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OpenARK Avatar Development Project

Pipeline

Demo Screenshot (Quite Old)

A smaller reimplementation of OpenARK Avatar using only analytic derivatives.

Building

Dependencies

  • Boost 1.58
  • OpenCV 3.3+ (OpenCV 4 not supported)
  • Eigen 3.3.4
  • Ceres Solver 1.14 (Ceres 2 not supported).
    • This is very performance critical, and it is strongly recommended to manually build Ceres with LAPACK and OpenMP support.
    • If you are using an Intel processor, it is also recommended to use MKL as BLAS/LAPACK. Otherwise ATLAS is recommended.
    • Finally, make sure you build Ceres in release mode.
  • K4A (Azure Kinect SDK), optional but required for live-demo
  • PCL 1.8+, optional

Earlier versions of these libraries may work, but I have not tested them

How to build

If you haven't already, install CMake.

mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j4

Replace 4 with an appropriate number of threads. Add -DWITH_PCL=ON to enable PCL, add -DWITH_K4A=OFF to disable looking for Azure Kinect SDK, add -DBUILD_RTREE_TOOLS=OFF to disable building RTree tools such as rtree-train, rtree-run-dataset.

For unknown reasons, sometimes I encounter linker errors when not manually linking OpenMP. If this happens configure with -DWITH_OMP=ON.

Outputs

Core

  • live-demo: from live-demo.cpp. Live demo, runs the system end-to-end on Azure Kinect camera input. Requires K4A library to be installed
  • bgsubtract : from bgsubtract.cpp. Somewhat of a misnomer, runs the system end-to-end on an OpenARK dataset in standard format (depth_exr, etc)
  • data-recording : from DataRecording.cpp. Tool for recording datasets from the Azure Kinect camera. Mostly copied from OpenARK, but fixes memory bug.
  • libsmplsynth.a : the static library which the above depend on. I configure the project like this to improve build times when editing different outputs.

SMPL Model Tools

  • smplsynth : from smplsynth.cpp. Synthetic human dataset generator
  • smpltrim : fom smpltrim.cpp. A tool for generating partial SMPL models, including creating a smaller model with a specific joint as root, or cutting off limbs

Random Forest Tools

  • rtree-train: from rtree-train.cpp. High performance random tree trainer. Find trained trees in releases on Github
  • rtree-transfer: from rtree-transfer.cpp. Tool to refine a trained random tree by recomputing leaf distributions over a huge amount of images.
  • rtree-run: from rtree-run.cpp. Run rtree on images (not important).
  • rtree-run-dataset: from rtree-run-dataset.cpp. Run rtree on OpenARK dataset in standard format (depth_exr, etc)

Miscellaneous

  • scratch : from scratch.cpp. Currently configured to show human avatar when ran, with (limited) options to adjust pose and shape. Generally, used for scratch.
  • optim : from optim.cpp. Currently disabled since not updated after API change; optimizes avatar pose to fit a synthetic point cloud.

Getting model data

Please get the data from me via email sxyu (at) berkeley.edu. (This is not allowed to be shared so I am not putting the link here). Then put it in this directory: <smplsynth-repo-root>/data

So that the following exists: <smplsynth-repo-root>/data/avatar-model/skeleton.txt

License

Apache 2.0

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