CV：利用人工智能算法让古代皇帝画像以及古代四大美女画像动起来(模仿偶像胡歌剧中角色表情动作)
利用人工智能算法让古代四大美女画像动起来(模仿偶像胡歌剧中角色表情动作)

导读：本论文来自NeurIPS2019，该算法中主要采用一阶运动模型的思想，用一组自学习的关键点和局部仿射变换，建立了复杂运动模型。模型由运动估计模块和图像生成模块两个主要部分组成。首先进行关键点检测，然后根据关键点，进行运动估计，最后使用图像生成模块，生成最终效果。
额，哈哈，不好意思了，又拿我的偶像胡歌下手啦，视频截取来源偶像胡歌在《猎场》中的一角色。

利用人工智能算法让经典图片根据自定义动作嗨起来(将一张静态人像图片转为带表情动作视频)

输出结果

利用人工智能算法让古代皇帝画像动起来(模仿偶像胡歌《猎场》剧中角色表情动作)

利用人工智能算法让古代四大美女画像动起来

实现代码

更新中……

import imageio
import torch
from tqdm import tqdm
from animate import normalize_kp
from demo import load_checkpoints
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from skimage import img_as_ubyte
from skimage.transform import resize
import cv2
import os
import argparse

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input_image", required=True,help="Path to image to animate")
ap.add_argument("-c", "--checkpoint", required=True,help="Path to checkpoint")
ap.add_argument("-v","--input_video", required=False, help="Path to video input")

args = vars(ap.parse_args())

print("[INFO] loading source image and checkpoint...")
source_path = args['input_image']
checkpoint_path = args['checkpoint']
if args['input_video']:
    video_path = args['input_video']
else:
    video_path = None
source_image = imageio.imread(source_path)
source_image = resize(source_image,(256,256))[..., :3]

generator, kp_detector = load_checkpoints(config_path='config/vox-256.yaml', checkpoint_path=checkpoint_path)

if not os.path.exists('output'):
    os.mkdir('output')


relative=True
adapt_movement_scale=True
cpu=False

if video_path:
    cap = cv2.VideoCapture(video_path) 
    print("[INFO] Loading video from the given path")
else:
    cap = cv2.VideoCapture(0)
    print("[INFO] Initializing front camera...")

fourcc = cv2.VideoWriter_fourcc(*'MJPG')
out1 = cv2.VideoWriter('output/Animation_HuGe_02.avi', fourcc, 12, (256*3 , 256), True)

cv2_source = cv2.cvtColor(source_image.astype('float32'),cv2.COLOR_BGR2RGB)
with torch.no_grad() :
    predictions = []
    source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)
    if not cpu:
        source = source.cuda()
    kp_source = kp_detector(source)
    count = 0
    while(True):
        ret, frame = cap.read()
        frame = cv2.flip(frame,1)
        if ret == True:
            
            if not video_path:
                x = 143
                y = 87
                w = 322
                h = 322 
                frame = frame[y:y+h,x:x+w]
            frame1 = resize(frame,(256,256))[..., :3]
            
            if count == 0:
                source_image1 = frame1
                source1 = torch.tensor(source_image1[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)
                kp_driving_initial = kp_detector(source1)
            
            frame_test = torch.tensor(frame1[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)

            driving_frame = frame_test
            if not cpu:
                driving_frame = driving_frame.cuda()
            kp_driving = kp_detector(driving_frame)
            kp_norm = normalize_kp(kp_source=kp_source,
                                kp_driving=kp_driving,
                                kp_driving_initial=kp_driving_initial, 
                                use_relative_movement=relative,
                                use_relative_jacobian=relative, 
                                adapt_movement_scale=adapt_movement_scale)
            out = generator(source, kp_source=kp_source, kp_driving=kp_norm)
            predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])
            im = np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0]
            im = cv2.cvtColor(im,cv2.COLOR_RGB2BGR)
            joinedFrame = np.concatenate((cv2_source,im,frame1),axis=1)
            
            cv2.imshow('Test',joinedFrame)
            out1.write(img_as_ubyte(joinedFrame))
            count += 1
            if cv2.waitKey(20) & 0xFF == ord('q'):
                break
        else:
            break
        
    cap.release()
    out1.release()
    cv2.destroyAllWindows()

CV：利用人工智能算法让古代皇帝画像以及古代四大美女画像动起来(模仿偶像胡歌剧中角色表情动作)

利用人工智能算法让经典图片根据自定义动作嗨起来(将一张静态人像图片转为带表情动作视频)

相关论文

输出结果

利用人工智能算法让古代皇帝画像动起来(模仿偶像胡歌《猎场》剧中角色表情动作)

利用人工智能算法让古代四大美女画像动起来

实现代码

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