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
It is very common for most game engines or animation libraries to implement their own animation compression but the problem is so narrow and focused in scope, that it makes sense for the industry to converge on a single implementation.
Academic papers on the subject often will not compare techniques using the same error metric or raw size function which can skew the results or make them very hard to evaluate. They might also compare against techniques which are not state of the art or even used in production. This library aims to implement as many techniques as possible, side by side, in order to compare them fairly.
Much thought was put into designing the library for it to be as flexible and powerful as possible. To this end, the following decisions were made:
- The library consists of 100% C++ header files and is thus easy to integrate in any game engine
- An intermediary clip format is supported in order to facilitate debugging and bug reporting
- All allocations use a game provided allocator
- All asserts use a game provided macro
The library aims to support the most common platforms for the most common use cases. There is very little platform specific code as such it should work nearly everywhere.
The math library is not yet fully optimized for every platform. The overwhelming majority of the math heavy code executes when compressing, not decompressing. Decompression is typically very simple and light in order to be fast. Very little math is involved beyond interpolating values.
- Compression and decompression:
- Windows (VS2015, VS2017) x86 and x64
- Linux (gcc5, clang4, clang5) x86 and x64
- OS X (Xcode 8.3, Xcode 9.2) x86 and x64
- Decompression only: Android (NVIDIA CodeWorks)
- Install the proper compiler for your platform
- Install CMake 3.2 or higher
- Install Python 3
- Generate the IDE solution with:
python make.py
The solution is generated under./build
Note that if you do not have CMake in yourPATH
, you should define theACL_CMAKE_HOME
environment variable to something likeC:\Program Files\CMake
. - Build the IDE solution with:
python make.py -build
- Run the unit tests with:
python make.py -test
- Carnegie-Mellon University database performance
- Paragon database performance
- Matinee fight scene performance
This project uses the MIT license.
Copyright (c) 2017 Nicholas Frechette & Animation Compression Library contributors
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.