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# Copyright (c) 2023, Tencent Inc
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
import torch.nn as nn
import nvdiffrast.torch as dr
from einops import rearrange, repeat
from .encoder.dino_wrapper import DinoWrapper
from .decoder.transformer import TriplaneTransformer
from .renderer.synthesizer_mesh import TriplaneSynthesizer
from .geometry.camera.perspective_camera import PerspectiveCamera
from .geometry.render.neural_render import NeuralRender
from .geometry.rep_3d.flexicubes_geometry import FlexiCubesGeometry
from ..utils.mesh_util import xatlas_uvmap
class InstantMesh(nn.Module):
"""
Full model of the large reconstruction model.
"""
def __init__(
self,
encoder_freeze: bool = False,
encoder_model_name: str = 'facebook/dino-vitb16',
encoder_feat_dim: int = 768,
transformer_dim: int = 1024,
transformer_layers: int = 16,
transformer_heads: int = 16,
triplane_low_res: int = 32,
triplane_high_res: int = 64,
triplane_dim: int = 80,
rendering_samples_per_ray: int = 128,
grid_res: int = 128,
grid_scale: float = 2.0,
):
super().__init__()
# attributes
self.grid_res = grid_res
self.grid_scale = grid_scale
self.deformation_multiplier = 4.0
# modules
self.encoder = DinoWrapper(
model_name=encoder_model_name,
freeze=encoder_freeze,
)
self.transformer = TriplaneTransformer(
inner_dim=transformer_dim,
num_layers=transformer_layers,
num_heads=transformer_heads,
image_feat_dim=encoder_feat_dim,
triplane_low_res=triplane_low_res,
triplane_high_res=triplane_high_res,
triplane_dim=triplane_dim,
)
self.synthesizer = TriplaneSynthesizer(
triplane_dim=triplane_dim,
samples_per_ray=rendering_samples_per_ray,
)
def init_flexicubes_geometry(self, device, fovy=50.0, use_renderer=True):
camera = PerspectiveCamera(fovy=fovy, device=device)
if use_renderer:
renderer = NeuralRender(device, camera_model=camera)
else:
renderer = None
self.geometry = FlexiCubesGeometry(
grid_res=self.grid_res,
scale=self.grid_scale,
renderer=renderer,
render_type='neural_render',
device=device,
)
def forward_planes(self, images, cameras):
# images: [B, V, C_img, H_img, W_img]
# cameras: [B, V, 16]
B = images.shape[0]
# encode images
image_feats = self.encoder(images, cameras)
image_feats = rearrange(image_feats, '(b v) l d -> b (v l) d', b=B)
# decode triplanes
planes = self.transformer(image_feats)
return planes
def get_sdf_deformation_prediction(self, planes):
'''
Predict SDF and deformation for tetrahedron vertices
:param planes: triplane feature map for the geometry
'''
init_position = self.geometry.verts.unsqueeze(0).expand(planes.shape[0], -1, -1)
# Step 1: predict the SDF and deformation
sdf, deformation, weight = torch.utils.checkpoint.checkpoint(
self.synthesizer.get_geometry_prediction,
planes,
init_position,
self.geometry.indices,
use_reentrant=False,
)
# Step 2: Normalize the deformation to avoid the flipped triangles.
deformation = 1.0 / (self.grid_res * self.deformation_multiplier) * torch.tanh(deformation)
sdf_reg_loss = torch.zeros(sdf.shape[0], device=sdf.device, dtype=torch.float32)
####
# Step 3: Fix some sdf if we observe empty shape (full positive or full negative)
sdf_bxnxnxn = sdf.reshape((sdf.shape[0], self.grid_res + 1, self.grid_res + 1, self.grid_res + 1))
sdf_less_boundary = sdf_bxnxnxn[:, 1:-1, 1:-1, 1:-1].reshape(sdf.shape[0], -1)
pos_shape = torch.sum((sdf_less_boundary > 0).int(), dim=-1)
neg_shape = torch.sum((sdf_less_boundary < 0).int(), dim=-1)
zero_surface = torch.bitwise_or(pos_shape == 0, neg_shape == 0)
if torch.sum(zero_surface).item() > 0:
update_sdf = torch.zeros_like(sdf[0:1])
max_sdf = sdf.max()
min_sdf = sdf.min()
update_sdf[:, self.geometry.center_indices] += (1.0 - min_sdf) # greater than zero
update_sdf[:, self.geometry.boundary_indices] += (-1 - max_sdf) # smaller than zero
new_sdf = torch.zeros_like(sdf)
for i_batch in range(zero_surface.shape[0]):
if zero_surface[i_batch]:
new_sdf[i_batch:i_batch + 1] += update_sdf
update_mask = (new_sdf == 0).float()
# Regulraization here is used to push the sdf to be a different sign (make it not fully positive or fully negative)
sdf_reg_loss = torch.abs(sdf).mean(dim=-1).mean(dim=-1)
sdf_reg_loss = sdf_reg_loss * zero_surface.float()
sdf = sdf * update_mask + new_sdf * (1 - update_mask)
# Step 4: Here we remove the gradient for the bad sdf (full positive or full negative)
final_sdf = []
final_def = []
for i_batch in range(zero_surface.shape[0]):
if zero_surface[i_batch]:
final_sdf.append(sdf[i_batch: i_batch + 1].detach())
final_def.append(deformation[i_batch: i_batch + 1].detach())
else:
final_sdf.append(sdf[i_batch: i_batch + 1])
final_def.append(deformation[i_batch: i_batch + 1])
sdf = torch.cat(final_sdf, dim=0)
deformation = torch.cat(final_def, dim=0)
return sdf, deformation, sdf_reg_loss, weight
def get_geometry_prediction(self, planes=None):
'''
Function to generate mesh with give triplanes
:param planes: triplane features
'''
# Step 1: first get the sdf and deformation value for each vertices in the tetrahedon grid.
sdf, deformation, sdf_reg_loss, weight = self.get_sdf_deformation_prediction(planes)
v_deformed = self.geometry.verts.unsqueeze(dim=0).expand(sdf.shape[0], -1, -1) + deformation
tets = self.geometry.indices
n_batch = planes.shape[0]
v_list = []
f_list = []
flexicubes_surface_reg_list = []
# Step 2: Using marching tet to obtain the mesh
for i_batch in range(n_batch):
verts, faces, flexicubes_surface_reg = self.geometry.get_mesh(
v_deformed[i_batch],
sdf[i_batch].squeeze(dim=-1),
with_uv=False,
indices=tets,
weight_n=weight[i_batch].squeeze(dim=-1),
is_training=self.training,
)
flexicubes_surface_reg_list.append(flexicubes_surface_reg)
v_list.append(verts)
f_list.append(faces)
flexicubes_surface_reg = torch.cat(flexicubes_surface_reg_list).mean()
flexicubes_weight_reg = (weight ** 2).mean()
return v_list, f_list, sdf, deformation, v_deformed, (sdf_reg_loss, flexicubes_surface_reg, flexicubes_weight_reg)
def get_texture_prediction(self, planes, tex_pos, hard_mask=None):
'''
Predict Texture given triplanes
:param planes: the triplane feature map
:param tex_pos: Position we want to query the texture field
:param hard_mask: 2D silhoueete of the rendered image
'''
tex_pos = torch.cat(tex_pos, dim=0)
if not hard_mask is None:
tex_pos = tex_pos * hard_mask.float()
batch_size = tex_pos.shape[0]
tex_pos = tex_pos.reshape(batch_size, -1, 3)
###################
# We use mask to get the texture location (to save the memory)
if hard_mask is not None:
n_point_list = torch.sum(hard_mask.long().reshape(hard_mask.shape[0], -1), dim=-1)
sample_tex_pose_list = []
max_point = n_point_list.max()
expanded_hard_mask = hard_mask.reshape(batch_size, -1, 1).expand(-1, -1, 3) > 0.5
for i in range(tex_pos.shape[0]):
tex_pos_one_shape = tex_pos[i][expanded_hard_mask[i]].reshape(1, -1, 3)
if tex_pos_one_shape.shape[1] < max_point:
tex_pos_one_shape = torch.cat(
[tex_pos_one_shape, torch.zeros(
1, max_point - tex_pos_one_shape.shape[1], 3,
device=tex_pos_one_shape.device, dtype=torch.float32)], dim=1)
sample_tex_pose_list.append(tex_pos_one_shape)
tex_pos = torch.cat(sample_tex_pose_list, dim=0)
tex_feat = torch.utils.checkpoint.checkpoint(
self.synthesizer.get_texture_prediction,
planes,
tex_pos,
use_reentrant=False,
)
if hard_mask is not None:
final_tex_feat = torch.zeros(
planes.shape[0], hard_mask.shape[1] * hard_mask.shape[2], tex_feat.shape[-1], device=tex_feat.device)
expanded_hard_mask = hard_mask.reshape(hard_mask.shape[0], -1, 1).expand(-1, -1, final_tex_feat.shape[-1]) > 0.5
for i in range(planes.shape[0]):
final_tex_feat[i][expanded_hard_mask[i]] = tex_feat[i][:n_point_list[i]].reshape(-1)
tex_feat = final_tex_feat
return tex_feat.reshape(planes.shape[0], hard_mask.shape[1], hard_mask.shape[2], tex_feat.shape[-1])
def render_mesh(self, mesh_v, mesh_f, cam_mv, render_size=256):
'''
Function to render a generated mesh with nvdiffrast
:param mesh_v: List of vertices for the mesh
:param mesh_f: List of faces for the mesh
:param cam_mv: 4x4 rotation matrix
:return:
'''
return_value_list = []
for i_mesh in range(len(mesh_v)):
return_value = self.geometry.render_mesh(
mesh_v[i_mesh],
mesh_f[i_mesh].int(),
cam_mv[i_mesh],
resolution=render_size,
hierarchical_mask=False
)
return_value_list.append(return_value)
return_keys = return_value_list[0].keys()
return_value = dict()
for k in return_keys:
value = [v[k] for v in return_value_list]
return_value[k] = value
mask = torch.cat(return_value['mask'], dim=0)
hard_mask = torch.cat(return_value['hard_mask'], dim=0)
tex_pos = return_value['tex_pos']
depth = torch.cat(return_value['depth'], dim=0)
normal = torch.cat(return_value['normal'], dim=0)
return mask, hard_mask, tex_pos, depth, normal
def forward_geometry(self, planes, render_cameras, render_size=256):
'''
Main function of our Generator. It first generate 3D mesh, then render it into 2D image
with given `render_cameras`.
:param planes: triplane features
:param render_cameras: cameras to render generated 3D shape
'''
B, NV = render_cameras.shape[:2]
# Generate 3D mesh first
mesh_v, mesh_f, sdf, deformation, v_deformed, sdf_reg_loss = self.get_geometry_prediction(planes)
# Render the mesh into 2D image (get 3d position of each image plane)
cam_mv = render_cameras
run_n_view = cam_mv.shape[1]
antilias_mask, hard_mask, tex_pos, depth, normal = self.render_mesh(mesh_v, mesh_f, cam_mv, render_size=render_size)
tex_hard_mask = hard_mask
tex_pos = [torch.cat([pos[i_view:i_view + 1] for i_view in range(run_n_view)], dim=2) for pos in tex_pos]
tex_hard_mask = torch.cat(
[torch.cat(
[tex_hard_mask[i * run_n_view + i_view: i * run_n_view + i_view + 1]
for i_view in range(run_n_view)], dim=2)
for i in range(planes.shape[0])], dim=0)
# Querying the texture field to predict the texture feature for each pixel on the image
tex_feat = self.get_texture_prediction(planes, tex_pos, tex_hard_mask)
background_feature = torch.ones_like(tex_feat) # white background
# Merge them together
img_feat = tex_feat * tex_hard_mask + background_feature * (1 - tex_hard_mask)
# We should split it back to the original image shape
img_feat = torch.cat(
[torch.cat(
[img_feat[i:i + 1, :, render_size * i_view: render_size * (i_view + 1)]
for i_view in range(run_n_view)], dim=0) for i in range(len(tex_pos))], dim=0)
img = img_feat.clamp(0, 1).permute(0, 3, 1, 2).unflatten(0, (B, NV))
antilias_mask = antilias_mask.permute(0, 3, 1, 2).unflatten(0, (B, NV))
depth = -depth.permute(0, 3, 1, 2).unflatten(0, (B, NV)) # transform negative depth to positive
normal = normal.permute(0, 3, 1, 2).unflatten(0, (B, NV))
out = {
'img': img,
'mask': antilias_mask,
'depth': depth,
'normal': normal,
'sdf': sdf,
'mesh_v': mesh_v,
'mesh_f': mesh_f,
'sdf_reg_loss': sdf_reg_loss,
}
return out
def forward(self, images, cameras, render_cameras, render_size: int):
# images: [B, V, C_img, H_img, W_img]
# cameras: [B, V, 16]
# render_cameras: [B, M, D_cam_render]
# render_size: int
B, M = render_cameras.shape[:2]
planes = self.forward_planes(images, cameras)
out = self.forward_geometry(planes, render_cameras, render_size=render_size)
return {
'planes': planes,
**out
}
def extract_mesh(
self,
planes: torch.Tensor,
use_texture_map: bool = False,
texture_resolution: int = 1024,
**kwargs,
):
'''
Extract a 3D mesh from FlexiCubes. Only support batch_size 1.
:param planes: triplane features
:param use_texture_map: use texture map or vertex color
:param texture_resolution: the resolution of texure map
'''
assert planes.shape[0] == 1
device = planes.device
# predict geometry first
mesh_v, mesh_f, sdf, deformation, v_deformed, sdf_reg_loss = self.get_geometry_prediction(planes)
vertices, faces = mesh_v[0], mesh_f[0]
if not use_texture_map:
# query vertex colors
vertices_tensor = vertices.unsqueeze(0)
vertices_colors = self.synthesizer.get_texture_prediction(
planes, vertices_tensor).clamp(0, 1).squeeze(0).cpu().numpy()
vertices_colors = (vertices_colors * 255).astype(np.uint8)
return vertices.cpu().numpy(), faces.cpu().numpy(), vertices_colors
# use x-atlas to get uv mapping for the mesh
ctx = dr.RasterizeCudaContext(device=device)
uvs, mesh_tex_idx, gb_pos, tex_hard_mask = xatlas_uvmap(
self.geometry.renderer.ctx, vertices, faces, resolution=texture_resolution)
tex_hard_mask = tex_hard_mask.float()
# query the texture field to get the RGB color for texture map
tex_feat = self.get_texture_prediction(
planes, [gb_pos], tex_hard_mask)
background_feature = torch.zeros_like(tex_feat)
img_feat = torch.lerp(background_feature, tex_feat, tex_hard_mask)
texture_map = img_feat.permute(0, 3, 1, 2).squeeze(0)
return vertices, faces, uvs, mesh_tex_idx, texture_map