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.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+objects_occ/background.blend filter=lfs diff=lfs merge=lfs -text
+objects_occ/background.obj filter=lfs diff=lfs merge=lfs -text
+static/room.glb filter=lfs diff=lfs merge=lfs -text

Checkpoints/TRUMANS_mask_ind[0]_timesteps[100]_fixed_frame[2]_ac_type[last_add_first_token]_no_scene[False]_no_action[False]_batch_size[256]_epoch360.pth

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Checkpoints/model_joints_to_smpl_wrist.pth

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Data_blocks_motion_all/Object/basin.npy

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Data_blocks_motion_all/Object/bed.npy

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Data_blocks_motion_all/Object/flower.npy

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Data_blocks_motion_all/Object/kitchen_chair_1.npy

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Data_blocks_motion_all/Object/kitchen_chair_2.npy

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Data_blocks_motion_all/Object/office_chair.npy

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Data_blocks_motion_all/Object/sofa.npy

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Data_blocks_motion_all/Object/table.npy

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Data_blocks_motion_all/Object/wc.npy

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Data_blocks_motion_all/Scene/2a8a1191-d4cc-46e4-b5ea-65e01954dbfa.npy

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Data_blocks_motion_all/Scene/background.npy

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Data_blocks_motion_all/meta.npy

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Data_blocks_motion_all/norm.npy

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Dockerfile

@@ -0,0 +1,14 @@
+FROM python:3.9
+
+WORKDIR /app
+
+COPY ./requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /app/requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple --extra-index-url https://download.pytorch.org/whl/cu113
+
+COPY . .
+
+ENV FLASK_APP=app.py
+
+CMD [ "python3", "-m" , "flask", "run", "--host=0.0.0.0"]
+
+

app.py

@@ -0,0 +1,32 @@
+# app.py
+import os
+# import numpy as np
+from flask import Flask, jsonify, request, render_template
+from sample_hsi import sample_wrapper
+# from omegaconf import OmegaConf
+# from hydra import compose, initialize
+
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/move_cube', methods=['POST'])
+def move_cube():
+    print(os.getcwd())
+    data = request.json
+    trajectory = data['trajectory']
+    print(data)
+    obj_locs = {obj_name.split('.')[0]: data[obj_name] for obj_name in data.keys() if 'trajectory' not in obj_name}
+
+    res = sample_wrapper(trajectory, obj_locs)
+
+    return jsonify(res)
+
+if __name__ == '__main__':
+    # os.environ["HYDRA_FULL_ERROR"] = "1"
+    # initialize(version_base=None, config_path="./config")
+    # OmegaConf.register_new_resolver("times", lambda x, y: int(x) * int(y))
+    app.run(debug=True)

config/config_sample_synhsi.yaml

@@ -0,0 +1,86 @@
+
+exp_name: Test
+train: false
+batch_size: 1
+device: cuda
+interp_s: 3
+
+len_pre: 4
+len_act: 2
+action_type: 'none'
+scene_name: 'background'
+action_id: 1
+stay_and_act: false
+
+method_name: Test
+continue_last: false
+
+#exp_dir: ${oc.env:ROOT_DIR}/Experiments/${exp_name}
+ckpt_dir: ./Checkpoints
+smpl_dir: ./smpl_models
+#test_dir: ${oc.env:ROOT_DIR}/Test_settings
+
+num_gpus: 1
+num_workers: 0
+
+dataset:
+  folder: ./Data_blocks_motion_all
+  device: cuda
+  batch_size: 1
+  seq_len: 16
+  step: 3
+  nb_voxels: 32
+  mesh_grid: [ -0.6, 0.6, 0, 1.2, -0.6, 0.6 ]
+  train: false
+  load_scene: true
+  load_action: true
+
+  joints_ind: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 40 ]
+  nb_joints: 24
+  nb_actions: 10
+
+guidance:
+  pelvis:
+    seq_len: 16
+    step: 3
+    mask_ind: 0
+    fixed_frame: 2
+    mask_y: false
+    emb_f: -1
+    no_scene: false
+    no_action: false
+    fix_mode: true
+
+model:
+  model_smplx:
+    input_dim: 72
+    output_dim: 132
+    hidden_dim: 64
+    ckpt: ./Checkpoints/model_joints_to_smpl_wrist.pth
+  synhsi_body:
+    dim_model: 512
+    num_heads: 16
+    num_layers: 8
+    dropout_p: 0.1
+    nb_voxels: 32
+    free_p: 0
+    ac_type: last_add_first_token
+    dim_input: 72
+    dim_output: 72
+    nb_actions: 10
+    no_scene: false
+    no_action: false
+    ckpt: ./Checkpoints/TRUMANS_mask_ind[0]_timesteps[100]_fixed_frame[2]_ac_type[last_add_first_token]_no_scene[False]_no_action[False]_batch_size[256]_epoch360.pth
+
+sampler:
+  pelvis:
+    _target_: models.synhsi.Sampler
+    device: cuda
+    mask_ind: 0
+    emb_f: -1
+    batch_size: 1
+    seq_len: 16
+    channel: 72
+    fix_mode: true
+    timesteps: 100
+    fixed_frame: 2

constants.py

@@ -0,0 +1,93 @@
+import numpy as np
+import os
+
+
+try:
+    ROOT_DIR = os.environ['ROOT_DIR']
+except:
+    ROOT_DIR = '/home/jiangnan/SyntheticHSI/'
+DATA_DIR = os.path.join(ROOT_DIR, 'Data_augment', 'Data_blocks_motion_all')
+CKPT_DIR = os.path.join(ROOT_DIR, 'HSIScripts', 'motion_gen_diffusion', 'checkpoints')
+SMPL_DIR = os.path.join(ROOT_DIR, 'smpl_models')
+
+OBJ_ACT_DICT = {
+    'lie down': 0,
+    'squat': 1,
+    'mouse': 2,
+    'keyboard': 3,
+    'laptop': 4,
+    'phone': 5,
+    'book': 6,
+    'bottle': 7,
+    'pen': 8,
+    'vase': 9,
+}
+
+
+CC_BONE_NAMES = ['CC_Base_Hip', 'CC_Base_Pelvis',
+                 'CC_Base_Waist', 'CC_Base_Spine01', 'CC_Base_Spine02',
+                 'CC_Base_NeckTwist01', 'CC_Base_NeckTwist02', 'CC_Base_Head',
+
+                 'CC_Base_R_Clavicle', 'CC_Base_R_Upperarm', 'CC_Base_R_Forearm', 'CC_Base_R_Hand',
+
+                 'CC_Base_R_Mid1', 'CC_Base_R_Mid2', 'CC_Base_R_Mid3', 'CC_Base_R_Ring1',
+                 'CC_Base_R_Ring2', 'CC_Base_R_Ring3', 'CC_Base_R_Pinky1', 'CC_Base_R_Pinky2',
+                 'CC_Base_R_Pinky3', 'CC_Base_R_Index1', 'CC_Base_R_Index2', 'CC_Base_R_Index3',
+                 'CC_Base_R_Thumb1', 'CC_Base_R_Thumb2', 'CC_Base_R_Thumb3',
+
+                 'CC_Base_L_Clavicle', 'CC_Base_L_Upperarm', 'CC_Base_L_Forearm', 'CC_Base_L_Hand',
+                 'CC_Base_L_Mid1', 'CC_Base_L_Mid2', 'CC_Base_L_Mid3',
+                 'CC_Base_L_Ring1', 'CC_Base_L_Ring2', 'CC_Base_L_Ring3',
+                 'CC_Base_L_Pinky1', 'CC_Base_L_Pinky2', 'CC_Base_L_Pinky3',
+                 'CC_Base_L_Index1', 'CC_Base_L_Index2', 'CC_Base_L_Index3', 'CC_Base_L_Thumb1',
+                 'CC_Base_L_Thumb2', 'CC_Base_L_Thumb3', 'CC_Base_R_Thigh',
+                 'CC_Base_R_Calf', 'CC_Base_R_Foot',
+                 'CC_Base_L_Thigh',
+                 'CC_Base_L_Calf', 'CC_Base_L_Foot',
+                 'CC_Base_R_ToeBase',
+                 'CC_Base_L_ToeBase',
+                 ]
+
+SMPLX_JOINT_NAMES = [
+    'pelvis','left_hip','right_hip','spine1','left_knee','right_knee','spine2','left_ankle','right_ankle','spine3', 'left_foot','right_foot','neck','left_collar','right_collar','head','left_shoulder','right_shoulder','left_elbow', 'right_elbow','left_wrist','right_wrist',
+    'jaw','left_eye_smplhf','right_eye_smplhf','left_index1','left_index2','left_index3','left_middle1','left_middle2','left_middle3','left_pinky1','left_pinky2','left_pinky3','left_ring1','left_ring2','left_ring3','left_thumb1','left_thumb2','left_thumb3','right_index1','right_index2','right_index3','right_middle1','right_middle2','right_middle3','right_pinky1','right_pinky2','right_pinky3','right_ring1','right_ring2','right_ring3','right_thumb1','right_thumb2','right_thumb3'
+]
+
+# SMPL_MODEL_FOLDER = '/home/jiangnan/AHOI_cvpr/smpl_models'
+SMPL_MODEL_FOLDER = '/home/jiangnan/SyntheticHSI/smpl_models'
+
+rest_pelvis = np.matrix([[0.0000e+00,  0.0000e+00,  0.0000e+00],
+                             [5.6144e-02, -9.4542e-02, -2.3475e-02],
+                             [-5.7870e-02, -1.0517e-01, -1.6559e-02]])
+pelvis_shift = [0.001144, -0.366919, 0.012666]
+
+relaxed_hand_pose = np.array([0.11168, 0.04289, -0.41644,
+                     0.10881, -0.06599, -0.75622,
+                     -0.09639, -0.09092, -0.18846,
+                     -0.1181, 0.05094, -0.52958,
+                     -0.1437, 0.05524, -0.70486,
+                     -0.01918, -0.09234, -0.33791,
+                     -0.45703, -0.19628, -0.62546,
+                     -0.21465, -0.066, -0.50689,
+                     -0.36972, -0.06034, -0.07949,
+                     -0.14187, -0.08585, -0.63553,
+                     -0.30334, -0.05788, -0.63139,
+                     -0.17612, -0.13209, -0.37335,
+                     0.85096, 0.27692, -0.09155,
+                     -0.49984, 0.02656, 0.05288,
+                     0.53556, 0.04596, -0.27736,
+                     0.11168, -0.04289, 0.41644,
+                     0.10881, 0.06599, 0.75622,
+                     -0.09639, 0.09092, 0.18846,
+                     -0.1181, -0.05094, 0.52958,
+                     -0.1437, -0.05524, 0.70486,
+                     -0.01918, 0.09234, 0.33791,
+                     -0.45703, 0.19628, 0.62546,
+                     -0.21465, 0.066, 0.50689,
+                     -0.36972, 0.06034, 0.07949,
+                     -0.14187, 0.08585, 0.63553,
+                     -0.30334, 0.05788, 0.63139,
+                     -0.17612, 0.13209, 0.37335,
+                     0.85096, -0.27692, 0.09155,
+                     -0.49984, -0.02656, -0.05288,
+                     0.53556, -0.04596, 0.27736]).astype(np.float32)

datasets/__init__.py

@@ -0,0 +1 @@
+__all__ = ['trumans']

datasets/trumans.py

@@ -0,0 +1,228 @@
+import os
+import torch
+import numpy as np
+from scipy.spatial.transform import Rotation as R
+from torch.utils.data import Dataset, DataLoader, Subset
+
+
+class TrumansDataset(Dataset):
+    def __init__(self, folder, device, mesh_grid, batch_size=1, seq_len=32, step=1, nb_voxels=32, train=True, load_scene=True, load_action=True, no_objects=False, **kwargs):
+        self.device = device
+        self.train = train
+        self.load_scene = load_scene
+        self.load_action = load_action
+
+        # self.body_pose = np.load(os.path.join(folder, 'human_pose.npy'))
+        # self.transl = np.load(os.path.join(folder, 'human_transl.npy'))
+        # self.global_orient = np.load(os.path.join(folder, 'human_orient.npy'))
+        # self.motion_ind = np.load(os.path.join(folder, 'idx_start.npy'))
+        # self.joints = np.load(os.path.join(folder, 'human_joints.npy'))
+        # self.file_blend = np.load(os.path.join(folder, 'file_blend.npy'))
+
+        self.seq_len=seq_len
+        self.step = step
+        self.batch_size = batch_size
+
+        # if self.load_action:
+        #     self.action_label = np.load(os.path.join(folder, 'action_label.npy')).astype(np.float32)
+
+        if self.load_scene:
+            self.mesh_grid = mesh_grid
+            self.nb_voxels = nb_voxels
+            self.no_objects = no_objects
+            self.nb_voxels = nb_voxels
+            self.scene_occ = []
+            self.scene_dict = {}
+
+            self.scene_folder = os.path.join(folder, 'Scene')
+            # self.scene_flag = np.load(os.path.join(folder, 'scene_flag.npy'))
+            if not no_objects:
+                # self.object_flag = np.load(os.path.join(folder, 'object_flag.npy'))
+                # self.object_mat = np.load(os.path.join(folder, 'object_mat.npy'))
+                self.object_occ = {}
+                self.object_folder = os.path.join(folder, 'Object')
+                for file in sorted(os.listdir(self.object_folder)):
+                    print(f"Loading object occupied coordinates {file}")
+                    obj_name = file.replace('.npy', '')
+                    self.object_occ[obj_name] = torch.from_numpy(np.load(os.path.join(self.object_folder, file))).to(device)
+
+            for sid, file in enumerate(sorted(os.listdir(self.scene_folder))):
+                # if scene_name != '' and scene_name not in file:
+                #     continue
+                print(f"{sid} Loading Scene Mesh {file}")
+                scene_occ = np.load(os.path.join(self.scene_folder, file))
+                scene_occ = torch.from_numpy(scene_occ).to(device=device, dtype=bool)
+                self.scene_occ.append(scene_occ)
+                self.scene_dict[file] = sid
+            self.scene_occ = torch.stack(self.scene_occ)
+
+            self.scene_grid_np = np.array([-3, 0, -4, 3, 2, 4, 300, 100, 400])
+            self.scene_grid_torch = torch.tensor([-3, 0, -4, 3, 2, 4, 300, 100, 400]).to(device)
+            self.batch_id = torch.linspace(0, batch_size - 1, batch_size).tile((nb_voxels ** 3, 1)).T\
+                .reshape(-1, 1).to(device=device, dtype=torch.long)
+            self.batch_id_obj = torch.linspace(0, batch_size - 1, batch_size).tile((9000, 1)).T \
+                .reshape(-1, 1).to(device=device, dtype=torch.long)
+
+         # TODO CHANGE STEP
+        norm = np.load(os.path.join(folder, 'norm.npy'), allow_pickle=True).item()[f'{seq_len, 3}']
+        self.min = norm[0].astype(np.float32)
+        self.max = norm[1].astype(np.float32)
+        self.min_torch = torch.tensor(self.min).to(device)
+        self.max_torch = torch.tensor(self.max).to(device)
+
+
+    def add_object_points(self, points, occ):
+        points = points.reshape(-1, 3)
+        voxel_size = torch.div(self.scene_grid_torch[3: 6] - self.scene_grid_torch[:3], self.scene_grid_torch[6:])
+        voxel = torch.div((points - self.scene_grid_torch[:3]), voxel_size)
+        voxel = voxel.to(dtype=torch.long)
+        # voxel = rearrange(voxel, 'b p c -> (b p) c')
+        lb = torch.all(voxel >= 0, dim=-1)
+        ub = torch.all(voxel < self.scene_grid_torch[6:] - 0, dim=-1)
+        in_bound = torch.logical_and(lb, ub)
+        # voxel = torch.cat([self.batch_id_obj, voxel], dim=-1)
+        voxel = voxel[in_bound]
+        occ[0, voxel[:, 0], voxel[:, 1], voxel[:, 2]] = True
+
+    def get_occ_for_points(self, points, obj_locs, scene_flag):
+
+        #TODO
+
+        # points_new = points.reshape(-1, 3)
+        # center_xz = points_new[:, [0, 2]].mean(axis=0)
+        # if torch.norm(center_xz) > 0.:
+        #     occ_for_points = torch.load('occ_for_points_at_clear_space.pt').to(points.device)
+        #     return occ_for_points
+
+
+        if isinstance(scene_flag, str):
+            for k, v in self.scene_dict.items():
+                if scene_flag in k:
+                    scene_flag = [v]
+                    break
+        batch_size = points.shape[0]
+        seq_len = points.shape[1]
+        points = points.reshape(-1, 3)
+        voxel_size = torch.div(self.scene_grid_torch[3: 6] - self.scene_grid_torch[:3], self.scene_grid_torch[6:])
+        voxel = torch.div((points - self.scene_grid_torch[:3]), voxel_size)
+        voxel = voxel.to(dtype=torch.long)
+        # voxel = rearrange(voxel, 'b p c -> (b p) c')
+        lb = torch.all(voxel >= 0, dim=-1)
+        ub = torch.all(voxel < self.scene_grid_torch[6:] - 0, dim=-1)
+        in_bound = torch.logical_and(lb, ub)
+        voxel[torch.logical_not(in_bound)] = 0
+        voxel = torch.cat([self.batch_id, voxel], dim=1)
+        occ = self.scene_occ[scene_flag]
+
+        #TODO
+
+        # occ[:] = False
+        # occ[:, :, 0, :] = True
+
+        # import cv2
+        # img = occ[0, :, 10, :].detach().cpu().numpy()
+        # im = np.zeros((300, 400))
+        # im[img] = 255
+        # cv2.imwrite('gray.jpg', im.T)
+        if obj_locs:
+            for obj_name, obj_loc in obj_locs.items():
+                obj_points = self.object_occ[obj_name].clone()
+                obj_points[:, 0] += obj_loc['x']
+                obj_points[:, 2] += obj_loc['z']
+                # import pdb
+                # pdb.set_trace()
+                self.add_object_points(obj_points, occ)
+        occ_for_points = occ[voxel[:, 0], voxel[:, 1], voxel[:, 2], voxel[:, 3]]
+        occ_for_points[torch.logical_not(in_bound)] = True
+        occ_for_points = occ_for_points.reshape(batch_size, seq_len, -1)
+
+        # torch.save(occ_for_points, 'occ_for_points_at_clear_space.pt')
+
+        # occ_for_points = torch.ones(batch_size, seq_len, 22).to('cuda')
+
+
+        return occ_for_points
+
+    def create_meshgrid(self, batch_size=1):
+        bbox = self.mesh_grid
+        size = (self.nb_voxels, self.nb_voxels, self.nb_voxels)
+        x = torch.linspace(bbox[0], bbox[1], size[0])
+        y = torch.linspace(bbox[2], bbox[3], size[1])
+        z = torch.linspace(bbox[4], bbox[5], size[2])
+        xx, yy, zz = torch.meshgrid(x, y, z, indexing='ij')
+        grid = torch.stack([xx, yy, zz], dim=-1).reshape(-1, 3)
+        grid = grid.repeat(batch_size, 1, 1)
+
+        # aug_z = 0.75 + torch.rand(batch_size, 1) * 0.35
+        # grid[:, :, 2] = grid[:, :, 2] * aug_z
+
+        return grid
+
+
+    @staticmethod
+    def combine_mesh(vert_list, face_list):
+        assert len(vert_list) == len(face_list)
+        verts = None
+        faces = None
+        for v, f in zip(vert_list, face_list):
+            if verts is None:
+                verts = v
+                faces = f
+            else:
+                f = f + verts.shape[0]
+                verts = torch.cat([verts, v])
+                faces = torch.cat([faces, f])
+
+        return verts, faces
+
+    @staticmethod
+    def transform_mesh(vert_list, trans_mats):
+        assert len(vert_list) == len(trans_mats)
+        vert_list_new = []
+        for v, m in zip(vert_list, trans_mats):
+            v = v @ m[:3, :3].T + m[:3, 3]
+            vert_list_new.append(v)
+        vert_list_new = torch.stack(vert_list_new)
+
+        return vert_list_new
+
+    def __len__(self):
+        return len(self.motion_ind)
+
+
+    def normalize(self, data):
+        shape_orig = data.shape
+        data = data.reshape((-1, 3))
+        # data = (data - self.mean) / self.std
+        data = -1. + 2. * (data - self.min) / (self.max - self.min)
+        data = data.reshape(shape_orig)
+
+        return data
+
+    def normalize_torch(self, data):
+        shape_orig = data.shape
+        data = data.reshape((-1, 3))
+        # data = (data - self.mean) / self.std
+        data = -1. + 2. * (data - self.min_torch) / (self.max_torch - self.min_torch)
+        data = data.reshape(shape_orig)
+
+        return data
+
+    def denormalize(self, data):
+        shape_orig = data.shape
+        data = data.reshape((-1, 3))
+        # data = data * self.std + self.mean
+        data = (data + 1.) * (self.max - self.min) / 2. + self.min
+        data = data.reshape(shape_orig)
+
+        return data
+
+    def denormalize_torch(self, data):
+        shape_orig = data.shape
+        data = data.reshape((-1, 3))
+        # data = data * self.std + self.mean
+        import pdb
+        data = (data + 1.) * (self.max_torch - self.min_torch) / 2. + self.min_torch
+        data = data.reshape(shape_orig)
+
+        return data

models/__init__.py

@@ -0,0 +1,2 @@
+
+__all__ = ['synhsi', 'joints_to_smplx', 'goal_model', 'imos_model']

models/joints_to_smplx.py

@@ -0,0 +1,124 @@
+import torch
+import smplx
+from constants import *
+from scipy.interpolate import interp1d
+from torch import nn, einsum
+import pytorch3d as T
+
+
+class JointsToSMPLX(nn.Module):
+    def __init__(self, input_dim, output_dim, hidden_dim, **kwargs):
+        super().__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(input_dim, hidden_dim),
+            nn.BatchNorm1d(hidden_dim),
+            nn.ReLU(),
+            # nn.Linear(hidden_dim, hidden_dim),
+            # nn.BatchNorm1d(hidden_dim),
+            # nn.ReLU(),
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.BatchNorm1d(hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, output_dim),
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+def optimize_smpl(pose_pred, joints, joints_ind, hand_pca=45):
+    device = joints.device
+    len = joints.shape[0]
+
+    smpl_model = smplx.create('./smpl_models', model_type='smplx',
+                              gender='male', ext='npz',
+                              num_betas=10,
+                              use_pca=False,
+                              create_global_orient=True,
+                              create_body_pose=True,
+                              create_betas=True,
+                              create_left_hand_pose=True,
+                              create_right_hand_pose=True,
+                              create_expression=True,
+                              create_jaw_pose=True,
+                              create_leye_pose=True,
+                              create_reye_pose=True,
+                              create_transl=True,
+                              batch_size=len,
+                              ).to(device)
+    smpl_model.eval()
+
+    # weights = torch.tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 100, 100, 100, 100]).reshape(nb_joints, 1).repeat(1, 3).to(device)
+    joints = joints.reshape(len, -1, 3) + torch.tensor(pelvis_shift).to(device)
+    pose_input = torch.nn.Parameter(pose_pred.detach(), requires_grad=True)
+    transl = torch.nn.Parameter(torch.zeros(pose_pred.shape[0], 3).to(device), requires_grad=True)
+    # left_hand = torch.nn.Parameter(torch.zeros(pose_pred.shape[0], hand_pca).to(device), requires_grad=True)
+    # right_hand = torch.nn.Parameter(torch.zeros(pose_pred.shape[0], hand_pca).to(device), requires_grad=True)
+    left_hand = torch.from_numpy(relaxed_hand_pose[:45].reshape(1, -1).repeat(pose_pred.shape[0], axis=0)).to(device)
+    right_hand = torch.from_numpy(relaxed_hand_pose[45:].reshape(1, -1).repeat(pose_pred.shape[0], axis=0)).to(device)
+    optimizer = torch.optim.Adam(params=[pose_input, transl], lr=0.05)
+    loss_fn = nn.MSELoss()
+    vertices_output = None
+    for step in range(100):
+        smpl_output = smpl_model(transl=transl, body_pose=pose_input[:, 3:], global_orient=pose_input[:, :3], return_verts=True,
+                                 left_hand_pose=left_hand,# @ left_hand_components[:hand_pca],
+                                 right_hand_pose=right_hand,# @ right_hand_components[:hand_pca],
+                                 )
+        joints_output = smpl_output.joints[:, joints_ind].reshape(len, -1, 3)
+        vertices_output = smpl_output.vertices[:, ::10].detach().cpu().numpy()
+        loss = loss_fn(joints[:, :], joints_output[:, :])
+        # loss = torch.mean((joints - joints_output) ** 2 * weights)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        print(loss.item())
+
+
+
+    #left_hand = left_hand @ left_hand_components[:hand_pca]
+    #right_hand = right_hand @ right_hand_components[:hand_pca]
+
+    return pose_input.detach().cpu().numpy(), transl.detach().cpu().numpy(), left_hand.detach().cpu().numpy(), right_hand.detach().cpu().numpy(), vertices_output
+
+
+def joints_to_smpl(model, joints, joints_ind, interp_s):
+    joints = interpolate_joints(joints, scale=interp_s)
+    # joints = interpolate_joints(joints, scale=0.33)
+    # joints = interpolate_joints(joints, scale=interp_s * 3)
+    input_len = joints.shape[0]
+    joints = joints.reshape(input_len, -1, 3)
+    joints = joints.permute(1, 0, 2)
+    trans_np = joints[0].detach().cpu().numpy()
+    joints = joints - joints[0]
+    joints = joints.permute(1, 0, 2)
+    joints = joints.reshape(input_len, -1)
+    pose_pred = model(joints)
+    pose_pred = pose_pred.reshape(-1, 6)
+    pose_pred = T.matrix_to_axis_angle(T.rotation_6d_to_matrix(pose_pred)).reshape(input_len, -1)
+    # pose_pred = pose_pred[:seq_len]
+    pose_output, transl, left_hand, right_hand, vertices = optimize_smpl(pose_pred, joints, joints_ind)
+
+    transl = trans_np - np.array(pelvis_shift) + transl
+
+    vertices = vertices + transl.reshape(-1, 1, 3)
+
+
+    return pose_output, transl, left_hand, right_hand, vertices
+
+
+def interpolate_joints(joints, scale):
+    if scale == 1:
+        return joints
+    device = joints.device
+    joints = joints.detach().cpu().numpy()
+    in_len = joints.shape[0]
+    out_len = int(in_len * scale)
+    joints = joints.reshape(in_len, -1)
+    x = np.array(range(in_len))
+    xnew = np.linspace(0, in_len - 1, out_len)
+    f = interp1d(x, joints, axis=0)
+    joints_new = f(xnew)
+    joints_new = torch.from_numpy(joints_new).to(device).float()
+
+    return  joints_new

models/synhsi.py

@@ -0,0 +1,444 @@
+import math
+import pdb
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from vit_pytorch import ViT
+from tqdm import tqdm
+from utils import *
+
+
+class Sampler:
+    def __init__(self, device, mask_ind, emb_f, batch_size, seq_len, channel, fix_mode, timesteps, fixed_frame, **kwargs):
+        self.device = device
+        self.mask_ind = mask_ind
+        self.emb_f = emb_f
+        self.batch_size = batch_size
+        self.seq_len = seq_len
+        self.channel = channel
+        self.fix_mode = fix_mode
+        self.timesteps = timesteps
+        self.fixed_frame = fixed_frame
+        self.get_scheduler()
+
+    def set_dataset_and_model(self, dataset, model):
+        self.dataset = dataset
+        if dataset.load_scene:
+            self.grid = dataset.create_meshgrid(batch_size=self.batch_size).to(self.device)
+        self.model = model
+
+
+    def get_scheduler(self):
+        betas = linear_beta_schedule(timesteps=self.timesteps)
+
+        # define alphas
+        alphas = 1. - betas
+        alphas_cumprod = torch.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value=1.0)
+        self.sqrt_recip_alphas = torch.sqrt(1.0 / alphas)
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
+        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod)
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        self.posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        self.betas = betas
+
+    def q_sample(self, x_start, t, noise):
+        if noise is None:
+            noise = torch.randn_like(x_start)
+        sqrt_alphas_cumprod_t = extract(self.sqrt_alphas_cumprod, t, x_start.shape)
+        sqrt_one_minus_alphas_cumprod_t = extract(
+            self.sqrt_one_minus_alphas_cumprod, t, x_start.shape
+        )
+        return sqrt_alphas_cumprod_t * x_start + sqrt_one_minus_alphas_cumprod_t * noise
+
+
+    def p_losses(self, x_start, obj_points, mat, scene_flag, mask, t, action_label, noise=None, loss_type='huber'):
+        if noise is None:
+            noise = torch.randn_like(x_start)
+
+        noise[mask] = 0.
+
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+
+        if self.dataset.load_scene:
+            with torch.no_grad():
+                x_orig = transform_points(self.dataset.denormalize_torch(x_noisy), mat)
+                mat_for_query = mat.clone()
+                target_ind = self.mask_ind if self.mask_ind != -1 else 0
+                mat_for_query[:, :3, 3] = x_orig[:, self.emb_f, target_ind * 3: target_ind * 3 + 3]
+                mat_for_query[:, 1, 3] = 0
+                query_points = transform_points(self.grid, mat_for_query)
+
+                occ = self.dataset.get_occ_for_points(query_points, obj_points, scene_flag)
+                nb_voxels = self.dataset.nb_voxels
+                occ = occ.reshape(-1, nb_voxels, nb_voxels, nb_voxels).float()
+
+                # import trimesh
+                # print(mat[0])
+                # grid_np = self.grid[0].detach().cpu().numpy().reshape((-1, 3))
+                # occ_np = occ[0].detach().cpu().numpy().reshape((-1))
+                # points = grid_np[occ_np > 0.5]
+                # pcd_trimesh = trimesh.PointCloud(vertices=points)
+                # scene = trimesh.Scene([pcd_trimesh, trimesh.creation.axis(origin_color=[0, 0, 0])])
+                # scene.show()
+
+                occ = occ.permute(0, 2, 1, 3)
+        else:
+            occ = None
+
+        # x_noisy = torch.cat([x_noisy, occ], dim=-1).detach()
+
+        predicted_noise = self.model(x_noisy, occ, t, action_label, mask)
+
+        mask_inv = torch.logical_not(mask)
+
+        if loss_type == 'l1':
+            loss = F.l1_loss(noise[mask_inv], predicted_noise[mask_inv])
+        elif loss_type == 'l2':
+            loss = F.mse_loss(noise[mask_inv], predicted_noise[mask_inv])
+        elif loss_type == "huber":
+            loss = F.smooth_l1_loss(noise[mask_inv], predicted_noise[mask_inv])
+        else:
+            raise NotImplementedError()
+
+        return loss
+
+    @torch.no_grad()
+    def p_sample_loop(self, fixed_points, obj_locs, mat, scene, goal, action_label):
+        device = next(self.model.parameters()).device
+        shape = (self.batch_size, self.seq_len, self.channel)
+        points = torch.randn(shape, device=device)  # + torch.tensor([0., 0.3, 0.] * 22, device=device)
+
+        if self.fix_mode:
+            self.set_fixed_points(points, goal, fixed_points, mat, joint_id=self.mask_ind, fix_mode=True, fix_goal=True)
+        imgs = []
+        occs = []
+
+        if self.dataset.load_scene:
+            x_orig = transform_points(self.dataset.denormalize_torch(points), mat)
+            mat_for_query = mat.clone()
+            target_ind = self.mask_ind if self.mask_ind != -1 else 0
+            mat_for_query[:, :3, 3] = x_orig[:, self.emb_f, target_ind * 3: target_ind * 3 + 3]
+            mat_for_query[:, 1, 3] = 0
+            query_points = transform_points(self.grid, mat_for_query)
+            occ = self.dataset.get_occ_for_points(query_points, obj_locs, scene)
+            nb_voxels = self.dataset.nb_voxels
+            occ = occ.reshape(-1, nb_voxels, nb_voxels, nb_voxels).float()
+
+            # import trimesh
+            # print('\n', mat[0])
+            # grid_np = self.grid[0].detach().cpu().numpy().reshape((-1, 3))
+            # occ_np = occ[0].detach().cpu().numpy().reshape((-1))
+            # pointcloud = grid_np[occ_np > 0.5]
+            # pcd_trimesh = trimesh.PointCloud(vertices=pointcloud)
+            # np.save('/home/jiangnan/SyntheticHSI/Paper/Teaser/occ.npy', pointcloud)
+            # scene = trimesh.Scene([pcd_trimesh, trimesh.creation.axis(origin_color=[0, 0, 0])])
+            # scene.show()
+
+            occ = occ.permute(0, 2, 1, 3)
+
+        else:
+            occ = None
+
+        for i in tqdm(reversed(range(0, self.timesteps)), desc='sampling loop time step', total=self.timesteps):
+            model_used = self.model
+            # if s < 3 or (s == 3 and i < 5) or i < 3:
+            #     model_used = model_fix
+            # else:
+            #     model_used = model
+            points, occ = self.p_sample(model_used, points, fixed_points, goal, None, mat, occ,
+                                   torch.full((self.batch_size,), i, device=device, dtype=torch.long), i, action_label, self.mask_ind,
+                                   self.emb_f, self.fix_mode)
+            if self.fix_mode:
+                self.set_fixed_points(points, goal, fixed_points, mat, joint_id=self.mask_ind, fix_mode=True, fix_goal=True)
+            # set_fixed_points(points, goal, mat, joint_id=mask_ind)
+            # # t = torch.ones(b, device=device, dtype=torch.int64) * i
+            # if fixed_points is not None:
+            #     points[:, :fixed_points.shape[1], :] = fixed_points # q_sample(fixed_points, t, None, sqrt_alphas_cumprod, sqrt_one_minus_alphas_cumprod)
+
+            points_orig = transform_points(self.dataset.denormalize_torch(points), mat)
+            imgs.append(points_orig)
+            if occ is not None:
+                occs.append(occ.cpu().numpy())
+        return imgs, occs
+
+    @torch.no_grad()
+    def p_sample(self, model, x, fixed_points, goal, obj_points, mat, occ, t, t_index, action_label, mask_ind, emb_f,
+                 fix_mode, no_scene=False):
+        betas_t = extract(self.betas, t, x.shape)
+        sqrt_one_minus_alphas_cumprod_t = extract(
+            self.sqrt_one_minus_alphas_cumprod, t, x.shape
+        )
+        sqrt_recip_alphas_t = extract(self.sqrt_recip_alphas, t, x.shape)
+
+        # Equation 11 in the paper
+        # Use our model (noise predictor) to predict the mean
+
+
+        # joints_orig = transform_points(synhsi_dataset.denormalize_torch(x), mat)
+        # occ = synhsi_dataset.get_occ_for_points(joints_orig, obj_points, scene)
+        # x_occ = torch.cat([x, occ], dim=-1).detach()
+
+        model_mean = sqrt_recip_alphas_t * (
+                x - betas_t * model(x, occ, t, action_label, mask=None) / sqrt_one_minus_alphas_cumprod_t
+        )
+        # model_mean_noact = sqrt_recip_alphas_t * (
+        #         x - betas_t * model(x, occ, t, action_label, mask=None, no_action=True) / sqrt_one_minus_alphas_cumprod_t
+        # )
+        # model_mean = model_mean_noact + (model_mean - model_mean_noact) * 10
+        if not fix_mode:
+            self.set_fixed_points(model_mean, goal, fixed_points, mat, joint_id=mask_ind, fix_mode=True, fix_goal=False)
+
+        if t_index == 0:
+            return model_mean, occ
+        else:
+            posterior_variance_t = extract(self.posterior_variance, t, x.shape)
+            noise = torch.randn_like(x)
+            # Algorithm 2 line 4:
+            return model_mean + torch.sqrt(posterior_variance_t) * noise, occ
+
+        # Algorithm 2 (including returning all images)
+
+
+    def set_fixed_points(self, img, goal, fixed_points, mat, joint_id=0, fix_mode=False, fix_goal=True):
+        # if joint_id != 0:
+        #     goal_len = 2
+        goal_len = goal.shape[1]
+        # goal_batch = goal.reshape(1, 1, 3).repeat(img.shape[0], 1, 1)
+        goal = self.dataset.normalize_torch(transform_points(goal, torch.inverse(mat)))
+        # img[:, -1, joint_id * 3: joint_id * 3 + 3] = goal_batch[:, 0]
+        if fix_goal:
+            img[:, -goal_len:, joint_id * 3] = goal[:, :, 0]
+            if joint_id != 0:
+                img[:, -goal_len:, joint_id * 3 + 1] = goal[:, :, 1]
+            img[:, -goal_len:, joint_id * 3 + 2] = goal[:, :, 2]
+
+        if fixed_points is not None and fix_mode:
+            img[:, :fixed_points.shape[1], :] = fixed_points
+
+
+class Unet(nn.Module):
+    def __init__(
+            self,
+            dim_model,
+            num_heads,
+            num_layers,
+            dropout_p,
+            dim_input,
+            dim_output,
+            nb_voxels=None,
+            free_p=0.1,
+            nb_actions=0,
+            ac_type='',
+            no_scene=False,
+            no_action=False,
+            **kwargs
+    ):
+        super().__init__()
+
+        # INFO
+        self.model_type = "Transformer"
+        self.dim_model = dim_model
+        self.nb_actions = nb_actions
+        self.ac_type = ac_type
+        self.no_scene = no_scene
+        self.no_action = no_action
+
+        # LAYERS
+        if not no_scene:
+            self.scene_embedding = ViT(
+                image_size=nb_voxels,
+                patch_size=nb_voxels // 4,
+                channels=nb_voxels,
+                num_classes=dim_model,
+                dim=1024,
+                depth=6,
+                heads=16,
+                mlp_dim=2048,
+                dropout=0.1,
+                emb_dropout=0.1
+            )
+        self.free_p = free_p
+        self.positional_encoder = PositionalEncoding(
+            dim_model=dim_model, dropout_p=dropout_p, max_len=5000
+        )
+        self.embedding_input = nn.Linear(dim_input, dim_model)
+        self.embedding_output = nn.Linear(dim_output, dim_model)
+
+        # self.embedding_action = nn.Parameter(torch.randn(16, dim_model))
+
+        if not no_action and nb_actions > 0:
+            if self.ac_type in ['last_add_first_token', 'last_new_token']:
+                self.embedding_action = ActionTransformerEncoder(action_number=nb_actions,
+                                                                 dim_model=dim_model,
+                                                                 nhead=num_heads // 2,
+                                                                 num_layers=num_layers,
+                                                                 dim_feedforward=dim_model,
+                                                                 dropout_p=dropout_p,
+                                                                 activation="gelu")
+            elif self.ac_type in ['all_add_token']:
+                self.embedding_action = nn.Sequential(
+                    nn.Linear(nb_actions, dim_model),
+                    nn.SiLU(inplace=False),
+                    nn.Linear(dim_model, dim_model),
+                )
+
+        encoder_layer = nn.TransformerEncoderLayer(d_model=dim_model,
+                                                   nhead=num_heads,
+                                                   dim_feedforward=dim_model,
+                                                   dropout=dropout_p,
+                                                   activation="gelu")
+
+        self.transformer = nn.TransformerEncoder(encoder_layer,
+                                                 num_layers=num_layers
+        )
+        # self.out = nn.Linear(dim_model, dim_output)
+
+        self.out = nn.Linear(dim_model, dim_output)
+
+        self.embed_timestep = TimestepEmbedder(self.dim_model, self.positional_encoder)
+
+    def forward(self, x, cond, timesteps, action, mask, no_action=None):
+
+        #TODO ActionFlag
+        # action[action[:, 0] != 0., 0] = 1.
+
+        t_emb = self.embed_timestep(timesteps)  # [1, b, d]
+
+        if self.no_scene:
+            scene_emb = torch.zeros_like(t_emb)
+        else:
+            scene_emb = self.scene_embedding(cond).reshape(-1, 1, self.dim_model)
+
+        if self.no_action or self.nb_actions == 0:
+            action_emb = torch.zeros_like(t_emb)
+        else:
+            if self.ac_type in ['all_add_token']:
+                action_emb = self.embedding_action(action)
+            elif self.ac_type in ['last_add_first_token', 'last_new_token']:
+                action_emb = self.embedding_action(action)
+            else:
+                raise NotImplementedError
+
+        t_emb = t_emb.permute(1, 0, 2)
+
+        free_ind = torch.rand(scene_emb.shape[0]).to(scene_emb.device) < self.free_p
+        scene_emb[free_ind] = 0.
+        # if mask is not None:
+        #     x[free_ind][:, mask[0]] = 0.
+
+        if self.ac_type in ['last_add_first_token', 'last_new_token']:
+            action_emb[free_ind] = 0.
+        scene_emb = scene_emb.permute(1, 0, 2)
+        action_emb = action_emb.permute(1, 0, 2)
+
+        if self.ac_type in ['all_add_token', 'last_new_token']:
+            emb = t_emb + scene_emb
+        elif self.ac_type in ['last_add_first_token']:
+            emb = t_emb + scene_emb + action_emb
+
+        x = x.permute(1, 0, 2)
+        x = self.embedding_input(x) * math.sqrt(self.dim_model)
+        if self.ac_type in ['all_add_token', 'last_add_first_token']:
+            x = torch.cat((emb, x), dim=0)
+        elif self.ac_type in ['last_new_token']:
+            x = torch.cat((emb, action_emb, x), dim=0)
+
+        if self.ac_type in ['all_add_token']:
+            x[1:] = x[1:] + action_emb
+
+        x = self.positional_encoder(x)
+        x = self.transformer(x)
+        if self.ac_type in ['all_add_token', 'last_add_first_token']:
+            output = self.out(x)[1:]
+        elif self.ac_type in ['last_new_token']:
+            output = self.out(x)[2:]
+        output = output.permute(1, 0, 2)
+
+        return output
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, dim_model, dropout_p, max_len):
+        super().__init__()
+        # Modified version from: https://pytorch.org/tutorials/beginner/transformer_tutorial.html
+        # max_len determines how far the position can have an effect on a token (window)
+
+        # Info
+        self.dropout = nn.Dropout(dropout_p)
+
+        # Encoding - From formula
+        pos_encoding = torch.zeros(max_len, dim_model)
+        positions_list = torch.arange(0, max_len, dtype=torch.float).reshape(-1, 1)  # 0, 1, 2, 3, 4, 5
+        division_term = torch.exp(
+            torch.arange(0, dim_model, 2).float() * (-math.log(10000.0)) / dim_model)  # 1000^(2i/dim_model)
+
+        # PE(pos, 2i) = sin(pos/1000^(2i/dim_model))
+        pos_encoding[:, 0::2] = torch.sin(positions_list * division_term)
+
+        # PE(pos, 2i + 1) = cos(pos/1000^(2i/dim_model))
+        pos_encoding[:, 1::2] = torch.cos(positions_list * division_term)
+
+        # Saving buffer (same as parameter without gradients needed)
+        pos_encoding = pos_encoding.unsqueeze(0).transpose(0, 1)
+        self.register_buffer("pos_encoding", pos_encoding)
+
+    def forward(self, token_embedding: torch.tensor) -> torch.tensor:
+        # Residual connection + pos encoding
+        return self.dropout(token_embedding + self.pos_encoding[:token_embedding.size(0), :])
+
+
+class TimestepEmbedder(nn.Module):
+    def __init__(self, latent_dim, sequence_pos_encoder):
+        super().__init__()
+        self.latent_dim = latent_dim
+        self.sequence_pos_encoder = sequence_pos_encoder
+
+        time_embed_dim = self.latent_dim
+        self.time_embed = nn.Sequential(
+            nn.Linear(self.latent_dim, time_embed_dim),
+            nn.SiLU(inplace=False),
+            nn.Linear(time_embed_dim, time_embed_dim),
+        )
+
+    def forward(self, timesteps):
+        return self.time_embed(self.sequence_pos_encoder.pos_encoding[timesteps])#.permute(1, 0, 2)
+
+
+class ActionTransformerEncoder(nn.Module):
+    def __init__(self,
+                 action_number,
+                 dim_model,
+                 nhead,
+                 num_layers,
+                 dim_feedforward,
+                 dropout_p,
+                 activation="gelu") -> None:
+        super().__init__()
+        self.positional_encoder = PositionalEncoding(
+            dim_model=dim_model, dropout_p=dropout_p, max_len=5000
+        )
+        self.input_embedder = nn.Linear(action_number, dim_model)
+        encoder_layer = nn.TransformerEncoderLayer(d_model=dim_model,
+                                                    nhead=nhead,
+                                                    dim_feedforward=dim_feedforward,
+                                                    dropout=dropout_p,
+                                                    activation=activation)
+        self.transformer_encoder = nn.TransformerEncoder(encoder_layer,
+                                                 num_layers=num_layers
+        )
+
+    def forward(self, x):
+        x = x.permute(1, 0, 2)
+        x = self.input_embedder(x)
+        x = self.positional_encoder(x)
+        x = self.transformer_encoder(x)
+        x = x.permute(1, 0, 2)
+        x = torch.mean(x, dim=1, keepdim=True)
+        return x
+
+

not_used.py

@@ -0,0 +1,4 @@
+import numpy as np
+
+test = np.random.randn(3,4,5)
+print(0)

objects_occ/Background.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3df6127677f16bc78d3193fe8a51fdde4e87e2651e1cb4e6935e348b2f28741
+size 12000128

objects_occ/background.blend

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dde8176ebca147248b6ff88d2aaa2d2de3522d84ad99133509195ea265b8f4c6
+size 2747420

objects_occ/background.obj

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e8261b62db7767afa66e91c450af153c0eefc836d78b2a6b05dd1af74c6cee0b
+size 65882377

objects_occ/basin.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aad0216f00c4162730cc572623d69a86e7473ab5d2d040cf33a48a4a1ae46b08
+size 1605344

objects_occ/bed.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aaae4ec071dca60fdc847add067a9fbd0f7f1194a1de42fa9896f57e294c704c
+size 2243504

objects_occ/flower.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7aaaf632feec63231fb8dd8ff0bc5cf5c05a547c11901e966affd456d001ef7a
+size 165428

objects_occ/flower.obj

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+# Blender 3.3.0
+# www.blender.org
+o flower
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