating point mathematics? Again, we will not discuss the performance implications as we are only concerned with accuracy here. Using the previous example quaternion and using the SSE 2 dot product yields the following result with the first method:

  • Dot product: 1.00000012
  • Length: sqrt(1.00000012) = 1.00000000
  • Normalized quaternion using division: { -0.6767403483, 0.7361232042, 0.0120376134, -0.0006215832 }
  • New dot product: 1.00000012
  • New length: 1.00000000

And now using the reciprocal square root with 2 Newton-Raphson iterations:

  • Dot product: 1.00000012
  • Reciprocal square root: 0.999999940
  • Normalized quaternion using multiplication: { -0.676740289, 0.736123145, 0.0120376125, -0.000621583138 }
  • New dot product: 0.999999940
  • New length: 0.999999940
  • New reciprocal square root: 1.00000000

By using the division, normalization fails to yield us a more accurate quaternion because of square root is 1.0. The reciprocal square root instead allows us to get a more accurate quaternion as demonstrated in the previous section.

Conclusion

It is hard to see if the numerical difference is meaningful but over the entire CMU database, both tricks together help reduce the memory footprint by 200 KB and lower our error by a tiny bit.

For most game purposes, the accuracy implication of these methods does not matter all that much and rarely have a measurable impact. Picking whichever method is fastest to execute might just be good enough.

But when accuracy is of a particular concern, special care must be taken to ensure every bit of precision is retained. This is one of the motivating reasons for ACL having its own internal math library: granular control over performance and accuracy.

Animation Compression Library: Release 0.3.0

This release marks an important milestone. It now supports a fully variable bit rate and it performs admirably so far. The numbers don’t lie. Without using any form of key reduction, we match the compression ratio of Unreal 4 (which uses a mix of linear key reduction with a form of variable quantization) and many more tricks will follow to push this even further. It is worth noting that this new variable bit rate algorithm is entirely different from the one I presented at the GDC 2017 and it should outperform it. In due time, more stats and graphs will be published to outline how the data looks across the whole dataset.

While v0.3.0 remains a pre-release, we are quickly approaching a production ready state. Already for the vast majority of clips the error introduced is invisible to the naked eye and the performance is there to match. Major features missing to reach the production ready state are: scale support (sadly the Carnegie-Mellon data set does not contain any scale as such testing this will be problematic), and proper multi-platform support (iOS, OS X, android, clang, gcc, etc.). Both of these things are easily solved problems which is why they were deferred into future releases.

Version 0.4.0 will aim to introduce clip segmenting and hopefully curve based key reduction. Segmenting should improve our accuracy further and at the same time allow us to reduce the memory footprint even further. Curve key reduction will of course allow us to reduce the memory footprint further as well, perhaps dramatically so. Stay tuned!

Introducing ACL

Over the years, I’ve had my fare share of discussions about animation compression and two things became obvious over time: we were all (re-)doing similar things and none of us had access to a state of art implementation to compare against. This lead to rampant speculation about which algorithm was superior or inferior. Having implemented a few algorithms in the past, I have finally decided to redo all that work once more and in the open this time. Say ‘Hello’ to the Animation Compression Library (ACL for short).

To quote the readme:

This library has two primary goals:

  • Implement state of the art and production ready animation compression algorithms
  • Serve as a benchmark to compare various techniques against one another

Over the next few months, I hope to implement state of the art versions of common algorithms and to surpass what current game engines currently offer. It is my hope that this library can serve as the foundation for an industry standard so that together we may be able to move forward and well past the foot sliding issues of yester-year!