lib/utils/blend_utils.py

import torch
import torch.nn.functional as F
import numpy as np


def ppts_to_pts(ppts, bw, A):
    """transform points from the pose space to the zero space"""
    sh = ppts.shape
    bw = bw.permute(0, 2, 1)
    A = torch.bmm(bw, A.view(sh[0], 24, -1))
    A = A.view(sh[0], -1, 4, 4)
    pts = ppts - A[..., :3, 3]
    R_inv = torch.inverse(A[..., :3, :3])
    pts = torch.sum(R_inv * pts[:, :, None], dim=3)
    return pts


def grid_sample_blend_weights(grid_coords, bw):
    # the blend weight is indexed by xyz
    grid_coords = grid_coords[:, None, None]
    bw = F.grid_sample(bw,
                       grid_coords,
                       padding_mode='border',
                       align_corners=True)
    bw = bw[:, :, 0, 0]
    return bw


def bounds_grid_sample_blend_weights(pts, bw, bounds):
    """grid sample blend weights"""
    pts = pts.clone()

    # interpolate blend weights
    min_xyz = bounds[:, 0]
    max_xyz = bounds[:, 1]
    bounds = max_xyz[:, None] - min_xyz[:, None]
    grid_coords = (pts - min_xyz[:, None]) / bounds
    grid_coords = grid_coords * 2 - 1
    # convert xyz to zyx, since the blend weight is indexed by xyz
    grid_coords = grid_coords[..., [2, 1, 0]]

    # the blend weight is indexed by xyz
    bw = bw.permute(0, 4, 1, 2, 3)
    grid_coords = grid_coords[:, None, None]
    bw = F.grid_sample(bw,
                       grid_coords,
                       padding_mode='border',
                       align_corners=True)
    bw = bw[:, :, 0, 0]

    return bw


def grid_sample_A_blend_weights(nf_grid_coords, bw):
    """
    nf_grid_coords: batch_size x N_samples x 24 x 3
    bw: batch_size x 24 x 64 x 64 x 64
    """
    bws = []
    for i in range(24):
        nf_grid_coords_ = nf_grid_coords[:, :, i]
        nf_grid_coords_ = nf_grid_coords_[:, None, None]
        bw_ = F.grid_sample(bw[:, i:i + 1],
                            nf_grid_coords_,
                            padding_mode='border',
                            align_corners=True)
        bw_ = bw_[:, :, 0, 0]
        bws.append(bw_)
    bw = torch.cat(bws, dim=1)
    return bw


def ppts_to_pts(pts, bw, A):
    """transform points from the pose space to the t pose"""
    sh = pts.shape
    bw = bw.permute(0, 2, 1)
    A = torch.bmm(bw, A.view(sh[0], 24, -1))
    A = A.view(sh[0], -1, 4, 4)
    pts = pts - A[..., :3, 3]
    R_inv = torch.inverse(A[..., :3, :3])
    pts = torch.sum(R_inv * pts[:, :, None], dim=3)
    return pts