import os import torch import trimesh from accelerate.utils import set_seed from accelerate import Accelerator import numpy as np import gradio as gr from main import get_args, load_model from mesh_to_pc import process_mesh_to_pc import time import matplotlib.pyplot as plt from mpl_toolkits.mplot3d.art3d import Poly3DCollection from PIL import Image import io args = get_args() model = load_model(args) device = torch.device('cuda') accelerator = Accelerator( mixed_precision="fp16", ) model = accelerator.prepare(model) model.eval() print("Model loaded to device") def wireframe_render(mesh): views = [ (90, 20), (270, 20) ] mesh.vertices = mesh.vertices[:, [0, 2, 1]] bounding_box = mesh.bounds center = mesh.centroid scale = np.ptp(bounding_box, axis=0).max() fig = plt.figure(figsize=(10, 10)) # Function to render and return each view as an image def render_view(mesh, azimuth, elevation): ax = fig.add_subplot(111, projection='3d') ax.set_axis_off() # Extract vertices and faces for plotting vertices = mesh.vertices faces = mesh.faces # Plot faces ax.add_collection3d(Poly3DCollection( vertices[faces], facecolors=(0.8, 0.5, 0.2, 1.0), # Brownish yellow edgecolors='k', linewidths=0.5, )) # Set limits and center the view on the object ax.set_xlim(center[0] - scale / 2, center[0] + scale / 2) ax.set_ylim(center[1] - scale / 2, center[1] + scale / 2) ax.set_zlim(center[2] - scale / 2, center[2] + scale / 2) # Set view angle ax.view_init(elev=elevation, azim=azimuth) # Save the figure to a buffer buf = io.BytesIO() plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0, dpi=300) plt.clf() buf.seek(0) return Image.open(buf) # Render each view and store in a list images = [render_view(mesh, az, el) for az, el in views] # Combine images horizontally widths, heights = zip(*(i.size for i in images)) total_width = sum(widths) max_height = max(heights) combined_image = Image.new('RGBA', (total_width, max_height)) x_offset = 0 for img in images: combined_image.paste(img, (x_offset, 0)) x_offset += img.width # Save the combined image save_path = f"combined_mesh_view_{int(time.time())}.png" combined_image.save(save_path) plt.close(fig) return save_path @torch.no_grad() def do_inference(input_3d, sample_seed=0, do_sampling=False, do_marching_cubes=False): set_seed(sample_seed) print("Seed value:", sample_seed) input_mesh = trimesh.load(input_3d) pc_list, mesh_list = process_mesh_to_pc([input_mesh], marching_cubes = do_marching_cubes) pc_normal = pc_list[0] # 4096, 6 mesh = mesh_list[0] vertices = mesh.vertices pc_coor = pc_normal[:, :3] normals = pc_normal[:, 3:] bounds = np.array([vertices.min(axis=0), vertices.max(axis=0)]) # scale mesh and pc vertices = vertices - (bounds[0] + bounds[1])[None, :] / 2 vertices = vertices / (bounds[1] - bounds[0]).max() mesh.vertices = vertices pc_coor = pc_coor - (bounds[0] + bounds[1])[None, :] / 2 pc_coor = pc_coor / (bounds[1] - bounds[0]).max() mesh.merge_vertices() mesh.update_faces(mesh.unique_faces()) mesh.fix_normals() if mesh.visual.vertex_colors is not None: orange_color = np.array([255, 165, 0, 255], dtype=np.uint8) mesh.visual.vertex_colors = np.tile(orange_color, (mesh.vertices.shape[0], 1)) else: orange_color = np.array([255, 165, 0, 255], dtype=np.uint8) mesh.visual.vertex_colors = np.tile(orange_color, (mesh.vertices.shape[0], 1)) input_save_name = f"processed_input_{int(time.time())}.obj" mesh.export(input_save_name) input_render_res = wireframe_render(mesh) pc_coor = pc_coor / np.abs(pc_coor).max() * 0.9995 # input should be from -1 to 1 assert (np.linalg.norm(normals, axis=-1) > 0.99).all(), "normals should be unit vectors, something wrong" normalized_pc_normal = np.concatenate([pc_coor, normals], axis=-1, dtype=np.float16) input = torch.tensor(normalized_pc_normal, dtype=torch.float16, device=device)[None] print("Data loaded") # with accelerator.autocast(): with accelerator.autocast(): outputs = model(input, do_sampling) print("Model inference done") recon_mesh = outputs[0] recon_mesh = recon_mesh[~torch.isnan(recon_mesh[:, 0, 0])] # nvalid_face x 3 x 3 vertices = recon_mesh.reshape(-1, 3).cpu() vertices_index = np.arange(len(vertices)) # 0, 1, ..., 3 x face triangles = vertices_index.reshape(-1, 3) artist_mesh = trimesh.Trimesh(vertices=vertices, faces=triangles, force="mesh", merge_primitives=True) artist_mesh.merge_vertices() artist_mesh.update_faces(artist_mesh.unique_faces()) artist_mesh.fix_normals() if artist_mesh.visual.vertex_colors is not None: orange_color = np.array([255, 165, 0, 255], dtype=np.uint8) artist_mesh.visual.vertex_colors = np.tile(orange_color, (artist_mesh.vertices.shape[0], 1)) else: orange_color = np.array([255, 165, 0, 255], dtype=np.uint8) artist_mesh.visual.vertex_colors = np.tile(orange_color, (artist_mesh.vertices.shape[0], 1)) num_faces = len(artist_mesh.faces) brown_color = np.array([165, 42, 42, 255], dtype=np.uint8) face_colors = np.tile(brown_color, (num_faces, 1)) artist_mesh.visual.face_colors = face_colors # add time stamp to avoid cache save_name = f"output_{int(time.time())}.obj" artist_mesh.export(save_name) output_render = wireframe_render(artist_mesh) return input_save_name, input_render_res, save_name, output_render _HEADER_ = '''

Official ? Gradio Demo

MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers

**MeshAnything** converts any 3D representation into meshes created by human artists, i.e., Artist-Created Meshes (AMs). Code: GitHub. Arxiv Paper: ArXiv. ??????**Important Notes:** - Gradio doesn't support interactive wireframe rendering currently. For interactive mesh visualization, please use download the obj file and open it with MeshLab or https://3dviewer.net/. - The input mesh will be normalized to a unit bounding box. The up vector of the input mesh should be +Y for better results. Click **Preprocess with Marching Cubes** if the input mesh is a manually created mesh. - Limited by computational resources, MeshAnything is trained on meshes with fewer than 800 faces and cannot generate meshes with more than 800 faces. The shape of the input mesh should be sharp enough; otherwise, it will be challenging to represent it with only 800 faces. Thus, feed-forward image-to-3D methods may often produce bad results due to insufficient shape quality. - For point cloud input, please refer to our github repo GitHub. ''' _CITE_ = r""" If MeshAnything is helpful, please help to ? the Github Repo. Thanks! --- ? **License** S-Lab-1.0 LICENSE. Please refer to the [LICENSE file](https://github.com/buaacyw/GaussianEditor/blob/master/LICENSE.txt) for details. ? **Contact** If you have any questions, feel free to open a discussion or contact us at yiwen002@e.ntu.edu.sg. """ output_model_obj = gr.Model3D( label="Generated Mesh (OBJ Format)", clear_color=[1, 1, 1, 1], ) preprocess_model_obj = gr.Model3D( label="Processed Input Mesh (OBJ Format)", clear_color=[1, 1, 1, 1], ) input_image_render = gr.Image( label="Wireframe Render of Processed Input Mesh", ) output_image_render = gr.Image( label="Wireframe Render of Generated Mesh", ) with (gr.Blocks() as demo): gr.Markdown(_HEADER_) with gr.Row(variant="panel"): with gr.Column(): with gr.Row(): input_3d = gr.Model3D( label="Input Mesh", clear_color=[1,1,1,1], ) with gr.Row(): with gr.Group(): do_marching_cubes = gr.Checkbox(label="Preprocess with Marching Cubes", value=False) do_sampling = gr.Checkbox(label="Random Sampling", value=False) sample_seed = gr.Number(value=0, label="Seed Value", precision=0) with gr.Row(): submit = gr.Button("Generate", elem_id="generate", variant="primary") with gr.Row(variant="panel"): mesh_examples = gr.Examples( examples=[ os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples")) ], inputs=input_3d, outputs=[preprocess_model_obj, input_image_render, output_model_obj, output_image_render], fn=do_inference, cache_examples = False, examples_per_page=10 ) with gr.Column(): with gr.Row(): input_image_render.render() with gr.Row(): with gr.Tab("OBJ"): preprocess_model_obj.render() with gr.Row(): output_image_render.render() with gr.Row(): with gr.Tab("OBJ"): output_model_obj.render() with gr.Row(): gr.Markdown('''Try click random sampling and different Seed Value if the result is unsatisfying''') gr.Markdown(_CITE_) mv_images = gr.State() submit.click( fn=do_inference, inputs=[input_3d, sample_seed, do_sampling, do_marching_cubes], outputs=[preprocess_model_obj, input_image_render, output_model_obj, output_image_render], ) demo.launch(share=True)