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# SPDX-License-Identifier: Apache-2.0
import torch
import torch.nn as nn
import gem.utils.matrix as matrix
from gem.utils.motion_utils import get_local_transl_vel, get_static_joint_mask
from gem.utils.rotation_conversions import (
axis_angle_to_matrix,
matrix_to_axis_angle,
matrix_to_rotation_6d,
rotation_6d_to_matrix,
)
from gem.utils.soma_utils.soma_layer import SomaLayer
from . import stats_compose
class EnDecoder(nn.Module):
def __init__(
self,
stats_name="DEFAULT_01",
encode_type="soma",
feature_arr=None,
stats_arr=None,
noise_pose_k=10,
clip_std=False,
feat_dim=None,
):
super().__init__()
if encode_type in ["soma", "soma_v2"]:
feature_arr = [encode_type]
stats_arr = [stats_name]
# Define feature dimensions as a class attribute
self.FEATURE_DIMS = {
"soma": 591,
"soma_v2": 585,
}
if feat_dim is not None:
self.FEATURE_DIMS[encode_type] = feat_dim
# Store stats for each feature type
self.stats_dict = {}
for feature, stats_name in zip(feature_arr, stats_arr):
stats = getattr(stats_compose, stats_name)
mean = torch.tensor(stats["mean"]).float()
std = torch.tensor(stats["std"]).float()
feature_dim = self.FEATURE_DIMS[feature]
if stats_name != "DEFAULT_01":
assert mean.shape[-1] == feature_dim
assert std.shape[-1] == feature_dim
if clip_std:
std[std < 1] = 1
self.stats_dict[feature] = {"mean": mean, "std": std}
# Store feature configuration
self.feature_arr = feature_arr
self.stats_arr = stats_arr
self.clip_std = clip_std
# option
self.noise_pose_k = noise_pose_k
self.encode_type = encode_type
self.obs_indices_dict = None
self.soma_model = None
def normalize(self, x, feature_type):
"""Normalize input using stats for specific feature type"""
stats = self.stats_dict[feature_type]
return (x - stats["mean"].to(x)) / stats["std"].to(x)
def denormalize(self, x_norm, feature_type):
"""Denormalize input using stats for specific feature type"""
stats = self.stats_dict[feature_type]
return x_norm * stats["std"].to(x_norm) + stats["mean"].to(x_norm)
def get_static_gt(self, inputs, vel_thr):
if "soma_params_w" in inputs:
# SOMA77: [L_ankle, L_foot, R_ankle, R_foot, L_wrist, R_wrist]
joint_ids = [69, 70, 74, 75, 14, 42]
self._ensure_soma_model()
soma_params_w = {k: v.float().cpu() for k, v in inputs["soma_params_w"].items()}
gt_w_j3d = self.soma_model(**soma_params_w)["joints"].to(
inputs["soma_params_w"]["body_pose"].device
)
else:
B, L = inputs["target_x"].shape[:2]
device = inputs["target_x"].device
return torch.zeros((B, L, 6), device=device)
static_gt = get_static_joint_mask(gt_w_j3d, vel_thr=vel_thr, repeat_last=True) # (B, L, J)
static_gt = static_gt[:, :, joint_ids].float() # (B, L, J')
return static_gt
def _ensure_soma_model(self):
"""Lazily initialize the SOMA body model."""
if self.soma_model is None:
self.soma_model = SomaLayer(
data_root="inputs/soma_assets",
low_lod=True,
device="cuda",
identity_model_type="mhr",
mode="warp",
)
def fk_v2(
self,
body_pose,
identity_coeffs=None,
scale_params=None,
global_orient=None,
transl=None,
get_intermediate=False,
**kwargs,
):
"""Forward kinematics using SOMA body model.
Args:
body_pose: (B, L, (J-1)*3) axis-angle
identity_coeffs: (B, L, C)
scale_params: (B, L, S)
global_orient: (B, L, 3) axis-angle
transl: (B, L, 3)
get_intermediate: if True, return (joints, local_mat, fk_mat)
Returns:
joints: (B, L, 77, 3), or (joints, local_mat, fk_mat) when get_intermediate=True
"""
B, L = body_pose.shape[:2]
if global_orient is None:
global_orient = torch.zeros((B, L, 3), device=body_pose.device)
aa = torch.cat([global_orient, body_pose], dim=-1).reshape(B, L, -1, 3)
rotmat = axis_angle_to_matrix(aa) # (B, L, J, 3, 3)
self._ensure_soma_model()
skeleton = self.soma_model.get_skeleton(
identity_coeffs.float(), scale_params.float()
) # (B, L, 77, 3)
parents = self.soma_model.parents
parents_tensor = torch.tensor(parents, device=body_pose.device)
local_skeleton = skeleton - skeleton[:, :, parents_tensor]
local_skeleton = torch.cat([skeleton[:, :, :1], local_skeleton[:, :, 1:]], dim=2)
if transl is not None:
local_skeleton[..., 0, :] += transl # (B, L, 77, 3)
mat = matrix.get_TRS(rotmat, local_skeleton) # (B, L, 77, 4, 4)
fk_mat = matrix.forward_kinematics(mat, parents) # (B, L, 77, 4, 4)
joints = matrix.get_position(fk_mat) # (B, L, 77, 3)
if not get_intermediate:
return joints
else:
return joints, mat, fk_mat
def build_obs_indices_dict(self):
"""
Initialize observation index mapping for decode-time use.
This mirrors the legacy behavior where eval/demo could decode without
a preceding encode() call.
"""
for feature in self.feature_arr:
if feature == "soma":
self.obs_indices_dict = {
"body_pose": (0, 456),
"identity_coeffs": (456, 501),
"scale_params": (501, 576),
"global_orient": (576, 582),
"global_orient_gv": (582, 588),
"local_transl_vel": (588, 591),
}
elif feature == "soma_v2":
self.obs_indices_dict = {
"body_pose": (0, 456),
"identity_coeffs": (456, 501),
"scale_params": (501, 570),
"global_orient": (570, 576),
"global_orient_gv": (576, 582),
"local_transl_vel": (582, 585),
}
def encode(self, inputs):
"""Composite encoder that combines multiple feature types"""
encoded_features = []
for feature in self.feature_arr:
if feature == "soma":
encoded = self.encode_soma(inputs)
elif feature == "soma_v2":
encoded = self.encode_soma_v2(inputs)
encoded_features.append(encoded)
return torch.cat(encoded_features, dim=-1)
def encode_soma(self, inputs):
J = 77
self.obs_indices_dict = {
"body_pose": (0, (J - 1) * 6),
"identity_coeffs": ((J - 1) * 6, (J - 1) * 6 + 45),
"scale_params": ((J - 1) * 6 + 45, (J - 1) * 6 + 45 + 75),
"global_orient": ((J - 1) * 6 + 45 + 75, (J - 1) * 6 + 45 + 75 + 6),
"global_orient_gv": (
(J - 1) * 6 + 45 + 75 + 6,
(J - 1) * 6 + 45 + 75 + 6 + 6,
),
"local_transl_vel": (
(J - 1) * 6 + 45 + 75 + 6 + 6,
(J - 1) * 6 + 45 + 75 + 6 + 6 + 3,
),
}
B, L = inputs["soma_params_c"]["body_pose"].shape[:2]
soma_params_c = inputs["soma_params_c"]
body_pose = soma_params_c["body_pose"].reshape(B, L, J - 1, 3)
body_pose_r6d = matrix_to_rotation_6d(axis_angle_to_matrix(body_pose)).flatten(-2)
identity_coeffs = soma_params_c["identity_coeffs"]
scale_params = soma_params_c["scale_params"]
global_orient_R = axis_angle_to_matrix(soma_params_c["global_orient"])
global_orient_r6d = matrix_to_rotation_6d(global_orient_R)
R_c2gv = inputs["R_c2gv"]
global_orient_gv_r6d = matrix_to_rotation_6d(R_c2gv @ global_orient_R)
soma_params_w = inputs["soma_params_w"]
local_transl_vel = get_local_transl_vel(
soma_params_w["transl"], soma_params_w["global_orient"]
)
x = torch.cat(
[
body_pose_r6d,
identity_coeffs,
scale_params,
global_orient_r6d,
global_orient_gv_r6d,
local_transl_vel,
],
dim=-1,
)
return self.normalize(x, "soma")
def encode_soma_v2(self, inputs):
J = 77
self.obs_indices_dict = {
"body_pose": (0, (J - 1) * 6),
"identity_coeffs": ((J - 1) * 6, (J - 1) * 6 + 45),
"scale_params": ((J - 1) * 6 + 45, (J - 1) * 6 + 45 + 69),
"global_orient": ((J - 1) * 6 + 45 + 69, (J - 1) * 6 + 45 + 69 + 6),
"global_orient_gv": (
(J - 1) * 6 + 45 + 69 + 6,
(J - 1) * 6 + 45 + 69 + 6 + 6,
),
"local_transl_vel": (
(J - 1) * 6 + 45 + 69 + 6 + 6,
(J - 1) * 6 + 45 + 69 + 6 + 6 + 3,
),
}
B, L = inputs["soma_params_c"]["body_pose"].shape[:2]
soma_params_c = inputs["soma_params_c"]
body_pose = soma_params_c["body_pose"].reshape(B, L, J - 1, 3)
body_pose_r6d = matrix_to_rotation_6d(axis_angle_to_matrix(body_pose)).flatten(-2)
identity_coeffs = soma_params_c["identity_coeffs"]
scale_params = soma_params_c["scale_params"]
global_orient_R = axis_angle_to_matrix(soma_params_c["global_orient"])
global_orient_r6d = matrix_to_rotation_6d(global_orient_R)
R_c2gv = inputs["R_c2gv"]
global_orient_gv_r6d = matrix_to_rotation_6d(R_c2gv @ global_orient_R)
soma_params_w = inputs["soma_params_w"]
local_transl_vel = get_local_transl_vel(
soma_params_w["transl"], soma_params_w["global_orient"]
)
x = torch.cat(
[
body_pose_r6d,
identity_coeffs,
scale_params,
global_orient_r6d,
global_orient_gv_r6d,
local_transl_vel,
],
dim=-1,
)
return self.normalize(x, "soma_v2")
def decode(self, x_norm):
"""Composite decoder that handles multiple feature types"""
current_idx = 0
decoded_outputs = {}
for feature in self.feature_arr:
feature_size = self.FEATURE_DIMS[feature]
feature_norm = x_norm[..., current_idx : current_idx + feature_size]
if feature == "soma":
decoded = self.decode_soma(feature_norm)
elif feature == "soma_v2":
decoded = self.decode_soma_v2(feature_norm)
decoded_outputs.update(decoded)
current_idx += feature_size
return decoded_outputs
def decode_soma(self, x_norm):
B, L, _ = x_norm.shape
x = self.denormalize(x_norm, "soma")
body_pose_r6d = x[:, :, : self.obs_indices_dict["body_pose"][1]]
identity_coeffs = x[
:,
:,
self.obs_indices_dict["identity_coeffs"][0] : self.obs_indices_dict["identity_coeffs"][
1
],
]
scale_params = x[
:,
:,
self.obs_indices_dict["scale_params"][0] : self.obs_indices_dict["scale_params"][1],
]
global_orient_r6d = x[
:,
:,
self.obs_indices_dict["global_orient"][0] : self.obs_indices_dict["global_orient"][1],
]
global_orient_gv_r6d = x[
:,
:,
self.obs_indices_dict["global_orient_gv"][0] : self.obs_indices_dict[
"global_orient_gv"
][1],
]
local_transl_vel = x[
:,
:,
self.obs_indices_dict["local_transl_vel"][0] : self.obs_indices_dict[
"local_transl_vel"
][1],
]
body_pose = matrix_to_axis_angle(
rotation_6d_to_matrix(body_pose_r6d.reshape(B, L, -1, 6))
).flatten(-2)
global_orient_c = matrix_to_axis_angle(rotation_6d_to_matrix(global_orient_r6d))
global_orient_gv = matrix_to_axis_angle(rotation_6d_to_matrix(global_orient_gv_r6d))
offset = torch.zeros((B, L, 3), device=x.device)
return {
"body_pose": body_pose,
"identity_coeffs": identity_coeffs,
"scale_params": scale_params,
"global_orient": global_orient_c,
"global_orient_gv": global_orient_gv,
"local_transl_vel": local_transl_vel,
"offset": offset,
}
def decode_soma_v2(self, x_norm):
B, L, _ = x_norm.shape
x = self.denormalize(x_norm, "soma_v2")
body_pose_r6d = x[:, :, : self.obs_indices_dict["body_pose"][1]]
identity_coeffs = x[
:,
:,
self.obs_indices_dict["identity_coeffs"][0] : self.obs_indices_dict["identity_coeffs"][
1
],
]
scale_params = x[
:,
:,
self.obs_indices_dict["scale_params"][0] : self.obs_indices_dict["scale_params"][1],
]
global_orient_r6d = x[
:,
:,
self.obs_indices_dict["global_orient"][0] : self.obs_indices_dict["global_orient"][1],
]
global_orient_gv_r6d = x[
:,
:,
self.obs_indices_dict["global_orient_gv"][0] : self.obs_indices_dict[
"global_orient_gv"
][1],
]
local_transl_vel = x[
:,
:,
self.obs_indices_dict["local_transl_vel"][0] : self.obs_indices_dict[
"local_transl_vel"
][1],
]
body_pose = matrix_to_axis_angle(
rotation_6d_to_matrix(body_pose_r6d.reshape(B, L, -1, 6))
).flatten(-2)
global_orient_c = matrix_to_axis_angle(rotation_6d_to_matrix(global_orient_r6d))
global_orient_gv = matrix_to_axis_angle(rotation_6d_to_matrix(global_orient_gv_r6d))
offset = torch.zeros((B, L, 3), device=x.device)
return {
"body_pose": body_pose,
"identity_coeffs": identity_coeffs,
"scale_params": scale_params,
"global_orient": global_orient_c,
"global_orient_gv": global_orient_gv,
"local_transl_vel": local_transl_vel,
"offset": offset,
}
def get_motion_dim(self):
"""Calculate total dimension based on enabled features"""
return sum(self.FEATURE_DIMS[feature] for feature in self.feature_arr)
def get_obs_indices(self, obs):
return self.obs_indices_dict[obs]
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