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A10G
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import os
import json
import tqdm
import cv2
import numpy as np
import torch, lrm
import torch.nn.functional as F
from lrm.utils.config import load_config
from datetime import datetime
import gradio as gr
from pygltflib import GLTF2
from PIL import Image
from huggingface_hub import hf_hub_download
from refine import refine
device = "cuda"
import trimesh
import pymeshlab
import numpy as np
from huggingface_hub import hf_hub_download, list_repo_files
repo_id = "zjpshadow/CharacterGen"
all_files = list_repo_files(repo_id, revision="main")
for file in all_files:
if os.path.exists("../" + file):
continue
if file.startswith("3D_Stage"):
hf_hub_download(repo_id, file, local_dir="../")
def traverse(path, back_proj):
mesh = trimesh.load(f"{path}/model-00.obj")
mesh.apply_transform(trimesh.transformations.rotation_matrix(np.radians(90.0), [-1, 0, 0]))
mesh.apply_transform(trimesh.transformations.rotation_matrix(np.radians(180.0), [0, 1, 0]))
cmesh = pymeshlab.Mesh(mesh.vertices, mesh.faces)
ms = pymeshlab.MeshSet()
ms.add_mesh(cmesh)
ms.apply_coord_laplacian_smoothing(stepsmoothnum=4)
mesh.vertices = ms.current_mesh().vertex_matrix()
mesh.export(f'{path}/output.glb', file_type='glb')
image = Image.open(f"{path}/{'refined_texture_kd.jpg' if back_proj else 'texture_kd.jpg'}")
texture = np.array(image)
vertex_colors = np.zeros((mesh.vertices.shape[0], 4), dtype=np.uint8)
for vertex_index in range(len(mesh.visual.uv)):
uv = mesh.visual.uv[vertex_index]
x = int(uv[0] * (texture.shape[1] - 1))
y = int((1 - uv[1]) * (texture.shape[0] - 1))
color = texture[y, x, :3]
vertex_colors[vertex_index] = [color[0], color[1], color[2], 255]
return trimesh.Trimesh(vertices=mesh.vertices, faces=mesh.faces, vertex_colors=vertex_colors)
class Inference_API:
def __init__(self):
# Load config
self.cfg = load_config("configs/infer.yaml", makedirs=False)
# Load system
print("Loading system")
self.system = lrm.find(self.cfg.system_cls)(self.cfg.system).to(device)
self.system.eval()
def process_images(self, img_input0, img_input1, img_input2, img_input3, back_proj):
meta = json.load(open("material/meta.json"))
c2w_cond = [np.array(loc["transform_matrix"]) for loc in meta["locations"]]
c2w_cond = torch.from_numpy(np.stack(c2w_cond, axis=0)).float()[None].to(device)
# Prepare input data
rgb_cond = []
files = [img_input0, img_input1, img_input2, img_input3]
new_image = []
for file in files:
image = np.array(file)
image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
new_image.append(Image.fromarray(image.astype(np.uint8)).convert("RGB"))
rgb = cv2.resize(image, (self.cfg.data.cond_width,
self.cfg.data.cond_height)).astype(np.float32) / 255.0
rgb_cond.append(rgb)
assert len(rgb_cond) == 4, "Please provide 4 images"
rgb_cond = torch.from_numpy(np.stack(rgb_cond, axis=0)).float()[None].to(device)
# Run inference
with torch.no_grad():
scene_codes = self.system({"rgb_cond": rgb_cond, "c2w_cond": c2w_cond})
exporter_output = self.system.exporter([f"{i:02d}" for i in range(rgb_cond.shape[0])], scene_codes)
# Save output
save_dir = os.path.join("./outputs", datetime.now().strftime("@%Y%m%d-%H%M%S"))
os.makedirs(save_dir, exist_ok=True)
self.system.set_save_dir(save_dir)
for out in exporter_output:
save_func_name = f"save_{out.save_type}"
save_func = getattr(self.system, save_func_name)
save_func(f"{out.save_name}", **out.params)
if back_proj:
refine(save_dir, new_image[1], new_image[0], new_image[3], new_image[2])
new_obj = traverse(save_dir, back_proj)
new_obj.export(f'{save_dir}/output.obj', file_type='obj')
gltf = GLTF2().load(f'{save_dir}/output.glb')
for material in gltf.materials:
if material.pbrMetallicRoughness:
material.pbrMetallicRoughness.baseColorFactor = [1.0, 1.0, 1.0, 100.0]
material.pbrMetallicRoughness.metallicFactor = 0.0
material.pbrMetallicRoughness.roughnessFactor = 1.0
gltf.save(f'{save_dir}/output.glb')
return save_dir, f"{save_dir}/output.obj", f"{save_dir}/output.glb"
inferapi = Inference_API()
# Define the interface
with gr.Blocks() as demo:
gr.Markdown("# [SIGGRAPH'24] CharacterGen: Efficient 3D Character Generation from Single Images with Multi-View Pose Calibration")
gr.Markdown("# 3D Stage: Four View Images to 3D Mesh")
with gr.Row(variant="panel"):
with gr.Column():
with gr.Row():
img_input0 = gr.Image(type="pil", label="Back Image", image_mode="RGBA", width=256, height=384)
img_input1 = gr.Image(type="pil", label="Front Image", image_mode="RGBA", width=256, height=384)
with gr.Row():
img_input2 = gr.Image(type="pil", label="Right Image", image_mode="RGBA", width=256, height=384)
img_input3 = gr.Image(type="pil", label="Left Image", image_mode="RGBA", width=256, height=384)
with gr.Row():
gr.Examples(
examples=
[["material/examples/1/1.png",
"material/examples/1/2.png",
"material/examples/1/3.png",
"material/examples/1/4.png"]],
label="Example Images",
inputs=[img_input0, img_input1, img_input2, img_input3]
)
with gr.Column():
with gr.Row():
back_proj = gr.Checkbox(label="Back Projection")
submit_button = gr.Button("Process")
output_dir = gr.Textbox(label="Output Directory")
with gr.Column():
with gr.Tab("GLB"):
output_model_glb = gr.Model3D( label="Output Model (GLB Format)", height = 768)
gr.Markdown("Note: The model shown here has a darker appearance. Download to get correct results.")
with gr.Tab("OBJ"):
output_model_obj = gr.Model3D( label="Output Model (OBJ Format)", height = 768)
gr.Markdown("Note: The model shown here is flipped. Download to get correct results.")
submit_button.click(inferapi.process_images, inputs=[img_input0, img_input1, img_input2, img_input3, back_proj],
outputs=[output_dir, output_model_obj, output_model_glb])
# Run the interface
demo.launch() |