smooth: &smooth_keypoints weight: 50. module: myeasymocap.operations.loss.Smooth key_from_output: [keypoints, Th] key_from_infos: [] # TODO: 根据2D的置信度来计算smooth权重 args: keys: [Th, keypoints] smooth_type: [Linear, Linear] # 这个depth似乎需要相机参数进行转换 norm: [l2, l2] order: [2, 2] weights: [1000., 1000.] window_weight: [0.5, 0.3, 0.1, 0.1] module: myeasymocap.stages.basestage.MultiStage args: output: output/sv1p_hand at_step: detect_by_mediapipe: module: myeasymocap.backbone.mediapipe.hand.MediaPipe key_from_data: [images, imgnames] args: ckpt: models/mediapipe/hand_landmarker.task hand2d: module: myeasymocap.backbone.hand2d.hand2d.MyHand2D key_from_data: [images, imgnames] key_from_previous: [bbox] args: # ckpt: /nas/public/EasyMocapModels/hrnetv2_w18_coco_wholebody_hand_256x256-1c028db7_20210908.pth # ckpt: /nas/public/EasyMocapModels/hand/resnet_kp2d_clean.pt ckpt: models/hand_resnet_kp2d_clean.pt url: 1LTK7e9oAS6B3drmQyXwTZild6k87fEZa mode: resnet vis2d: module: myeasymocap.io.vis.Vis2D skip: False key_from_data: [images] key_from_previous: [keypoints, bbox] args: name: vis_keypoints2d scale: 0.5 infer_mano: # module: myeasymocap.backbone.hmr.hmr.MyHMR key_from_data: [images, imgnames] key_from_previous: [bbox] key_keep: [meta, cameras, imgnames] # 将这些参数都保留到最后的输出中 args: ckpt: models/hand_manol_pca45_noflat.ckpt url: '1KTi_oJ_udLRK3WZ3xyHzBUd6vKAApfT8' # TODO: add visualize for Init MANO at_final: load_hand_model: # 载入身体模型 module: myeasymocap.io.model.MANOLoader args: cfg_path: config/model/manol.yml model_path: models/manov1.2/MANO_LEFT.pkl #models/handmesh/data/MANO_RIGHT.pkl # load mano model regressor_path: models/manov1.2/J_regressor_mano_LEFT.txt #models/handmesh/data/J_regressor_mano_RIGHT.txt num_pca_comps: 45 use_pca: True use_flat_mean: False # 这个模块返回两个内容:body_model, model; 其中的body_model是用来进行可视化的 mean_param: # 初始化姿态,这里将poses和shapes都进行平均 module: myeasymocap.operations.init.MeanShapes key_from_data: [params] args: keys: ['shapes'] init_T: # 初始化每一帧的位置 module: myeasymocap.operations.optimizer.Optimizer key_from_data: [keypoints, cameras, params] key_from_previous: [model] args: optimizer_args: {optim_type: lbfgs} optimize_keys: [Th] loss: repro: weight: 100. module: myeasymocap.operations.loss.Keypoints2D key_from_output: [keypoints] key_from_infos: [keypoints, cameras] args: norm: l2 smooth: *smooth_keypoints init_R: # 初始化每一帧的旋转 module: myeasymocap.operations.optimizer.Optimizer key_from_data: [keypoints, cameras] key_from_previous: [model, params] args: optimizer_args: {optim_type: lbfgs} optimize_keys: [Rh] loss: repro: weight: 100. module: myeasymocap.operations.loss.Keypoints2D key_from_output: [keypoints] key_from_infos: [keypoints, cameras] args: norm: l2 smooth: *smooth_keypoints refine_poses: # 优化poses repeat: 2 module: myeasymocap.operations.optimizer.Optimizer key_from_data: [keypoints, cameras] key_from_previous: [model, params] args: optimizer_args: {optim_type: lbfgs} optimize_keys: [poses, shapes, Rh, Th] loss: repro: weight: 100. module: myeasymocap.operations.loss.Keypoints2D key_from_output: [keypoints] key_from_infos: [keypoints, cameras] args: norm: l1 reg: weight: 0.001 module: myeasymocap.operations.loss.RegLoss key_from_output: [poses] key_from_infos: [] args: key: poses norm: l2 smooth: *smooth_keypoints write: module: myeasymocap.io.write.WriteSMPL key_from_data: [meta] key_from_previous: [params, model] args: name: smpl render: module: myeasymocap.io.vis3d.Render_multiview key_from_data: [cameras, imgnames] key_from_previous: [hand_model, params] args: model_name: hand_model backend: pyrender view_list: [0] scale: 0.5 make_video: module: myeasymocap.io.video.MakeVideo args: fps: 50 keep_image: False