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os.environ["PYOPENGL_PLATFORM"] = "egl" |
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os.environ["MESA_GL_VERSION_OVERRIDE"] = "4.1" |
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os.system('pip install /home/user/app/pyrender') |
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|
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import gradio as gr |
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import random |
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import torch |
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import time |
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import cv2 |
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import os |
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import numpy as np |
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import OpenGL.GL as gl |
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import imageio |
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import pytorch_lightning as pl |
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import moviepy.editor as mp |
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from pathlib import Path |
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from mGPT.data.build_data import build_data |
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from mGPT.models.build_model import build_model |
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from mGPT.config import parse_args |
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from scipy.spatial.transform import Rotation as RRR |
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import mGPT.render.matplot.plot_3d_global as plot_3d |
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from mGPT.render.pyrender.hybrik_loc2rot import HybrIKJointsToRotmat |
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from mGPT.render.pyrender.smpl_render import SMPLRender |
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from transformers import WhisperProcessor, WhisperForConditionalGeneration |
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import librosa |
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from huggingface_hub import snapshot_download |
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cfg = parse_args(phase="webui") |
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cfg.FOLDER = 'cache' |
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output_dir = Path(cfg.FOLDER) |
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output_dir.mkdir(parents=True, exist_ok=True) |
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pl.seed_everything(cfg.SEED_VALUE) |
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if torch.cuda.is_available(): |
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device = torch.device("cuda") |
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else: |
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device = torch.device("cpu") |
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model_path = snapshot_download(repo_id="bill-jiang/MotionGPT-base") |
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datamodule = build_data(cfg, phase="test") |
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model = build_model(cfg, datamodule) |
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state_dict = torch.load(f'{model_path}/motiongpt_s3_h3d.tar', |
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map_location="cpu")["state_dict"] |
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model.load_state_dict(state_dict) |
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model.to(device) |
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audio_processor = WhisperProcessor.from_pretrained(cfg.model.whisper_path) |
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audio_model = WhisperForConditionalGeneration.from_pretrained( |
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cfg.model.whisper_path).to(device) |
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forced_decoder_ids_zh = audio_processor.get_decoder_prompt_ids( |
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language="zh", task="translate") |
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forced_decoder_ids_en = audio_processor.get_decoder_prompt_ids( |
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language="en", task="translate") |
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Video_Components = """ |
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<div class="side-video" style="position: relative;"> |
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<video width="340" autoplay loop> |
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<source src="file/{video_path}" type="video/mp4"> |
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</video> |
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<a class="videodl-button" href="file/{video_path}" download="{video_fname}" title="Download Video"> |
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<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="#000000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-video"><path d="m22 8-6 4 6 4V8Z"/><rect width="14" height="12" x="2" y="6" rx="2" ry="2"/></svg> |
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</a> |
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<a class="npydl-button" href="file/{motion_path}" download="{motion_fname}" title="Download Motion"> |
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<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="#000000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-file-box"><path d="M14.5 22H18a2 2 0 0 0 2-2V7.5L14.5 2H6a2 2 0 0 0-2 2v4"/><polyline points="14 2 14 8 20 8"/><path d="M2.97 13.12c-.6.36-.97 1.02-.97 1.74v3.28c0 .72.37 1.38.97 1.74l3 1.83c.63.39 1.43.39 2.06 0l3-1.83c.6-.36.97-1.02.97-1.74v-3.28c0-.72-.37-1.38-.97-1.74l-3-1.83a1.97 1.97 0 0 0-2.06 0l-3 1.83Z"/><path d="m7 17-4.74-2.85"/><path d="m7 17 4.74-2.85"/><path d="M7 17v5"/></svg> |
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</a> |
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</div> |
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""" |
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Video_Components_example = """ |
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<div class="side-video" style="position: relative;"> |
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<video width="340" autoplay loop controls> |
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<source src="file/{video_path}" type="video/mp4"> |
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</video> |
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<a class="npydl-button" href="file/{video_path}" download="{video_fname}" title="Download Video"> |
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<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-video"><path d="m22 8-6 4 6 4V8Z"/><rect width="14" height="12" x="2" y="6" rx="2" ry="2"/></svg> |
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</a> |
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</div> |
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""" |
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Text_Components = """ |
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<h3 class="side-content" >{msg}</h3> |
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""" |
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def motion_token_to_string(motion_token, lengths, codebook_size=512): |
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motion_string = [] |
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for i in range(motion_token.shape[0]): |
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motion_i = motion_token[i].cpu( |
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) if motion_token.device.type == 'cuda' else motion_token[i] |
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motion_list = motion_i.tolist()[:lengths[i]] |
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motion_string.append( |
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(f'<motion_id_{codebook_size}>' + |
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''.join([f'<motion_id_{int(i)}>' for i in motion_list]) + |
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f'<motion_id_{codebook_size + 1}>')) |
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return motion_string |
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def render_motion(data, feats, method='fast'): |
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fname = time.strftime("%Y-%m-%d-%H_%M_%S", time.localtime( |
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time.time())) + str(np.random.randint(10000, 99999)) |
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video_fname = fname + '.mp4' |
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feats_fname = fname + '.npy' |
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output_npy_path = os.path.join(output_dir, feats_fname) |
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output_mp4_path = os.path.join(output_dir, video_fname) |
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np.save(output_npy_path, feats) |
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if method == 'slow': |
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if len(data.shape) == 4: |
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data = data[0] |
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data = data - data[0, 0] |
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pose_generator = HybrIKJointsToRotmat() |
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pose = pose_generator(data) |
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pose = np.concatenate([ |
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pose, |
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np.stack([np.stack([np.eye(3)] * pose.shape[0], 0)] * 2, 1) |
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], 1) |
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shape = [768, 768] |
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render = SMPLRender(cfg.RENDER.SMPL_MODEL_PATH) |
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r = RRR.from_rotvec(np.array([np.pi, 0.0, 0.0])) |
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pose[:, 0] = np.matmul(r.as_matrix().reshape(1, 3, 3), pose[:, 0]) |
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vid = [] |
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aroot = data[[0], 0] |
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aroot[:, 1] = -aroot[:, 1] |
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params = dict(pred_shape=np.zeros([1, 10]), |
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pred_root=aroot, |
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pred_pose=pose) |
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render.init_renderer([shape[0], shape[1], 3], params) |
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for i in range(data.shape[0]): |
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renderImg = render.render(i) |
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vid.append(renderImg) |
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out = np.stack(vid, axis=0) |
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output_gif_path = output_mp4_path[:-4] + '.gif' |
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imageio.mimwrite(output_gif_path, out, duration=50) |
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out_video = mp.VideoFileClip(output_gif_path) |
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out_video.write_videofile(output_mp4_path) |
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del out, render |
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elif method == 'fast': |
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output_gif_path = output_mp4_path[:-4] + '.gif' |
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if len(data.shape) == 3: |
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data = data[None] |
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if isinstance(data, torch.Tensor): |
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data = data.cpu().numpy() |
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pose_vis = plot_3d.draw_to_batch(data, [''], [output_gif_path]) |
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out_video = mp.VideoFileClip(output_gif_path) |
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out_video.write_videofile(output_mp4_path) |
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del pose_vis |
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return output_mp4_path, video_fname, output_npy_path, feats_fname |
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def load_motion(motion_uploaded, method): |
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file = motion_uploaded['file'] |
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feats = torch.tensor(np.load(file), device=model.device) |
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if len(feats.shape) == 2: |
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feats = feats[None] |
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motion_lengths = feats.shape[0] |
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motion_token, _ = model.vae.encode(feats) |
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motion_token_string = model.lm.motion_token_to_string( |
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motion_token, [motion_token.shape[1]])[0] |
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motion_token_length = motion_token.shape[1] |
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joints = model.datamodule.feats2joints(feats.cpu()).cpu().numpy() |
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output_mp4_path, video_fname, output_npy_path, joints_fname = render_motion( |
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joints, |
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feats.to('cpu').numpy(), method) |
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motion_uploaded.update({ |
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"feats": feats, |
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"joints": joints, |
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"motion_video": output_mp4_path, |
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"motion_video_fname": video_fname, |
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"motion_joints": output_npy_path, |
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"motion_joints_fname": joints_fname, |
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"motion_lengths": motion_lengths, |
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"motion_token": motion_token, |
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"motion_token_string": motion_token_string, |
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"motion_token_length": motion_token_length, |
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}) |
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return motion_uploaded |
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def add_text(history, text, motion_uploaded, data_stored, method): |
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data_stored = data_stored + [{'user_input': text}] |
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text = f"""<h3>{text}</h3>""" |
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history = history + [(text, None)] |
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if 'file' in motion_uploaded.keys(): |
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motion_uploaded = load_motion(motion_uploaded, method) |
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output_mp4_path = motion_uploaded['motion_video'] |
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video_fname = motion_uploaded['motion_video_fname'] |
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output_npy_path = motion_uploaded['motion_joints'] |
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joints_fname = motion_uploaded['motion_joints_fname'] |
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history = history + [(Video_Components.format( |
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video_path=output_mp4_path, |
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video_fname=video_fname, |
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motion_path=output_npy_path, |
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motion_fname=joints_fname), None)] |
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return history, gr.update(value="", |
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interactive=False), motion_uploaded, data_stored |
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def add_audio(history, audio_path, data_stored, language='en'): |
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audio, sampling_rate = librosa.load(audio_path, sr=16000) |
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input_features = audio_processor( |
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audio, sampling_rate, return_tensors="pt" |
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).input_features |
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input_features = torch.Tensor(input_features).to(device) |
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if language == 'English': |
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forced_decoder_ids = forced_decoder_ids_en |
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else: |
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forced_decoder_ids = forced_decoder_ids_zh |
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predicted_ids = audio_model.generate(input_features, |
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forced_decoder_ids=forced_decoder_ids) |
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text_input = audio_processor.batch_decode(predicted_ids, |
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skip_special_tokens=True) |
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text_input = str(text_input).strip('[]"') |
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data_stored = data_stored + [{'user_input': text_input}] |
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gr.update(value=data_stored, interactive=False) |
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history = history + [(text_input, None)] |
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return history, data_stored |
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def add_file(history, file, txt, motion_uploaded): |
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motion_uploaded['file'] = file.name |
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txt = txt.replace(" <Motion_Placeholder>", "") + " <Motion_Placeholder>" |
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return history, gr.update(value=txt, interactive=True), motion_uploaded |
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def bot(history, motion_uploaded, data_stored, method): |
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motion_length, motion_token_string = motion_uploaded[ |
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"motion_lengths"], motion_uploaded["motion_token_string"] |
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input = data_stored[-1]['user_input'] |
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prompt = model.lm.placeholder_fulfill(input, motion_length, |
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motion_token_string, "") |
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data_stored[-1]['model_input'] = prompt |
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batch = { |
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"length": [motion_length], |
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"text": [prompt], |
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} |
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outputs = model(batch, task="t2m") |
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out_feats = outputs["feats"][0] |
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out_lengths = outputs["length"][0] |
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out_joints = outputs["joints"][:out_lengths].detach().cpu().numpy() |
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out_texts = outputs["texts"][0] |
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output_mp4_path, video_fname, output_npy_path, joints_fname = render_motion( |
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out_joints, |
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out_feats.to('cpu').numpy(), method) |
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|
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motion_uploaded = { |
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"feats": None, |
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"joints": None, |
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"motion_video": None, |
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"motion_lengths": 0, |
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"motion_token": None, |
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"motion_token_string": '', |
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"motion_token_length": 0, |
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} |
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data_stored[-1]['model_output'] = { |
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"feats": out_feats, |
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"joints": out_joints, |
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"length": out_lengths, |
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"texts": out_texts, |
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"motion_video": output_mp4_path, |
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"motion_video_fname": video_fname, |
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"motion_joints": output_npy_path, |
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"motion_joints_fname": joints_fname, |
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} |
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if '<Motion_Placeholder>' == out_texts: |
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response = [ |
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Video_Components.format(video_path=output_mp4_path, |
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video_fname=video_fname, |
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motion_path=output_npy_path, |
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motion_fname=joints_fname) |
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] |
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elif '<Motion_Placeholder>' in out_texts: |
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response = [ |
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Text_Components.format( |
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msg=out_texts.split("<Motion_Placeholder>")[0]), |
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Video_Components.format(video_path=output_mp4_path, |
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video_fname=video_fname, |
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motion_path=output_npy_path, |
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motion_fname=joints_fname), |
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Text_Components.format( |
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msg=out_texts.split("<Motion_Placeholder>")[1]), |
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] |
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else: |
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response = f"""<h3>{out_texts}</h3>""" |
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history[-1][1] = "" |
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for character in response: |
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history[-1][1] += character |
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time.sleep(0.02) |
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yield history, motion_uploaded, data_stored |
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def bot_example(history, responses): |
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history = history + responses |
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return history |
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with open("assets/css/custom.css", "r", encoding="utf-8") as f: |
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customCSS = f.read() |
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|
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with gr.Blocks(css=customCSS) as demo: |
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chat_instruct = gr.State([ |
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(None, |
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"π Hi, I'm MotionGPT! I can generate realistic human motion from text, or generate text from motion." |
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), |
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(None, |
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"π‘ You can chat with me in pure text like generating human motion following your descriptions." |
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), |
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(None, |
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"π‘ After generation, you can click the button in the top right of generation human motion result to download the human motion video or feature stored in .npy format." |
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), |
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(None, |
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"π‘ With the human motion feature file downloaded or got from dataset, you are able to ask me to translate it!" |
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), |
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(None, |
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"π‘ Of courser, you can also purely chat with me and let me give you human motion in text, here are some examples!" |
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), |
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(None, |
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"π‘ We provide two motion visulization methods. The default fast method is skeleton line ploting which is like the examples below:" |
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), |
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(None, |
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Video_Components_example.format( |
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video_path="assets/videos/example0_fast.mp4", |
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video_fname="example0_fast.mp4")), |
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(None, |
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"π‘ And the slow method is SMPL model rendering which is more realistic but slower." |
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), |
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(None, |
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Video_Components_example.format( |
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video_path="assets/videos/example0.mp4", |
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video_fname="example0.mp4")), |
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(None, |
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"π‘ If you want to get the video in our paper and website like below, you can refer to the scirpt in our [github repo](https://github.com/OpenMotionLab/MotionGPT#-visualization)." |
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), |
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(None, |
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Video_Components_example.format( |
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video_path="assets/videos/example0_blender.mp4", |
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video_fname="example0_blender.mp4")), |
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(None, "π Follow the examples and try yourself!"), |
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]) |
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|
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t2m_examples = gr.State([ |
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(None, |
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"π‘ You can chat with me in pure text, following are some examples of text-to-motion generation!" |
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), |
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("A person is walking forwards, but stumbles and steps back, then carries on forward.", |
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Video_Components_example.format( |
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video_path="assets/videos/example0.mp4", |
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video_fname="example0.mp4")), |
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("Generate a man aggressively kicks an object to the left using his right foot.", |
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Video_Components_example.format( |
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video_path="assets/videos/example1.mp4", |
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video_fname="example1.mp4")), |
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("Generate a person lowers their arms, gets onto all fours, and crawls.", |
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Video_Components_example.format( |
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video_path="assets/videos/example2.mp4", |
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video_fname="example2.mp4")), |
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("Show me the video of a person bends over and picks things up with both hands individually, then walks forward.", |
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Video_Components_example.format( |
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video_path="assets/videos/example3.mp4", |
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video_fname="example3.mp4")), |
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("Imagine a person is practing balancing on one leg.", |
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Video_Components_example.format( |
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video_path="assets/videos/example5.mp4", |
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video_fname="example5.mp4")), |
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("Show me a person walks forward, stops, turns directly to their right, then walks forward again.", |
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Video_Components_example.format( |
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video_path="assets/videos/example6.mp4", |
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video_fname="example6.mp4")), |
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("I saw a person sits on the ledge of something then gets off and walks away.", |
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Video_Components_example.format( |
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video_path="assets/videos/example7.mp4", |
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video_fname="example7.mp4")), |
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("Show me a person is crouched down and walking around sneakily.", |
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Video_Components_example.format( |
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video_path="assets/videos/example8.mp4", |
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video_fname="example8.mp4")), |
|
]) |
|
|
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m2t_examples = gr.State([ |
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(None, |
|
"π‘ With the human motion feature file downloaded or got from dataset, you are able to ask me to translate it, here are some examples!" |
|
), |
|
("Please explain the movement shown in <Motion_Placeholder> using natural language.", |
|
None), |
|
(Video_Components_example.format( |
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video_path="assets/videos/example0.mp4", |
|
video_fname="example0.mp4"), |
|
"The person was pushed but didn't fall down"), |
|
("What kind of action is being represented in <Motion_Placeholder>? Explain it in text.", |
|
None), |
|
(Video_Components_example.format( |
|
video_path="assets/videos/example4.mp4", |
|
video_fname="example4.mp4"), |
|
"The figure has its hands curled at jaw level, steps onto its left foot and raises right leg with bent knee to kick forward and return to starting stance." |
|
), |
|
("Provide a summary of the motion demonstrated in <Motion_Placeholder> using words.", |
|
None), |
|
(Video_Components_example.format( |
|
video_path="assets/videos/example2.mp4", |
|
video_fname="example2.mp4"), |
|
"A person who is standing with his arms up and away from his sides bends over, gets down on his hands and then his knees and crawls forward." |
|
), |
|
("Generate text for <Motion_Placeholder>:", None), |
|
(Video_Components_example.format( |
|
video_path="assets/videos/example5.mp4", |
|
video_fname="example5.mp4"), |
|
"The man tries to stand in a yoga tree pose and looses his balance."), |
|
("Provide a summary of the motion depicted in <Motion_Placeholder> using language.", |
|
None), |
|
(Video_Components_example.format( |
|
video_path="assets/videos/example6.mp4", |
|
video_fname="example6.mp4"), |
|
"Person walks up some steps then leeps to the other side and goes up a few more steps and jumps dow" |
|
), |
|
("Describe the motion represented by <Motion_Placeholder> in plain English.", |
|
None), |
|
(Video_Components_example.format( |
|
video_path="assets/videos/example7.mp4", |
|
video_fname="example7.mp4"), |
|
"Person sits down, then stands up and walks forward. then the turns around 180 degrees and walks the opposite direction" |
|
), |
|
("Provide a description of the action in <Motion_Placeholder> using words.", |
|
None), |
|
(Video_Components_example.format( |
|
video_path="assets/videos/example8.mp4", |
|
video_fname="example8.mp4"), |
|
"This man is bent forward and walks slowly around."), |
|
]) |
|
|
|
t2t_examples = gr.State([ |
|
(None, |
|
"π‘ Of course, you can also purely chat with me and let me give you human motion in text, here are some examples!" |
|
), |
|
('Depict a motion as like you have seen it.', |
|
"A person slowly walked forward in rigth direction while making the circle" |
|
), |
|
('Random say something about describing a human motion.', |
|
"A man throws punches using his right hand."), |
|
('Describe the motion of someone as you will.', |
|
"Person is moving left to right in a dancing stance swaying hips, moving feet left to right with arms held out" |
|
), |
|
('Come up with a human motion caption.', |
|
"A person is walking in a counter counterclockwise motion."), |
|
('Write a sentence about how someone might dance.', |
|
"A person with his hands down by his sides reaches down for something with his right hand, uses the object to make a stirring motion, then places the item back down." |
|
), |
|
('Depict a motion as like you have seen it.', |
|
"A person is walking forward a few feet, then turns around, walks back, and continues walking." |
|
) |
|
]) |
|
|
|
Init_chatbot = chat_instruct.value[: |
|
1] + t2m_examples.value[: |
|
3] + m2t_examples.value[:3] + t2t_examples.value[:2] + chat_instruct.value[ |
|
-7:] |
|
|
|
|
|
motion_uploaded = gr.State({ |
|
"feats": None, |
|
"joints": None, |
|
"motion_video": None, |
|
"motion_lengths": 0, |
|
"motion_token": None, |
|
"motion_token_string": '', |
|
"motion_token_length": 0, |
|
}) |
|
data_stored = gr.State([]) |
|
|
|
gr.Markdown("# MotionGPT") |
|
|
|
chatbot = gr.Chatbot(Init_chatbot, |
|
elem_id="mGPT", |
|
height=600, |
|
label="MotionGPT", |
|
avatar_images=(("assets/images/avatar_user.png"), |
|
("assets/images/avatar_bot.jpg")), |
|
bubble_full_width=False) |
|
|
|
with gr.Row(): |
|
with gr.Column(scale=0.85): |
|
with gr.Row(): |
|
txt = gr.Textbox( |
|
label="Text", |
|
show_label=False, |
|
elem_id="textbox", |
|
placeholder= |
|
"Enter text and press ENTER or speak to input. You can also upload motion.", |
|
container=False) |
|
|
|
with gr.Row(): |
|
aud = gr.Audio(source="microphone", |
|
label="Speak input", |
|
type='filepath') |
|
btn = gr.UploadButton("π Upload motion", |
|
elem_id="upload", |
|
file_types=["file"]) |
|
regen = gr.Button("π Regenerate", elem_id="regen") |
|
clear = gr.ClearButton([txt, chatbot, aud], value='ποΈ Clear') |
|
|
|
with gr.Row(): |
|
gr.Markdown(''' |
|
### You can get more examples (pre-generated for faster response) by clicking the buttons below: |
|
''') |
|
|
|
with gr.Row(): |
|
instruct_eg = gr.Button("Instructions", elem_id="instruct") |
|
t2m_eg = gr.Button("Text-to-Motion", elem_id="t2m") |
|
m2t_eg = gr.Button("Motion-to-Text", elem_id="m2t") |
|
t2t_eg = gr.Button("Random description", elem_id="t2t") |
|
|
|
with gr.Column(scale=0.15, min_width=150): |
|
method = gr.Dropdown(["slow", "fast"], |
|
label="Visulization method", |
|
interactive=True, |
|
elem_id="method", |
|
value="slow") |
|
|
|
language = gr.Dropdown(["English", "δΈζ"], |
|
label="Speech language", |
|
interactive=True, |
|
elem_id="language", |
|
value="English") |
|
|
|
txt_msg = txt.submit( |
|
add_text, [chatbot, txt, motion_uploaded, data_stored, method], |
|
[chatbot, txt, motion_uploaded, data_stored], |
|
queue=False).then(bot, [chatbot, motion_uploaded, data_stored, method], |
|
[chatbot, motion_uploaded, data_stored]) |
|
|
|
txt_msg.then(lambda: gr.update(interactive=True), None, [txt], queue=False) |
|
|
|
file_msg = btn.upload(add_file, [chatbot, btn, txt, motion_uploaded], |
|
[chatbot, txt, motion_uploaded], |
|
queue=False) |
|
aud_msg = aud.stop_recording( |
|
add_audio, [chatbot, aud, data_stored, language], |
|
[chatbot, data_stored], |
|
queue=False).then(bot, [chatbot, motion_uploaded, data_stored, method], |
|
[chatbot, motion_uploaded, data_stored]) |
|
regen_msg = regen.click(bot, |
|
[chatbot, motion_uploaded, data_stored, method], |
|
[chatbot, motion_uploaded, data_stored], |
|
queue=False) |
|
|
|
instruct_msg = instruct_eg.click(bot_example, [chatbot, chat_instruct], |
|
[chatbot], |
|
queue=False) |
|
t2m_eg_msg = t2m_eg.click(bot_example, [chatbot, t2m_examples], [chatbot], |
|
queue=False) |
|
m2t_eg_msg = m2t_eg.click(bot_example, [chatbot, m2t_examples], [chatbot], |
|
queue=False) |
|
t2t_eg_msg = t2t_eg.click(bot_example, [chatbot, t2t_examples], [chatbot], |
|
queue=False) |
|
|
|
chatbot.change(scroll_to_output=True) |
|
|
|
if __name__ == "__main__": |
|
demo.launch(debug=True) |
|
|
