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