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lbs.cpp
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lbs.cpp
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#include "lbs.hpp"
#include <cmath>
#include <tuple>
#include <vector>
#include "ATen/TensorIndexing.h"
#include "ATen/ops/arange.h"
#include "ATen/ops/bmm.h"
#include "ATen/ops/cat.h"
#include "ATen/ops/clamp.h"
#include "ATen/ops/einsum.h"
#include "ATen/ops/from_blob.h"
#include "ATen/ops/index_select.h"
#include "ATen/ops/matmul.h"
#include "ATen/ops/norm.h"
#include "ATen/ops/pad.h"
#include "ATen/ops/round.h"
#include "ATen/ops/split.h"
#include "ATen/ops/unsqueeze.h"
#include "c10/core/ScalarType.h"
#include "c10/core/TensorOptions.h"
#include "torch/types.h"
namespace smplx::lbs {
auto batch_rodrigues(Tensor &&rot_vecs, float epsilon) -> Tensor {
auto batch_size = rot_vecs.size(0);
auto angle = torch::norm(rot_vecs + 1e-8, 2, 1, true);
auto rot_dir = rot_vecs / angle;
auto cos = torch::unsqueeze(torch::cos(angle), 1);
auto sin = torch::unsqueeze(torch::sin(angle), 1);
auto r = torch::split(rot_dir, 1, 1);
auto K =
torch::zeros({batch_size, 3, 3},
torch::device(rot_vecs.device()).dtype(rot_vecs.dtype()));
auto zeros =
torch::zeros({batch_size, 1},
torch::device(rot_vecs.device()).dtype(rot_vecs.dtype()));
K = torch::cat({zeros, -r[2], r[1], r[2], zeros, -r[0], -r[1], r[0], zeros},
1)
.view({batch_size, 3, 3});
auto ident =
torch::eye(3, torch::device(rot_vecs.device()).dtype(rot_vecs.dtype()))
.unsqueeze(0);
auto rot_mat = ident + sin * K + (1 - cos) * torch::bmm(K, K);
return rot_mat;
}
auto batch_rigid_transform(Tensor &rot_mats, Tensor &joints, Tensor &parents,
torch::Dtype) -> std::tuple<Tensor, Tensor> {
joints = torch::unsqueeze(joints, -1);
auto rel_joints = joints.clone();
rel_joints.index_put_(
{Slice(None), Slice(1, None)},
rel_joints.index({Slice(None), Slice(1, None)}) -
joints.index({Slice(None), parents.index({Slice(1, None)})}));
auto transforms_mat = transform_mat(rot_mats.reshape({-1, 3, 3}),
rel_joints.reshape({-1, 3, 1}))
.reshape({-1, joints.size(1), 4, 4});
// auto transform_chain = [transforms_mat[:, 0]]
std::vector<Tensor> transform_chain{transforms_mat.index({Slice(None), 0})};
transform_chain.reserve(parents.size(0));
for (auto i = 1; i < parents.size(0); ++i) {
auto x = parents[i];
auto curr_res = torch::matmul(transform_chain[parents[i].item<int>()],
transforms_mat.index({Slice(None), i}));
transform_chain.emplace_back(curr_res);
}
auto transforms = torch::stack(transform_chain, 1);
auto posed_joints =
transforms.index({Slice(None), Slice(None), Slice(None, 3), 3});
auto joints_homogen = torch::pad(joints, {0, 0, 0, 1});
auto rel_transforms =
transforms - torch::pad(torch::matmul(transforms, joints_homogen),
{3, 0, 0, 0, 0, 0, 0, 0});
return std::make_tuple(posed_joints, rel_transforms);
}
auto lbs(Tensor &betas, Tensor &pose, Tensor &v_template, Tensor &shapedirs,
Tensor &posedirs, Tensor &J_regressor, Tensor &parents,
Tensor &lbs_weights, bool pose2rot) -> std::tuple<Tensor, Tensor> {
auto batch_size = std::max(betas.size(0), pose.size(0));
auto v_shaped = v_template + blend_shape(betas, shapedirs);
auto J = vertices2joints(J_regressor, v_shaped);
auto ident =
torch::eye(3, torch::device(betas.device()).dtype(betas.dtype()));
Tensor pose_offsets, rot_mats;
if (pose2rot) {
rot_mats =
batch_rodrigues(pose.view({-1, 3})).view({batch_size, -1, 3, 3});
auto pose_feature = (rot_mats.index({Slice(None), Slice(1, None),
Slice(None), Slice(None)}) -
ident)
.view({batch_size, -1});
pose_offsets =
torch::matmul(pose_feature, posedirs).view({batch_size, -1, 3});
} else {
auto pose_feature = pose.index({Slice(None), Slice(1, None)})
.view({batch_size, -1, 3, 3}) -
ident;
rot_mats = pose.view({batch_size, -1, 3, 3});
pose_offsets =
torch::matmul(pose_feature.view({batch_size, -1}), posedirs)
.view({batch_size, -1, 3});
}
auto v_posed = pose_offsets + v_shaped;
auto [J_transformed, A] = batch_rigid_transform(rot_mats, J, parents);
auto W = lbs_weights.unsqueeze(0).expand({batch_size, -1, -1});
auto num_joints = J_regressor.size(0);
auto T = torch::matmul(W, A.view({batch_size, num_joints, 16}))
.view({batch_size, -1, 4, 4});
auto homogen_coord =
torch::ones({batch_size, v_posed.size(1), 1},
torch::device(betas.device()).dtype(betas.dtype()));
auto v_posed_homo = torch::cat({v_posed, homogen_coord}, 2);
auto v_homo = torch::matmul(T, torch::unsqueeze(v_posed_homo, -1));
return std::make_tuple(
v_homo.index({Slice(None), Slice(None), Slice(None, 3), 0}),
J_transformed);
}
auto vertices2landmarks(Tensor &vertices, Tensor &faces, Tensor &lmk_faces_idx,
Tensor &lmk_bary_coords) -> Tensor {
auto batch_size{vertices.size(0)}, num_verts{vertices.size(1)};
auto device = vertices.device();
auto lmk_faces = torch::index_select(faces, 0, lmk_faces_idx.view({-1}))
.view({batch_size, -1, 3});
lmk_faces +=
torch::arange(batch_size, torch::device(device).dtype(torch::kLong))
.view({-1, 1, 1}) *
num_verts;
auto lmk_vertices = vertices.view({-1, 3})
.index_select(0, lmk_faces)
.view({batch_size, -1, 3, 3});
return torch::einsum("blfi,blf->bli", {lmk_vertices, lmk_bary_coords});
}
auto find_dynamic_lmk_idx_and_bcoords(
const Tensor &vertices, const Tensor &pose,
const Tensor &dynamic_lmk_faces_idx, const Tensor &dynamic_lmk_b_coords,
std::vector<int> &neck_kin_chain,
bool pose2rot) -> std::tuple<Tensor, Tensor> {
auto dtype = vertices.dtype();
auto batch_size = vertices.size(0);
Tensor rot_mats;
if (pose2rot) {
auto aa_pose = torch::index_select(
pose.view({batch_size, -1, 3}), 1,
torch::from_blob(neck_kin_chain.data(),
{static_cast<long long>(neck_kin_chain.size())},
torch::dtype(torch::kInt)));
rot_mats =
batch_rodrigues(aa_pose.view({-1, 3})).view({batch_size, -1, 3, 3});
} else {
rot_mats = torch::index_select(
pose.view({batch_size, -1, 3, 3}), 1,
torch::from_blob(neck_kin_chain.data(),
{static_cast<long long>(neck_kin_chain.size())},
torch::dtype(torch::kInt)));
}
auto rel_rot_mat =
torch::eye(3, torch::device(vertices.device()).dtype(dtype))
.unsqueeze_(0)
.repeat({batch_size, 1, 1});
auto size = neck_kin_chain.size();
for (auto i = 0; i < size; ++i) {
rel_rot_mat = torch::bmm(rot_mats.index({Slice(None), i}), rel_rot_mat);
}
auto y_rot_angle =
torch::round(
torch::clamp(-rot_mat_to_euler(rel_rot_mat) * 180.0 / M_PI, {}, 39))
.to(torch::dtype(torch::kLong));
auto neg_mask = y_rot_angle.lt(0).to(torch::dtype(torch::kLong));
auto mask = y_rot_angle.lt(-39).to(torch::dtype(torch::kLong));
auto neg_vals = mask * 78 + (1 - mask) * (39 - y_rot_angle);
y_rot_angle = (neg_mask * neg_vals + (1 - neg_mask) * y_rot_angle);
auto dyn_lmk_faces_idx =
torch::index_select(dynamic_lmk_faces_idx, 0, y_rot_angle);
auto dyn_lmk_b_coords =
torch::index_select(dynamic_lmk_b_coords, 0, y_rot_angle);
return std::make_tuple(dyn_lmk_faces_idx, dyn_lmk_b_coords);
}
} // namespace smplx::lbs