File size: 9,069 Bytes
434b0b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 | # -*- coding: utf-8 -*-
# @Organization : Tongyi Lab, Alibaba
# @Author : Lingteng Qiu
# @Email : 220019047@link.cuhk.edu.cn
# @Time : 2025-06-04 20:43:18
# @Function : 3DGSRender Class
import os
import numpy as np
import torch
from core.models.rendering.base_gs_render import BaseGSRender
from core.models.rendering.gaussian_decoder.mlp_decoder import GSMLPDecoder
from core.models.rendering.utils.typing import *
from core.outputs.output import GaussianAppOutput
class GS3DRenderer(BaseGSRender):
def __init__(
self,
human_model_path,
subdivide_num,
smpl_type,
feat_dim,
query_dim,
use_rgb,
sh_degree,
xyz_offset_max_step,
mlp_network_config,
expr_param_dim,
shape_param_dim,
clip_scaling=0.2,
cano_pose_type=0,
decoder_mlp=False,
skip_decoder=False,
fix_opacity=False,
fix_rotation=False,
decode_with_extra_info=None,
gradient_checkpointing=False,
apply_pose_blendshape=False,
dense_sample_pts=40000, # only use for dense_smaple_smplx
gs_deform_scale=0.005,
render_features=False,
):
"""
Initializes the GS3DRenderer, a subclass of BaseGSRender for 3D Gaussian Splatting rendering.
Args:
human_model_path (str): Path to human model files.
subdivide_num (int): Subdivision number for base mesh.
smpl_type (str): Type of SMPL/SMPL-X/other model to use.
feat_dim (int): Dimension of feature embeddings.
query_dim (int): Dimension of query points/features.
use_rgb (bool): Whether to use RGB channels.
sh_degree (int): Spherical harmonics degree for appearance.
xyz_offset_max_step (float): Max offset per step for position.
mlp_network_config (dict or None): MLP configuration for feature mapping.
expr_param_dim (int): Expression parameter dimension.
shape_param_dim (int): Shape parameter dimension.
clip_scaling (float, optional): Output scaling for decoder. Default 0.2.
cano_pose_type (int, optional): Canonical pose type. Default 0.
decoder_mlp (bool, optional): Use MLP in decoder cross-attention. Default False.
skip_decoder (bool, optional): Whether to skip decoder and cross-attn layers. Default False.
fix_opacity (bool, optional): Fix opacity during training. Default False.
fix_rotation (bool, optional): Fix rotation during training. Default False.
decode_with_extra_info (dict or None, optional): Provide extra info to decoder. Default None.
gradient_checkpointing (bool, optional): Enable gradient checkpointing. Default False.
apply_pose_blendshape (bool, optional): Apply pose blendshape. Default False.
dense_sample_pts (int, optional): Dense sample points for mesh/voxel. Default 40000.
gs_deform_scale (float, optional): Deformation scale for Gaussian Splatting. Default 0.005.
render_features (bool, optional): Output additional features in renderer. Default False.
"""
super(GS3DRenderer, self).__init__(
human_model_path,
subdivide_num,
smpl_type,
feat_dim,
query_dim,
use_rgb,
sh_degree,
xyz_offset_max_step,
mlp_network_config,
expr_param_dim,
shape_param_dim,
clip_scaling,
cano_pose_type,
decoder_mlp,
skip_decoder,
fix_opacity,
fix_rotation,
decode_with_extra_info,
gradient_checkpointing,
apply_pose_blendshape,
dense_sample_pts, # only use for dense_smaple_smplx
gs_deform_scale,
render_features,
)
self.gs_net = GSMLPDecoder(
in_channels=query_dim,
use_rgb=use_rgb,
sh_degree=self.sh_degree,
clip_scaling=clip_scaling,
init_scaling=-6.0,
init_density=0.1,
xyz_offset=True,
restrict_offset=True,
xyz_offset_max_step=xyz_offset_max_step,
fix_opacity=fix_opacity,
fix_rotation=fix_rotation,
use_fine_feat=(
True
if decode_with_extra_info is not None
and decode_with_extra_info["type"] is not None
else False
),
)
self.gs_deform_scale = gs_deform_scale # deform mask scale.
def hyper_step(self, step):
"""using to adjust the constrain scale."""
self.gs_net.hyper_step(step)
def forward_gs_attr(
self, x, query_points, smplx_data, debug=False, x_fine=None, mesh_meta=None
):
"""
x: [N, C] Float[Tensor, "Np Cp"],
query_points: [N, 3] Float[Tensor, "Np 3"]
"""
device = x.device
if self.mlp_network_config is not None:
# x is processed by LayerNorm
x = self.mlp_net(x)
if x_fine is not None:
x_fine = self.mlp_net(x_fine)
# NOTE that gs_attr contains offset xyz
is_constrain_body = mesh_meta["is_constrain_body"].to(
self.smplx_model.is_constrain_body
)
is_hands = (mesh_meta["is_rhand"] + mesh_meta["is_lhand"]).to(
self.smplx_model.is_rhand
)
is_upper_body = mesh_meta["is_upper_body"].to(self.smplx_model.is_upper_body)
# is_constrain_body = self.smplx_model.is_constrain_body
# is_hands = self.smplx_model.is_rhand + self.smplx_model.is_lhand
# is_upper_body = self.smplx_model.is_upper_body
constrain_dict = dict(
is_constrain_body=is_constrain_body,
is_hands=is_hands,
is_upper_body=is_upper_body,
)
gs_attr: GaussianAppOutput = self.gs_net(
x, query_points, x_fine, constrain_dict
)
return gs_attr
def test():
import cv2
human_model_path = "./pretrained_models/human_model_files"
smplx_data_root = "/data1/projects/ExAvatar_RELEASE/avatar/data/Custom/data/gyeongsik/smplx_optimized/smplx_params_smoothed"
shape_param_file = "/data1/projects/ExAvatar_RELEASE/avatar/data/Custom/data/gyeongsik/smplx_optimized/shape_param.json"
from core.models.rendering.smpl_x import read_smplx_param
batch_size = 1
device = "cuda"
smplx_data, cam_param_list, ori_image_list = read_smplx_param(
smplx_data_root=smplx_data_root, shape_param_file=shape_param_file, batch_size=2
)
smplx_data_tmp = smplx_data
for k, v in smplx_data.items():
smplx_data_tmp[k] = v.unsqueeze(0)
if (k == "betas") or (k == "face_offset") or (k == "joint_offset"):
smplx_data_tmp[k] = v[0].unsqueeze(0)
smplx_data = smplx_data_tmp
gs_render = GS3DRenderer(
human_model_path=human_model_path,
subdivide_num=2,
smpl_type="smplx",
feat_dim=64,
query_dim=64,
use_rgb=False,
sh_degree=3,
mlp_network_config=None,
xyz_offset_max_step=1.8 / 32,
)
gs_render.to(device)
# print(cam_param_list[0])
c2w_list = []
intr_list = []
for cam_param in cam_param_list:
c2w = torch.eye(4).to(device)
c2w[:3, :3] = cam_param["R"]
c2w[:3, 3] = cam_param["t"]
c2w_list.append(c2w)
intr = torch.eye(4).to(device)
intr[0, 0] = cam_param["focal"][0]
intr[1, 1] = cam_param["focal"][1]
intr[0, 2] = cam_param["princpt"][0]
intr[1, 2] = cam_param["princpt"][1]
intr_list.append(intr)
c2w = torch.stack(c2w_list).unsqueeze(0)
intrinsic = torch.stack(intr_list).unsqueeze(0)
out = gs_render.forward(
gs_hidden_features=torch.zeros((batch_size, 2048, 64)).float().to(device),
query_points=None,
smplx_data=smplx_data,
c2w=c2w,
intrinsic=intrinsic,
height=int(cam_param_list[0]["princpt"][1]) * 2,
width=int(cam_param_list[0]["princpt"][0]) * 2,
background_color=torch.tensor([1.0, 1.0, 1.0])
.float()
.view(1, 1, 3)
.repeat(batch_size, 2, 1)
.to(device),
debug=False,
)
for k, v in out.items():
if k == "comp_rgb_bg":
print("comp_rgb_bg", v)
continue
for b_idx in range(len(v)):
if k == "3dgs":
for v_idx in range(len(v[b_idx])):
v[b_idx][v_idx].save_ply(f"./debug_vis/{b_idx}_{v_idx}.ply")
continue
for v_idx in range(v.shape[1]):
save_path = os.path.join("./debug_vis", f"{b_idx}_{v_idx}_{k}.jpg")
cv2.imwrite(
save_path,
(v[b_idx, v_idx].detach().cpu().numpy() * 255).astype(np.uint8),
)
|