An automated algorithm to extract the linear blend skinning (LBS) from a set of example poses

Overview

Dem Bones

BSD3 Clause Version

This repository contains an implementation of Smooth Skinning Decomposition with Rigid Bones, an automated algorithm to extract the Linear Blend Skinning (LBS) with bone transformations from a set of example meshes. Skinning Decomposition can be used in various tasks:

  • converting any animated mesh sequence, e.g. geometry cache, to LBS, which can be replayed in popular game engines,
  • solving skinning weights from shapes and skeleton poses, e.g. converting blendshapes to LBS,
  • solving bone transformations for a mesh animation given skinning weights.

This project is named after "The Skeleton Dance" by Super Simple Songs.

Contents

  • include/DemBones: C++ header-only core library using Eigen and OpenMP. Check out the documentations in docs/index.html.
  • bin: pre-compiled command line tools for Windows, Linux, and MacOS that read and write FBX and Alembic files. Check out the usage by running DemBones --help.
  • src/command: source code for the command line tool. Check out AbcReader.cpp, FbxReader.cpp, and FbxWriter.cpp for the usage of the core library.
  • data: input/output test data for the command line tool. Run and check out the scripts run.bat (Windows) or ./run.sh (Linux/MacOS).

Compiling

Tested platforms:

  • Visual Studio 2019 on Windows 10 x64
  • g++ 9.3.0 on Ubuntu Linux 20.14
  • LLVM 10.0.0 (Homebrew) on MacOS 10.13.6

Compiling steps:

  1. Install cmake
  2. Copy the following libraries to their respective folders in ExtLibs so that cmake can find these paths:
  3. Run cmake:
mkdir build
cd build
cmake ..
  1. Build: $ cmake --build . --config Release --target install

Notes for Linux

Notes for MacOS

  • Apple Clang (default compiler) does not support OpenMP and Eigen. We recommend using LLVM. Assuming LLVM is installed in the default path: /usr/local/opt/llvm/bin, instead of cmake .., you can run:

    $ cmake -DCMAKE_C_COMPILER=/usr/local/opt/llvm/bin/clang -DCMAKE_CXX_COMPILER=/usr/local/opt/llvm/bin/clang++ ..
    
  • The pre-compiled tool bin/DemBones requires dynamic libomp for LLVM. If you have an error messeage related to OpenMP, please install libomp, e.g. with Homebrew using $ brew install libomp.

  • bin/MacOS/DemBones was compiled with the optimization flag -O3. It looks like LLVM uses fast math so the results are slightly different with those generated by Windows version. Removing optimization flags (in CMakeLists.txt) helps to reproduce the same results with Windows version but the tool will run 10x slower.

References

If you use the library or the command line tool, please cite the paper:

Binh Huy Le and Zhigang Deng. Smooth Skinning Decomposition with Rigid Bones. ACM Transactions on Graphics 31(6), Proceedings of ACM SIGGRAPH Asia 2012.

BibTeX:

@article{LeDeng2012,
    author = {Le, Binh Huy and Deng, Zhigang},
    title = {Smooth Skinning Decomposition with Rigid Bones},
    journal = {ACM Trans. Graph.},
    volume = {31},
    number = {6},
    year = {2012}
} 

The skinning weights smoothing regularization was published in the paper:

Binh Huy Le and Zhigang Deng. Robust and Accurate Skeletal Rigging from Mesh Sequences. ACM Transactions on Graphics 33(4), Proceedings of ACM SIGGRAPH 2014.

Authors


Search for Extraordinary Experiences Division (SEED) - Electronic Arts
http://seed.ea.com

We are a cross-disciplinary team within EA Worldwide Studios.
Our mission is to explore, build and help define the future of interactive entertainment.

Dem Bones was created by Binh Le ([email protected]). The logo was designed by Phuong Le.

Contributing

Before you can contribute, EA must have a Contributor License Agreement (CLA) on file that has been signed by each contributor. You can sign here: http://bit.ly/electronic-arts-cla

Licenses

  • The source code, including include/DemBones and src/command, uses BSD 3-Clause License as detailed in LICENSE.md
  • The pre-compiled command line tool bin/DemBones(.exe) uses third party libraries: Eigen, tclap, Alembic, FBXSDK, and zlib with licenses in 3RDPARTYLICENSES.md
Comments
  • Question about transAffine and transAffineNorm regularizers

    Question about transAffine and transAffineNorm regularizers

    Hi, I am really confused about the transAffine and transAffineNorm regularizers. I read the original SSDR paper and some following papers like the Robust SSDR in 2014, but can't find the reference for transAffineNorm regularizers.

    Would you please tell the reference about the transAffine and transAffineNorm regularizers? Thanks a lot!

    opened by pramanc 4
  • Infinite loop when initializing bones

    Infinite loop when initializing bones

    Occasionally I run in to cases where DemBones gets stuck in an infinite loop while trying to initialize the bones in DemBones.h init(). I've attached a sample fbx and abc that causes the issue.

    faceshapes.zip

    Any insight as to why?

    ../bin/DemBones.exe -i="neutral.fbx" -a="face_rom.abc" -b=64 -o="Decomposition_64.fbx"
    1 animated mesh sequence(s):
        "face_rom.abc" + "neutral.fbx" --> "Decomposition_64.fbx"
    Parameters:
        nBones (target)    = 64
        nInitIters         = 10 (default)
        nIters             = 30 (default)
        nTransIters        = 5 (default)
        nWeightsIters      = 3 (default)
        bindUpdate         = 0 (no update) (default)
        transAffine        = 10 (default)
        transAffineNorm    = 4 (default)
        nnz                = 8 (default)
        weightsSmooth      = 0.0001 (default)
        weightsSmoothStep  = 1 (default)
    Checking ABCs:
        "face_rom.abc": /Louise_asym/Louise_asymShape (57 frames)
    Reading ABCs......................................................... Done!
    Reading FBXs:
        "neutral.fbx"... Done!
        5034 vertices
    Initializing bones: 1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>1>
    
    opened by chadmv 4
  • Can dem-bones work directly on a PLY or OBJ sequence?

    Can dem-bones work directly on a PLY or OBJ sequence?

    Is it possible for Dem-Bones to directly convert a sequence of PLY or OBJ files into a single animated and skinned FBX, instead of using an animated Alembic files?

    opened by aapatre 2
  • Question about rigid transformation from covariance matrix

    Question about rigid transformation from covariance matrix

    Hi, Thanks for your code! Really nice work! I have one question about Best Rigid Transformation from covariance matrix. From the paper here https://igl.ethz.ch/projects/ARAP/svd_rot.pdf or the SSDR paper

    in the function qpT2m:

    Eigen::JacobiSVD svd(...) d(2, 2) = (svd.matrixU()*svd.matrixV.transpose()).determinant(); m.rotMat(k, j) = svd.matrixU()dsvd.matrixV().transpose();

    should be ?

    d(2, 2) = (svd.matrixV()*svd.matrixU.transpose()).determinant(); m.rotMat(k, j) = svd.matrixV()dsvd.matrixU().transpose();

    =================================== but definitly from the result, your implementation is right. I'm really confused!

    Thanks!

    opened by pramanc 2
  • how to compile in debug mode

    how to compile in debug mode

    I compile in debug mode in VS2019, Win10. all configures are in debug mode when configure cmake-gui. FBXSDK is debug version lib.

    but it seems Alembic from Alembic (from Maya 2020 Update 2 DevKit) only contains release libs. How to get Debug version Alembic? Should I compile Alembic from https://github.com/alembic/alembic? Then how to organize the catalogue? Which version of Alembic (from Maya 2020 Update 2 DevKit) is?

    The compile error is: 1>Alembic.lib(DataTypeRegistry.obj) : error LNK2038: mismatch detected for 'RuntimeLibrary': value 'MD_DynamicRelease' doesn't match value 'MDd_DynamicDebug' in AbcReader.obj 1>Alembic.lib(SprImpl.cpp.obj) : error LNK2038: mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '0' doesn't match value '2' in AbcReader.obj 1>Alembic.lib(SprImpl.cpp.obj) : error LNK2038: mismatch detected for 'RuntimeLibrary': value 'MD_DynamicRelease' doesn't match value 'MDd_DynamicDebug' in AbcReader.obj 1>Alembic.lib(AprImpl.cpp.obj) : error LNK2038: mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '0' doesn't match value '2' in AbcReader.obj 1>Alembic.lib(AprImpl.cpp.obj) : error LNK2038: mismatch detected for 'RuntimeLibrary': value 'MD_DynamicRelease' doesn't match value 'MDd_DynamicDebug' in AbcReader.obj 1>Alembic.lib(ScalarPropertyWriter.obj) : error LNK2038: mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '0' doesn't match value '2' in AbcReader.obj 1>Alembic.lib(ScalarPropertyWriter.obj) : error LNK2038: mismatch detected for 'RuntimeLibrary': value 'MD_DynamicRelease' doesn't match value 'MDd_DynamicDebug' in AbcReader.obj 1>Alembic.lib(ArrayPropertyWriter.obj) : error LNK2038: mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '0' doesn't match value '2' in AbcReader.obj 1>Alembic.lib(ArrayPropertyWriter.obj) : error LNK2038: mismatch detected for 'RuntimeLibrary': value 'MD_DynamicRelease' doesn't match value 'MDd_DynamicDebug' in AbcReader.obj 1>Alembic.lib(ArrayPropertyReader.obj) : error LNK2038: mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '0' doesn't match value '2' in AbcReader.obj 1>Alembic.lib(ArrayPropertyReader.obj) : error LNK2038: mismatch detected for 'RuntimeLibrary': value 'MD_DynamicRelease' doesn't match value 'MDd_DynamicDebug' in AbcReader.obj 1>Alembic.lib(ScalarSample.obj) : error LNK2038: mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '0' doesn't match value '2' in AbcReader.obj 1>Alembic.lib(ScalarSample.obj) : error LNK2038: mismatch detected for 'RuntimeLibrary': value 'MD_DynamicRelease' doesn't match value 'MDd_DynamicDebug' in AbcReader.obj 1>AlembicHalf.lib(half.obj) : error LNK2038: mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '0' doesn't match value '2' in AbcReader.obj 1>AlembicHalf.lib(half.obj) : error LNK2038: mismatch detected for 'RuntimeLibrary': value 'MD_DynamicRelease' doesn't match value 'MDd_DynamicDebug' in AbcReader.obj 1>AlembicIex.lib(IexBaseExc.obj) : error LNK2038: mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '0' doesn't match value '2' in AbcReader.obj 1>AlembicIex.lib(IexBaseExc.obj) : error LNK2038: mismatch detected for 'RuntimeLibrary': value 'MD_DynamicRelease' doesn't match value 'MDd_DynamicDebug' in AbcReader.obj 1>LINK : warning LNK4098: defaultlib 'MSVCRT' conflicts with use of other libs; use /NODEFAULTLIB:library 1>D:\Projects\dem-bones\build_debug\Debug\DemBones.exe : fatal error LNK1319: 184 mismatches detected 1>Done building project "DemBonesCmd.vcxproj" -- FAILED. ========== Build: 0 succeeded, 1 failed, 1 up-to-date, 0 skipped ==========

    opened by tang1485 2
  • Question about lockW/lockM

    Question about lockW/lockM

    There are finished skin meshes and animations, and then I added some blend shapes. I added some new bones at the origin without animation, and set lockM to 0 for those new bones. (The original bone is set to 1) Set the lockW value of the deformation vertex to 0. (set Others to 1) Set matrix w and matrix m with original data,set w value of new bones to 0, and m value of new bones to Matrix4::Identity(). I want these bones to replace blend shapes,but it did not work. The result of the compute is that these bones are still at the origin, and there are no vertices with their weights. I don't know how to work properly

    opened by Space-Invader-GS 1
  • Could it be possible to transform joint scale?

    Could it be possible to transform joint scale?

    Hi and thanks a lot for this, it works very well !

    I was wondering if it could be possible to allow joint scale to better match the deformed geometry. I think, in some cases where scale is supported, it could help reduce the number of bone needed.

    Thanks again!

    opened by JonasOuellet 1
  • The translation data of bones are always the same! But it changes when view in Maya!

    The translation data of bones are always the same! But it changes when view in Maya!

    Hi, I recently used SSDR created a skeleton animation and write the animation into an .fbx file. Then I tried to use fbxsdk to extract the translation and rotation data of each bone from the animation.

    However, I found that the extracted translation or rotation(both Global or Local ) data are always the same as the initial value, but when I import the fbx file into Maya, the animation looks fine, and the local translation and rotation value of each bone varies over time. The data extraction code are shown below:

    One thing wierd is that I found the number of AnimStack in the .fbx which created by SSDR are always 2. I'm not sure whether this factor leads to my problem.

    bool readFbx(string file_path)
    {
    	FbxManager* Sdkmanager = NULL;
    	fbxsdk::FbxScene* Scene = NULL;
    	bool Result;
    	//prepare the FBX SDK
    	InitializeSdkObjects(Sdkmanager, Scene);
    	FbxString file(file_path.c_str());
    	Result = LoadScene(Sdkmanager, Scene, file.Buffer());
    	vector<FbxString>boneName;
    	vector<fbxsdk::FbxNode*>bone_Node;
    	if (Result == false)
    	{
    		FBXSDK_printf("\n\nAn error occurred while loading the scene...");
    		return Result;
    	}
            else
    	{
    		// get the name of each bones of the skeleton
    		fbxsdk::FbxNode* root = Scene->GetRootNode();
    		for (int i = 0; i < root->GetChildCount(); ++i)
    		{
    			fbxsdk::FbxNode* pNode = root->GetChild(i);
    			fbxsdk::FbxNodeAttribute::EType lAttributeType;
    			if (pNode->GetNodeAttribute() == NULL)
    			{
    				FBXSDK_printf("NULL Node Attribute\n\n");
    			}
    			else
    			{
    				lAttributeType = (pNode->GetNodeAttribute()->GetAttributeType());
    				switch (lAttributeType)
    				{
    				case fbxsdk::FbxNodeAttribute::eSkeleton:
    					GetSkeletonInformation(boneName, bone_Node, pNode);//遍历节点找到mesh节点
    					break;
    				case FbxNodeAttribute::eCamera:
    					break;
    				default:
    					break;
    				}
    			}
    		}
                    // read the global translation data over time
    		const int AnimStackCount = Scene->GetSrcObjectCount<fbxsdk::FbxAnimStack>();
    		for (int i = 0; i < AnimStackCount; ++i)
    		{
    			fbxsdk::FbxAnimStack* pstack = Scene->GetSrcObject<fbxsdk::FbxAnimStack>(i);
    			const char* stackname = pstack->GetName();
    			cout << "Stack: " << stackname << endl;
    
    			const int nLayerCount = pstack->GetMemberCount();
    			for (int j = 0; j < nLayerCount; ++j)
    			{
    				fbxsdk::FbxAnimLayer* pLayer = pstack->GetMember<fbxsdk::FbxAnimLayer>(j);
    				const char* layername = pLayer->GetName();
    				cout << "Layer: " << layername << endl;
    				fbxsdk::FbxAnimCurve* lCurveRX = bone_Node[0]->LclRotation.GetCurve(pLayer, FBXSDK_CURVENODE_COMPONENT_X, false);
    				if (lCurveRX == NULL)
    				{
    					continue;
    				}
    				int pRXCount = lCurveRX->KeyGetCount();
    				for (int s = 0; s < pRXCount; ++s)
    				{
    					FbxTime ktime = lCurveRX->KeyGetTime(s);
    					vector<float> bone_position;
    					for (int bone = 0; bone < bone_Node.size(); ++bone)
    					{
    						FbxAMatrix GLOBAL = bone_Node[bone]->EvaluateLocalTransform(ktime);
    						FbxVector4 T = GLOBAL.GetT();
    						
    						fbxsdk::FbxString tmpName("LiYuanQian_rig_v001:Root_M");
    						if (boneName[bone] == tmpName)
    						{
    							cout << tmpName.Buffer() << "frame: " << s << " trans:" << T[0] << "," << T[1] << "," << T[2] << "." << " ktime"  << endl;;
    						}
    						
    						for (int k = 0; k < 3; ++k)
    						{
    							bone_position.push_back(T[k]);
    							
    						}
    					}
    				}
    
    			}
    		}
    		return Result;
    	}
    }
    
    opened by ANYMS-A 1
  • Consultation about bone joints constraints

    Consultation about bone joints constraints

    Thanks sincerely for sharing your code on Github, But I still have a few confusion. I don't see any code implementing bone joints constraints. I don't know if I missed something or you have not updated it yet.

    opened by OvidZheng 1
  • Template error message in VS2019

    Template error message in VS2019

    Hello,

    I am getting the following error messages when I compile demBones in VS2019:

    Error: Dem::DemBones: use of class templates requires template argument list

    Error C2352 'Dem::DemBones<_Scalar,_AniMeshScalar>::clear': illegal call of non-static member function cmt C:\Users\max\source\repos\cmt\src\DemBones\DemBonesExt.h 108

    The line error is in bold text.

    DemBonesExt.h ... /** @brief Clear all data */ void clear() { fTime.resize(0); boneName.resize(0); parent.resize(0); bind.resize(0, 0); preMulInv.resize(0, 0); rotOrder.resize(0, 0); DemBones::clear(); }

    Any idea? Thx, Max

    opened by mmallmann 1
  • Returning reference to temporary(results in seg fault or undefined behavior)

    Returning reference to temporary(results in seg fault or undefined behavior)

    gcc 7.4.0 reports an warning to this line

    https://github.com/electronicarts/dem-bones/blob/2ab9b0447ad4b8bf4a141b9c65af717e7e2c2e0f/include/DemBones/DemBones.h#L297

    Returning reference to temporary triggers segmentation fault or undefined behavior. Work around is creating an variable.

            VectorX x0 = w.col(i).toDense().cwiseMax(0.0);
            x=indexing_vector(x0, idx.head(nnzi));
    

    Although it is better to fix not returning temp address in Indexing.h

    /home/syoyo/work/dem-bones/include/DemBones/Indexing.h: In instantiation of ‘const typename ArgType::Scalar& Dem::indexing_functor_vector<ArgType, IndexType>::operator()(Eigen::Index) const [with ArgType = Eigen::CwiseBinaryOp<Eigen::internal::scalar_max_op<double, double>, const Eigen::Matrix<double, -1, 1>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1> > >; IndexType = Eigen::VectorBlock<Eigen::Array<int, -1, 1>, -1>; typename ArgType::Scalar = double; Eigen::Index = long int]’:
    /home/syoyo/work/dem-bones/ExtLibs/Eigen/Eigen/src/Core/CoreEvaluators.h:430:110:   required from ‘Scalar Eigen::internal::nullary_wrapper<Scalar, NullaryOp, false, true, false>::operator()(const NullaryOp&, IndexType) const [with IndexType = long int; Scalar = double; NullaryOp = Dem::indexing_functor_vector<Eigen::CwiseBinaryOp<Eigen::internal::scalar_max_op<double, double>, const Eigen::Matrix<double, -1, 1>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1> > >, Eigen::VectorBlock<Eigen::Array<int, -1, 1>, -1> >]’
    /home/syoyo/work/dem-bones/ExtLibs/Eigen/Eigen/src/Core/CoreEvaluators.h:533:37:   required from ‘Eigen::internal::evaluator<Eigen::CwiseNullaryOp<NullaryOp, PlainObjectType> >::CoeffReturnType Eigen::internal::evaluator<Eigen::CwiseNullaryOp<NullaryOp, PlainObjectType> >::coeff(IndexType) const [with IndexType = long int; NullaryOp = Dem::indexing_functor_vector<Eigen::CwiseBinaryOp<Eigen::internal::scalar_max_op<double, double>, const Eigen::Matrix<double, -1, 1>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1> > >, Eigen::VectorBlock<Eigen::Array<int, -1, 1>, -1> >; PlainObjectType = Eigen::Matrix<double, -1, 1>; Eigen::internal::evaluator<Eigen::CwiseNullaryOp<NullaryOp, PlainObjectType> >::CoeffReturnType = double]’
    /home/syoyo/work/dem-bones/ExtLibs/Eigen/Eigen/src/Core/AssignEvaluator.h:642:5:   required from ‘void Eigen::internal::generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>::assignCoeff(Eigen::Index) [with DstEvaluatorTypeT = Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1> >; SrcEvaluatorTypeT = Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Dem::indexing_functor_vector<Eigen::CwiseBinaryOp<Eigen::internal::scalar_max_op<double, double>, const Eigen::Matrix<double, -1, 1>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1> > >, Eigen::VectorBlock<Eigen::Array<int, -1, 1>, -1> >, Eigen::Matrix<double, -1, 1> > >; Functor = Eigen::internal::assign_op<double, double>; int Version = 0; Eigen::Index = long int]’
    /home/syoyo/work/dem-bones/ExtLibs/Eigen/Eigen/src/Core/AssignEvaluator.h:501:7:   required from ‘static void Eigen::internal::dense_assignment_loop<Kernel, 1, 0>::run(Kernel&) [with Kernel = Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1> >, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Dem::indexing_functor_vector<Eigen::CwiseBinaryOp<Eigen::internal::scalar_max_op<double, double>, const Eigen::Matrix<double, -1, 1>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1> > >, Eigen::VectorBlock<Eigen::Array<int, -1, 1>, -1> >, Eigen::Matrix<double, -1, 1> > >, Eigen::internal::assign_op<double, double>, 0>]’
    /home/syoyo/work/dem-bones/ExtLibs/Eigen/Eigen/src/Core/AssignEvaluator.h:767:37:   required from ‘void Eigen::internal::call_dense_assignment_loop(DstXprType&, const SrcXprType&, const Functor&) [with DstXprType = Eigen::Matrix<double, -1, 1>; SrcXprType = Eigen::CwiseNullaryOp<Dem::indexing_functor_vector<Eigen::CwiseBinaryOp<Eigen::internal::scalar_max_op<double, double>, const Eigen::Matrix<double, -1, 1>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1> > >, Eigen::VectorBlock<Eigen::Array<int, -1, 1>, -1> >, Eigen::Matrix<double, -1, 1> >; Functor = Eigen::internal::assign_op<double, double>]’
    /home/syoyo/work/dem-bones/ExtLibs/Eigen/Eigen/src/Core/AssignEvaluator.h:926:31:   [ skipping 3 instantiation contexts, use -ftemplate-backtrace-limit=0 to disable ]
    /home/syoyo/work/dem-bones/ExtLibs/Eigen/Eigen/src/Core/AssignEvaluator.h:808:18:   required from ‘void Eigen::internal::call_assignment(Dst&, const Src&) [with Dst = Eigen::Matrix<double, -1, 1>; Src = Eigen::CwiseNullaryOp<Dem::indexing_functor_vector<Eigen::CwiseBinaryOp<Eigen::internal::scalar_max_op<double, double>, const Eigen::Matrix<double, -1, 1>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1> > >, Eigen::VectorBlock<Eigen::Array<int, -1, 1>, -1> >, Eigen::Matrix<double, -1, 1> >]’
    /home/syoyo/work/dem-bones/ExtLibs/Eigen/Eigen/src/Core/PlainObjectBase.h:779:32:   required from ‘Derived& Eigen::PlainObjectBase<Derived>::_set(const Eigen::DenseBase<OtherDerived>&) [with OtherDerived = Eigen::CwiseNullaryOp<Dem::indexing_functor_vector<Eigen::CwiseBinaryOp<Eigen::internal::scalar_max_op<double, double>, const Eigen::Matrix<double, -1, 1>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1> > >, Eigen::VectorBlock<Eigen::Array<int, -1, 1>, -1> >, Eigen::Matrix<double, -1, 1> >; Derived = Eigen::Matrix<double, -1, 1>]’
    /home/syoyo/work/dem-bones/ExtLibs/Eigen/Eigen/src/Core/Matrix.h:225:24:   required from ‘Eigen::Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>& Eigen::Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::operator=(const Eigen::DenseBase<OtherDerived>&) [with OtherDerived = Eigen::CwiseNullaryOp<Dem::indexing_functor_vector<Eigen::CwiseBinaryOp<Eigen::internal::scalar_max_op<double, double>, const Eigen::Matrix<double, -1, 1>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1> > >, Eigen::VectorBlock<Eigen::Array<int, -1, 1>, -1> >, Eigen::Matrix<double, -1, 1> >; _Scalar = double; int _Rows = -1; int _Cols = 1; int _Options = 0; int _MaxRows = -1; int _MaxCols = 1]’
    /home/syoyo/work/dem-bones/include/DemBones/DemBones.h:303:6:   required from ‘void Dem::DemBones<_Scalar, _AniMeshScalar>::computeWeights() [with _Scalar = double; _AniMeshScalar = float]’
    /home/syoyo/work/dem-bones/include/DemBones/DemBones.h:344:18:   required from ‘void Dem::DemBones<_Scalar, _AniMeshScalar>::compute() [with _Scalar = double; _AniMeshScalar = float]’
    /home/syoyo/work/dem-bones/src/command/mainCmd.cpp:171:16:   required from here
    /home/syoyo/work/dem-bones/include/DemBones/Indexing.h:106:30: warning: returning reference to temporary [-Wreturn-local-addr]
       return m_arg(m_indices[idx]);
    
    opened by syoyo 1
  • 20 bones are generated in the setting, but 1 bone is actually generated

    20 bones are generated in the setting, but 1 bone is actually generated

    企业微信截图_16717911703795

    Initializing bones: 1>1 Only 1 bone was generated in .fbx file Is there anything not set? How to generate 20 bones normally。 Looking forward to your reply

    opened by Wyhzxc 0
  • Patience & Tolerance values hard-coded to 3 and 1e-3 respectively

    Patience & Tolerance values hard-coded to 3 and 1e-3 respectively

    In class MyDemBones of mainCmd.cpp, the member variables tolerance and patience are initialized to (1e-3) and (3).

    However, these values are never updated to those passed in via the command line.

    It looks like the following lines must be added to the main function to ensure that the user-provided arguments are being respected:

    model.tolerance = dbOptions.tolerance;
    model.patience = dbOptions.patience;
    
    opened by RajatG-SideFX 0
  • Can it decompose multi sequences with the same initial pose in the meantime?

    Can it decompose multi sequences with the same initial pose in the meantime?

    I have some .abc files and the init pose .fbx.

    Do I need to concatenate all the motion sequences into one .abc file? or I can use some scripts to decompose multi sequences in the meantime?

    opened by kunkun0w0 0
  • Weird Composition Result

    Weird Composition Result

    image

    The script I used: call "../bin/Windows/DemBones.exe" -i="dress.fbx" -a="dress_shape00_01_01.abc" -b=40 -o="dress_40.fbx" -n=100 --nWeightsIters=3 -z=8

    The decomposition result looks terrible. Can you give some suggestions?

    image

    opened by kunkun0w0 0
  • Animation of the skeleton fitted to mesh animation with only rotation change in each keyframe

    Animation of the skeleton fitted to mesh animation with only rotation change in each keyframe

    Hi, First, I want to say that I'm very impressed with DemBones solution and its possibilities. I'm wondering if it is possible to obtain the output animation of the RIG with changed only the rotation in the joints (translation would be only applied to the root). From the documentation of the parameters, it is not obvious to me how to achieve this. There is a demLockparameter, but it seems to lock the whole transformation of the joint in the animation.

    opened by ArcaneShadow82 8
  • The dem bones llvm library  executable not working on M1 Macbooks

    The dem bones llvm library executable not working on M1 Macbooks

    The issue is as :

    [email protected] MacOS % ./dembones -a=a.abc -i=a.fbx -o=out.fbx -b=16 dyld: Library not loaded: /usr/local/opt/llvm/lib/libomp.dylib Referenced from: /Users/mac/Desktop/dem-bones-1.2.0/bin/MacOS/./dembones Reason: no suitable image found. Did find: /usr/local/opt/llvm/lib/libomp.dylib: mach-o, but wrong architecture /usr/local/opt/llvm/lib/libomp.dylib: mach-o, but wrong architecture zsh: abort ./dembones -a=a.abc -i=a.fbx -o=out.fbx -b=16

    opened by Tekh-ops 2
Releases(v1.2.1)
  • v1.2.1(Jun 17, 2021)

  • v1.2.0(Jul 21, 2020)

    • Add stop on convergence to cbIterEnd(), cbWeightsIterEnd(), cbTransformationsIterEnd()
    • Add --bindUpdate=2 flag in the command line tool to group joints
    • Add --tolerance and --patience flags in the command line tool using cbIterEnd()
    Source code(tar.gz)
    Source code(zip)
  • v1.1.0(Jul 9, 2020)

    • Add per-bone transformation hard-lock and per-vertex weights soft-lock
    • Fix initialization issue on cluster splitting
    • Support joint orientation in command line tools
    • Add command line tool for Linux
    • FBXSDK 2020, Alembic (from Maya 2020 DevKit)
    Source code(tar.gz)
    Source code(zip)
  • v1.0.1(Jun 18, 2020)

  • v1.0.0(Jun 18, 2020)

Self-Supervised Learning of Event-based Optical Flow with Spiking Neural Networks

Self-Supervised Learning of Event-based Optical Flow with Spiking Neural Networks Work accepted at NeurIPS'21 [paper, video]. If you use this code in

TU Delft 43 Dec 07, 2022
LabelImg is a graphical image annotation tool.

LabelImgPlus LabelImg is a graphical image annotation tool. This project is not updated with new functions now. More functions are supported with Labe

lzx1413 200 Dec 20, 2022
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

RIFE RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation Ported from https://github.com/hzwer/arXiv2020-RIFE Dependencies NumPy

49 Jan 07, 2023
Crowd-sourced Annotation of Human Motion.

Motion Annotation Tool Live: https://motion-annotation.humanoids.kit.edu Paper: The KIT Motion-Language Dataset Installation Start by installing all P

Matthias Plappert 4 May 25, 2020
No-reference Image Quality Assessment(NIQA) Algorithms (BRISQUE, NIQE, PIQE, RankIQA, MetaIQA)

No-Reference Image Quality Assessment Algorithms No-reference Image Quality Assessment(NIQA) is a task of evaluating an image without a reference imag

Dae-Young Song 26 Jan 04, 2023
SegTransVAE: Hybrid CNN - Transformer with Regularization for medical image segmentation

SegTransVAE: Hybrid CNN - Transformer with Regularization for medical image segmentation This repo is the official implementation for SegTransVAE. Seg

Nguyen Truong Hai 4 Aug 04, 2022
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

dm_control: DeepMind Infrastructure for Physics-Based Simulation. DeepMind's software stack for physics-based simulation and Reinforcement Learning en

DeepMind 3k Dec 31, 2022
PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

Libo Qin 25 Sep 06, 2022
This package contains a PyTorch Implementation of IB-GAN of the submitted paper in AAAI 2021

The PyTorch implementation of IB-GAN model of AAAI 2021 This package contains a PyTorch implementation of IB-GAN presented in the submitted paper (IB-

Insu Jeon 9 Mar 30, 2022
Parameter-ensemble-differential-evolution - Shows how to do parameter ensembling using differential evolution.

Ensembling parameters with differential evolution This repository shows how to ensemble parameters of two trained neural networks using differential e

Sayak Paul 9 May 04, 2022
A PyTorch library and evaluation platform for end-to-end compression research

CompressAI CompressAI (compress-ay) is a PyTorch library and evaluation platform for end-to-end compression research. CompressAI currently provides: c

InterDigital 680 Jan 06, 2023
Official implementation of "UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer"

[AAAI2022] UCTransNet This repo is the official implementation of "UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspectiv

Haonan Wang 199 Jan 03, 2023
Source code of our BMVC 2021 paper: AniFormer: Data-driven 3D Animation with Transformer

AniFormer This is the PyTorch implementation of our BMVC 2021 paper AniFormer: Data-driven 3D Animation with Transformer. Haoyu Chen, Hao Tang, Nicu S

24 Nov 02, 2022
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators

Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators This is our Pytorch implementation for t

RUCAIBox 12 Jul 22, 2022
This is an official implementation for "DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation"

DeciWatch: A Simple Baseline for 10× Efficient 2D and 3D Pose Estimation This repo is the official implementation of "DeciWatch: A Simple Baseline for

117 Dec 24, 2022
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification [NeurIPS 2021] Abstract Multiple instance learn

132 Dec 30, 2022
ML-Decoder: Scalable and Versatile Classification Head

ML-Decoder: Scalable and Versatile Classification Head Paper Official PyTorch Implementation Tal Ridnik, Gilad Sharir, Avi Ben-Cohen, Emanuel Ben-Baru

189 Jan 04, 2023
A large-scale video dataset for the training and evaluation of 3D human pose estimation models

ASPset-510 (Australian Sports Pose Dataset) is a large-scale video dataset for the training and evaluation of 3D human pose estimation models. It contains 17 different amateur subjects performing 30

Aiden Nibali 25 Jun 20, 2021
Gif-caption - A straightforward GIF Captioner written in Python

Broksy's GIF Captioner Have you ever wanted to easily caption a GIF without havi

3 Apr 09, 2022
Public repository of the 3DV 2021 paper "Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds"

Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds Björn Michele1), Alexandre Boulch1), Gilles Puy1), Maxime Bucher1) and Rena

valeo.ai 15 Dec 22, 2022