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。

目录

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

作品视频链接
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美女《西施、王昭君、貂蝉、杨玉环四大美女领衔》画像动起来

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

Paper：《First Order Motion Model for Image Animation》翻译与解读

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()