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| import sys | |
| import os | |
| import OpenGL.GL as gl | |
| os.environ["PYOPENGL_PLATFORM"] = "egl" | |
| os.environ["MESA_GL_VERSION_OVERRIDE"] = "4.1" | |
| sys.argv = ['VQ-Trans/GPT_eval_multi.py'] | |
| os.chdir('VQ-Trans') | |
| sys.path.append('/home/user/app/VQ-Trans') | |
| sys.path.append('/home/user/app/VQ-Trans/pyrender') | |
| import options.option_transformer as option_trans | |
| from huggingface_hub import snapshot_download | |
| model_path = snapshot_download(repo_id="vumichien/T2M-GPT") | |
| args = option_trans.get_args_parser() | |
| args.dataname = 't2m' | |
| args.resume_pth = f'{model_path}/VQVAE/net_last.pth' | |
| args.resume_trans = f'{model_path}/VQTransformer_corruption05/net_best_fid.pth' | |
| args.down_t = 2 | |
| args.depth = 3 | |
| args.block_size = 51 | |
| import clip | |
| import torch | |
| import numpy as np | |
| import models.vqvae as vqvae | |
| import models.t2m_trans as trans | |
| from utils.motion_process import recover_from_ric | |
| import visualization.plot_3d_global as plot_3d | |
| from models.rotation2xyz import Rotation2xyz | |
| import numpy as np | |
| from trimesh import Trimesh | |
| import gc | |
| import torch | |
| from visualize.simplify_loc2rot import joints2smpl | |
| import pyrender | |
| # import matplotlib.pyplot as plt | |
| import io | |
| import imageio | |
| from shapely import geometry | |
| import trimesh | |
| from pyrender.constants import RenderFlags | |
| import math | |
| # import ffmpeg | |
| # from PIL import Image | |
| import hashlib | |
| import gradio as gr | |
| ## load clip model and datasets | |
| is_cuda = torch.cuda.is_available() | |
| device = torch.device("cuda" if is_cuda else "cpu") | |
| print(device) | |
| clip_model, clip_preprocess = clip.load("ViT-B/32", device=device, jit=False, download_root='./') # Must set jit=False for training | |
| if is_cuda: | |
| clip.model.convert_weights(clip_model) | |
| clip_model.eval() | |
| for p in clip_model.parameters(): | |
| p.requires_grad = False | |
| net = vqvae.HumanVQVAE(args, ## use args to define different parameters in different quantizers | |
| args.nb_code, | |
| args.code_dim, | |
| args.output_emb_width, | |
| args.down_t, | |
| args.stride_t, | |
| args.width, | |
| args.depth, | |
| args.dilation_growth_rate) | |
| trans_encoder = trans.Text2Motion_Transformer(num_vq=args.nb_code, | |
| embed_dim=1024, | |
| clip_dim=args.clip_dim, | |
| block_size=args.block_size, | |
| num_layers=9, | |
| n_head=16, | |
| drop_out_rate=args.drop_out_rate, | |
| fc_rate=args.ff_rate) | |
| print('loading checkpoint from {}'.format(args.resume_pth)) | |
| ckpt = torch.load(args.resume_pth, map_location='cpu') | |
| net.load_state_dict(ckpt['net'], strict=True) | |
| net.eval() | |
| print('loading transformer checkpoint from {}'.format(args.resume_trans)) | |
| ckpt = torch.load(args.resume_trans, map_location='cpu') | |
| trans_encoder.load_state_dict(ckpt['trans'], strict=True) | |
| trans_encoder.eval() | |
| mean = torch.from_numpy(np.load(f'{model_path}/meta/mean.npy')) | |
| std = torch.from_numpy(np.load(f'{model_path}/meta/std.npy')) | |
| if is_cuda: | |
| net.cuda() | |
| trans_encoder.cuda() | |
| mean = mean.cuda() | |
| std = std.cuda() | |
| def render(motions, device_id=0, name='test_vis'): | |
| frames, njoints, nfeats = motions.shape | |
| MINS = motions.min(axis=0).min(axis=0) | |
| MAXS = motions.max(axis=0).max(axis=0) | |
| height_offset = MINS[1] | |
| motions[:, :, 1] -= height_offset | |
| trajec = motions[:, 0, [0, 2]] | |
| is_cuda = torch.cuda.is_available() | |
| # device = torch.device("cuda" if is_cuda else "cpu") | |
| j2s = joints2smpl(num_frames=frames, device_id=0, cuda=is_cuda) | |
| rot2xyz = Rotation2xyz(device=device) | |
| faces = rot2xyz.smpl_model.faces | |
| if not os.path.exists(f'output/{name}_pred.pt'): | |
| print(f'Running SMPLify, it may take a few minutes.') | |
| motion_tensor, opt_dict = j2s.joint2smpl(motions) # [nframes, njoints, 3] | |
| vertices = rot2xyz(torch.tensor(motion_tensor).clone(), mask=None, | |
| pose_rep='rot6d', translation=True, glob=True, | |
| jointstype='vertices', | |
| vertstrans=True) | |
| vertices = vertices.detach().cpu() | |
| torch.save(vertices, f'output/{name}_pred.pt') | |
| else: | |
| vertices = torch.load(f'output/{name}_pred.pt') | |
| frames = vertices.shape[3] # shape: 1, nb_frames, 3, nb_joints | |
| print(vertices.shape) | |
| MINS = torch.min(torch.min(vertices[0], axis=0)[0], axis=1)[0] | |
| MAXS = torch.max(torch.max(vertices[0], axis=0)[0], axis=1)[0] | |
| out_list = [] | |
| minx = MINS[0] - 0.5 | |
| maxx = MAXS[0] + 0.5 | |
| minz = MINS[2] - 0.5 | |
| maxz = MAXS[2] + 0.5 | |
| polygon = geometry.Polygon([[minx, minz], [minx, maxz], [maxx, maxz], [maxx, minz]]) | |
| polygon_mesh = trimesh.creation.extrude_polygon(polygon, 1e-5) | |
| vid = [] | |
| for i in range(frames): | |
| if i % 10 == 0: | |
| print(i) | |
| mesh = Trimesh(vertices=vertices[0, :, :, i].squeeze().tolist(), faces=faces) | |
| base_color = (0.11, 0.53, 0.8, 0.5) | |
| ## OPAQUE rendering without alpha | |
| ## BLEND rendering consider alpha | |
| material = pyrender.MetallicRoughnessMaterial( | |
| metallicFactor=0.7, | |
| alphaMode='OPAQUE', | |
| baseColorFactor=base_color | |
| ) | |
| mesh = pyrender.Mesh.from_trimesh(mesh, material=material) | |
| polygon_mesh.visual.face_colors = [0, 0, 0, 0.21] | |
| polygon_render = pyrender.Mesh.from_trimesh(polygon_mesh, smooth=False) | |
| bg_color = [1, 1, 1, 0.8] | |
| scene = pyrender.Scene(bg_color=bg_color, ambient_light=(0.4, 0.4, 0.4)) | |
| sx, sy, tx, ty = [0.75, 0.75, 0, 0.10] | |
| camera = pyrender.PerspectiveCamera(yfov=(np.pi / 3.0)) | |
| light = pyrender.DirectionalLight(color=[1,1,1], intensity=300) | |
| scene.add(mesh) | |
| c = np.pi / 2 | |
| scene.add(polygon_render, pose=np.array([[ 1, 0, 0, 0], | |
| [ 0, np.cos(c), -np.sin(c), MINS[1].cpu().numpy()], | |
| [ 0, np.sin(c), np.cos(c), 0], | |
| [ 0, 0, 0, 1]])) | |
| light_pose = np.eye(4) | |
| light_pose[:3, 3] = [0, -1, 1] | |
| scene.add(light, pose=light_pose.copy()) | |
| light_pose[:3, 3] = [0, 1, 1] | |
| scene.add(light, pose=light_pose.copy()) | |
| light_pose[:3, 3] = [1, 1, 2] | |
| scene.add(light, pose=light_pose.copy()) | |
| c = -np.pi / 6 | |
| scene.add(camera, pose=[[ 1, 0, 0, (minx+maxx).cpu().numpy()/2], | |
| [ 0, np.cos(c), -np.sin(c), 1.5], | |
| [ 0, np.sin(c), np.cos(c), max(4, minz.cpu().numpy()+(1.5-MINS[1].cpu().numpy())*2, (maxx-minx).cpu().numpy())], | |
| [ 0, 0, 0, 1] | |
| ]) | |
| # render scene | |
| r = pyrender.OffscreenRenderer(960, 960) | |
| color, _ = r.render(scene, flags=RenderFlags.RGBA) | |
| # Image.fromarray(color).save(outdir+'/'+name+'_'+str(i)+'.png') | |
| vid.append(color) | |
| r.delete() | |
| out = np.stack(vid, axis=0) | |
| imageio.mimwrite(f'output/results.gif', out, fps=20) | |
| del out, vertices | |
| return f'output/results.gif' | |
| def predict(clip_text, method='fast'): | |
| gc.collect() | |
| if torch.cuda.is_available(): | |
| text = clip.tokenize([clip_text], truncate=True).cuda() | |
| else: | |
| text = clip.tokenize([clip_text], truncate=True) | |
| feat_clip_text = clip_model.encode_text(text).float() | |
| index_motion = trans_encoder.sample(feat_clip_text[0:1], False) | |
| pred_pose = net.forward_decoder(index_motion) | |
| pred_xyz = recover_from_ric((pred_pose*std+mean).float(), 22) | |
| output_name = hashlib.md5(clip_text.encode()).hexdigest() | |
| if method == 'fast': | |
| xyz = pred_xyz.reshape(1, -1, 22, 3) | |
| pose_vis = plot_3d.draw_to_batch(xyz.detach().cpu().numpy(), title_batch=None, outname=[f'output/results.gif']) | |
| return f'output/results.gif' | |
| elif method == 'slow': | |
| output_path = render(pred_xyz.detach().cpu().numpy().squeeze(axis=0), device_id=0, name=output_name) | |
| return output_path | |
| # ---- Gradio Layout ----- | |
| text_prompt = gr.Textbox(label="Text prompt", lines=1, interactive=True) | |
| video_out = gr.Video(label="Motion", mirror_webcam=False, interactive=False) | |
| demo = gr.Blocks() | |
| demo.encrypt = False | |
| with demo: | |
| gr.Markdown(''' | |
| <div> | |
| <h1 style='text-align: center'>Generating Human Motion from Textual Descriptions with Discrete Representations (T2M-GPT)</h1> | |
| This space uses <a href='https://mael-zys.github.io/T2M-GPT/' target='_blank'><b>T2M-GPT models</b></a> based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and Generative Pre-trained Transformer (GPT) for human motion generation from textural descriptions🤗 | |
| </div> | |
| ''') | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown(''' | |
| <figure> | |
| <img src="https://huggingface.co/vumichien/T2M-GPT/resolve/main/demo_slow1.gif" alt="Demo Slow", width="425", height=480/> | |
| <figcaption> a man starts off in an up right position with botg arms extended out by his sides, he then brings his arms down to his body and claps his hands together. after this he wals down amd the the left where he proceeds to sit on a seat | |
| </figcaption> | |
| </figure> | |
| ''') | |
| with gr.Column(): | |
| gr.Markdown(''' | |
| <figure> | |
| <img src="https://huggingface.co/vumichien/T2M-GPT/resolve/main/demo_slow2.gif" alt="Demo Slow 2", width="425", height=480/> | |
| <figcaption> a person puts their hands together, leans forwards slightly then swings the arms from right to left | |
| </figcaption> | |
| </figure> | |
| ''') | |
| with gr.Column(): | |
| gr.Markdown(''' | |
| <figure> | |
| <img src="https://huggingface.co/vumichien/T2M-GPT/resolve/main/demo_slow3.gif" alt="Demo Slow 3", width="425", height=480/> | |
| <figcaption> a man is practicing the waltz with a partner | |
| </figcaption> | |
| </figure> | |
| ''') | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown(''' | |
| ### Generate human motion by **T2M-GPT** | |
| ##### Step 1. Give prompt text describing human motion | |
| ##### Step 2. Choice method to generate output (Fast: Sketch skeleton; Slow: SMPL mesh) | |
| ##### Step 3. Generate output and enjoy | |
| ''') | |
| with gr.Row(): | |
| gr.Markdown(''' | |
| ### You can test by following examples: | |
| ''') | |
| examples = gr.Examples(examples= | |
| [ "a person jogs in place, slowly at first, then increases speed. they then back up and squat down.", | |
| "a man steps forward and does a handstand", | |
| "a man rises from the ground, walks in a circle and sits back down on the ground"], | |
| label="Examples", inputs=[text_prompt]) | |
| with gr.Column(): | |
| with gr.Row(): | |
| text_prompt.render() | |
| method = gr.Dropdown(["slow", "fast"], label="Method", value="fast") | |
| with gr.Row(): | |
| generate_btn = gr.Button("Generate") | |
| generate_btn.click(predict, [text_prompt, method], [video_out]) | |
| print(video_out) | |
| with gr.Row(): | |
| video_out.render() | |
| demo.launch(debug=True) | |