107 lines
4.0 KiB
YAML
107 lines
4.0 KiB
YAML
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init_params: # 初始化姿态
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module: myeasymocap.operations.init.InitParams
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key_from_data: [keypoints3d]
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args:
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num_poses: 69
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num_shapes: 10
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fitShape: # 这一步需要根据骨长优化一下SMPL的shape参数
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module: myeasymocap.operations.optimizer.Optimizer
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key_from_data: [keypoints3d]
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key_from_previous: [model, params] # 这一步优化所使用的model，是一个可调用的函数，负责把params的输入变成输出，而不用考虑其他，与SMPL model是不一样的
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args:
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optimizer_args: {optim_type: lbfgs}
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optimize_keys: [shapes]
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loss:
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k3d:
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weight: 1000.
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module: myeasymocap.operations.loss.LimbLength
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key_from_output: [keypoints]
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key_from_infos: [keypoints3d]
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args:
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kintree: [[8, 1], [2, 5], [2, 3], [5, 6], [3, 4], [6, 7], [2, 3], [5, 6], [3, 4], [6, 7], [2, 3], [5, 6], [3, 4], [6, 7], [1, 0], [9, 12], [9, 10], [10, 11], [12, 13],[13, 14]]
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regshape:
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weight: 0.1
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module: myeasymocap.operations.loss.RegLoss
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key_from_output: [shapes]
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key_from_infos: [] # TODO: 根据2D的置信度来计算smooth权重
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args:
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key: shapes
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norm: l2
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init_RT: # 这一步中，首先将SMPL参数的shape参数进行整段平均。重新优化更新RT参数
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module: myeasymocap.operations.optimizer.Optimizer
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key_from_data: [keypoints3d]
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key_from_previous: [model, params] # 这一步优化所使用的model，是一个可调用的函数，负责把params的输入变成输出，而不用考虑其他，与SMPL model是不一样的
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# 这样设计的目的是对于一些不只是SMPL本身的模型，可以在外面套一层接口
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# model是一个纯函数，用来进行可视化
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args:
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optimizer_args: {optim_type: lbfgs}
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optimize_keys: [Th, Rh]
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loss:
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k3d:
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weight: 100.
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module: myeasymocap.operations.loss.Keypoints3D
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key_from_output: [keypoints]
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key_from_infos: [keypoints3d]
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args:
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norm: l2
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index_est: [2, 5, 9, 12]
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index_gt: [2, 5, 9, 12]
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smooth:
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weight: 1.
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module: myeasymocap.operations.loss.Smooth
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key_from_output: [Th, keypoints]
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key_from_infos: [] # TODO: 根据2D的置信度来计算smooth权重
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args:
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keys: [keypoints, Th]
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smooth_type: [Linear, Linear] # 这个depth似乎需要相机参数进行转换
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norm: [l2, l2]
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order: [2, 2]
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weights: [10., 100.]
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window_weight: [0.5, 0.3, 0.1, 0.1]
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refine_poses:
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repeat: 2
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module: myeasymocap.operations.optimizer.Optimizer
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key_from_data: [keypoints3d]
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key_from_previous: [model, params]
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args:
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optimizer_args: {optim_type: lbfgs}
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optimize_keys: [[poses, Rh, Th], [poses, shapes, Rh, Th]]
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loss:
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k3d:
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weight: 1000.
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module: myeasymocap.operations.loss.Keypoints3D
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key_from_output: [keypoints]
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key_from_infos: [keypoints3d]
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args:
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norm: l2
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norm_info: 0.02
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ranges_est: [0, 25]
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ranges_gt: [0, 25]
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smooth:
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weight: 1.
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module: myeasymocap.operations.loss.Smooth
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key_from_output: [poses, Th, keypoints]
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key_from_infos: [] # TODO: 根据2D的置信度来计算smooth权重
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args:
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keys: [Th, poses, keypoints]
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smooth_type: [Linear, Linear, Linear] # 这个depth似乎需要相机参数进行转换
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norm: [l2, l2, l2]
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order: [2, 2, 2]
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weights: [10., 10., 10.,]
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window_weight: [0.5, 0.3, 0.1, 0.1]
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prior:
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weight: 0.1
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module: easymocap.multistage.gmm.GMMPrior
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key_from_output: [poses]
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key_from_infos: []
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args:
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start: 0
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end: 69
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regshape:
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weight: 0.1
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module: myeasymocap.operations.loss.RegLoss
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key_from_output: [shapes]
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key_from_infos: [] # TODO: 根据2D的置信度来计算smooth权重
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args:
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key: shapes
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norm: l2
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