ADAM-Net/index.html at gh-pages · MPS-Net/ADAM-Net · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
<!DOCTYPE html><!-- Last Published: Fri Mar 27 2020 21:28:31 GMT+0000 (Coordinated Universal Time) -->
<html data-wf-domain="www.matthewtancik.com" data-wf-page="5e6fb768456f961381500a5f"
  data-wf-site="51e0d73d83d06baa7a00000f">

<head>
  <meta charset="utf-8" />
  <title>PhotosInBetweening</title>
  <meta content="PhotosInBetweening." name="description" />
  <meta content="PhotosInBetweening ADAM-Net" property="og:title" />
  <meta content="PhotosInBetweening" property="og:description" />
  <meta content="summary" name="twitter:card" />
  <meta content="width=device-width, initial-scale=1" name="viewport" />
  <link href="./choreomaster.css" rel="stylesheet" type="text/css" />
  <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css"
    integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous">
  <!-- <script src="https://ajax.googleapis.com/ajax/libs/webfont/1.6.26/webfont.js" type="text/javascript"></script> -->
  <script src="./google.js" type="text/javascript"></script>
  <script
    type="text/javascript">WebFont.load({ google: { families: ["Lato:100,100italic,300,300italic,400,400italic,700,700italic,900,900italic", "Montserrat:100,100italic,200,200italic,300,300italic,400,400italic,500,500italic,600,600italic,700,700italic,800,800italic,900,900italic", "Ubuntu:300,300italic,400,400italic,500,500italic,700,700italic", "Changa One:400,400italic", "Open Sans:300,300italic,400,400italic,600,600italic,700,700italic,800,800italic", "Varela Round:400", "Bungee Shade:regular", "Roboto:300,regular,500"] } });</script>
  <script
    type="text/javascript">!function (o, c) { var n = c.documentElement, t = " w-mod-"; n.className += t + "js", ("ontouchstart" in o || o.DocumentTouch && c instanceof DocumentTouch) && (n.className += t + "touch") }(window, document);</script>

  <style>
    body {
      font-family: 'Microsoft Yahei' !important;
    }

    .all-wrap {
      margin: 0 auto;
      width: 1000px;
    }

    .wf-loading * {
      opacity: 0;
    }

    .w-container {
      max-width: 1000px;
    }

    .nerf_network {
      width: 100%;
    }

    .nerf_affiliation_v2,
    .nerf_affiliation_v3 {
      font-size: 20px;
      line-height: 24px;
    }

    .nerf_title_v2,
    .white_section_nerf {
      font-family: fangsong, 'Microsoft Yahei';
      color: #3c3c3c;
      text-align: center;
    }

    .long-text {
      font-family: monospace !important;
      font-size: 20px !important;
    }

    .bibtex {
      font-size: 16px;
    }

    .bold-text-nerf_v2 {
      font-size: 16px;
    }

    .row-name {
      text-align: center;
    }

    .name-wrap {
      display: none;
    }

    @media screen and (max-width: 479px) {
      .row-name {
        display: none;
      }

      .name-wrap {
        display: block;
      }

      .name-unit {
        display: block;
        text-align: center;
        line-height: 40px;
      }

      .nerf_title_v2 {
        font-size: 45px;
        width: 850px;
        margin-bottom: 10px;
      }

      .nerf_subheader_v2 {
        font-size: 6vw;
        margin-bottom: 20px;
      }

      .long-text {
        font-size: 28px !important;
        line-height: 28px !important;
      }
    }
  </style>
</head>

<body>
  <div class="all-wrap">
    <div class="section hero nerf-_v2">
      <div class="container-2 nerf_header_v2 w-container">
        <h1 class="nerf_title_v2">Bridging Actions: Generate 3D Poses and Shapes In-Between Photos</h1>
        <h1 class="nerf_subheader_v2">accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)</h1>
        <h1 class="nerf_affiliation_v2" style="margin-right: 20px;">Wen-Li Wei and Jen-Chun Lin</h1>
	<br>
        <h1 class="nerf_affiliation_v3" style="margin-right: 40px;font-size: 18px">Institute of Information Science, Academia Sinica, Taiwan

        <!--<div class="nerf_authors_list_single nerf_authors_affiliation w-row"
          style="margin-top: 15px;text-align: center;">
          <h1 class="nerf_affiliation_v3" style="margin-right: 40px;font-size: 18px"><sup>*</sup>Equal Contribution
          </h1>
        </div>-->
 <br>
        <div>
          <span class="center"><img src = "./Fig1.gif" width="1164px"></img></span>
          <h1 class="nerf_affiliation_v3" style="margin-right: 40px;font-size: 18px">The pose and shape transitions in-between photos are automatically generated by our system. It allows for generating transitions of variable lengths defined by the user and for arbitrary editing (replacement/insertion/deletion) of the input photos. Bright green represents the generated 3D human pose and shape of given photos, while dark green represents the generated transitions. (Corresponding to Fig. 1 in our paper)
          </h1>
        </div>

        <div class="w-container w-row">
          <div class="w-col w-col-4 w-col-small-4 w-col-tiny-4">
            <a href="https://ieeexplore.ieee.org/document/10497888" target="_blank" class="link-block w-inline-block">
              <img src="./pngtree-paper-icon-png-image_6294297.png" alt="paper" class="paper_img image-8 github_icon_nerf_v2" /></a>
          </div>
          <div class="w-col w-col-4 w-col-small-4 w-col-tiny-4">
            <a href="TPAMI__AQ___fixWarning___18pages___supplementary_material_3.pdf" target="_blank" class="link-block w-inline-block">
              <img src="./supporting-material-icon-3-removebg-preview.png" alt="supplementary material" class="paper_img image-8 github_icon_nerf_v2" /></a>
          </div>
          <div class="w-col w-col-4 w-col-small-4 w-col-tiny-4">
            <a href=" " target="_blank" class="link-block w-inline-block">
              <img src="./6455678.png" alt="Code (Coming soon...)"
                class="paper_img image-8 github_icon_nerf_v2" /></a>
          </div>
	</div>

        <div class="w-container w-row">
          <div class="w-col w-col-4 w-col-small-4 w-col-tiny-4">
            <div class="text-block-2"><strong class="bold-text-nerf_v2">Paper</strong></div>
          </div>
          <div class="w-col w-col-4 w-col-small-4 w-col-tiny-4">
            <div class="text-block-2"><strong class="bold-text-nerf_v2">Supplementary Material</strong></div>
          </div>
          <div class="w-col w-col-4 w-col-small-4 w-col-tiny-4">
            <div class="text-block-2"><strong class="bold-text-nerf_v2">Code (Coming soon...)</strong></div>
          </div>
        </div>

        <div class="w-container w-row">
          <div class="w-col w-col-4 w-col-small-4 w-col-tiny-4">
            <div class="text-block-2"><strong class="bold-text-nerf_v2"></strong></div>
          </div>
          <div class="w-col w-col-4 w-col-small-4 w-col-tiny-4">
            <div class="text-block-2"><strong class="bold-text-nerf_v2">(Visualization of Attention)</strong></div>
          </div>
          <div class="w-col w-col-4 w-col-small-4 w-col-tiny-4">
            <div class="text-block-2"><strong class="bold-text-nerf_v2"></strong></div>
          </div>
        </div>
	<!--
          <div class="w-col w-col-3 w-col-small-3 w-col-tiny-3">
            <div class="text-block-2">
              <strong class="bold-text-nerf_v2">User Study Video</strong>
            </div>
          </div>
          <div class="w-col w-col-3 w-col-small-3 w-col-tiny-3">
            <div class="text-block-2"><strong class="bold-text-nerf_v2">Data</strong></div>
          </div>
        </div>

        <div class="nerf_slide_nav w-slider-nav w-slider-nav-invert w-round"></div>
        -->
      </div>
    </div>


    <!--<div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <div class="w-row">
          <div class="w-col">
            <video class="results" loop="" playsinline="" controls="" width="100%" poster="./Demo Video-small.png">
              <source src="./Demo Video-small.mp4" type="video/mp4">
            </video>
          </div>
        </div>
      </div>
    </div>-->
    <hr />
    <div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <h2 class="white_section_nerf">Abstract</h2>
        <p class="paragraph-3 nerf_text long-text">Generating realistic 3D human motion has been a fundamental goal of the
          game/animation industry. This work presents a novel transition generation technique that can bridge the actions
          of people in the foreground by generating 3D poses and shapes in between photos, allowing 3D animators/novice users
          to easily create/edit 3D motions. To achieve this, we propose an adaptive motion network (ADAM-Net) that effectively
          learns human motion from masked action sequences to generate kinematically compliant 3D poses and shapes in-between
          given temporally-sparse photos. Three core learning designs underpin ADAM-Net. First, we introduce a random masking
          process that randomly masks images from an action sequence and fills masked regions in latent space by interpolation
          of unmasked images to simulate various transitions under given temporally-sparse photos. Second, we propose a
          long-range adaptive motion (L-ADAM) attention module that leverages visual cues observed from human motion to
          adaptively recalibrate the range that needs attention in a sequence, along with a multi-head cross-attention.
          Third, we develop a short-range adaptive motion (S-ADAM) attention module that weightedly selects and integrates
          adjacent feature representations at different levels to strengthen temporal correlation. By coupling these designs,
          the results demonstrate that ADAM-Net excels not only in generating 3D poses and shapes in-between photos, but also
          in classic 3D human pose and shape estimation. </p>
      </div>
    </div>
    <hr />
    <div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <h2 class="white_section_nerf">Demo</h2>
        <video class="results" playsinline="" controls="" width="100%" poster="./Demo Video-small.png">
              <source src="./Demo Video-small.mp4" type="video/mp4">
            </video>
      </div>
    </div>

    <div class="w-container">
      <span class="center"><img src="./images/choreo_overview.PNG" alt="" class="nerf_network" /></span>
    </div>
    <hr />
    <div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <h2 class="white_section_nerf">Qualitative Evaluation</h2>
        <h1 class="nerf_affiliation_v2" style="font-size: 24px;text-align: left;">- Visual comparisons with the baseline method, TCMR, and MPS-Net.</h1>
      </div>
    </div>

    <div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <div class="w-row">
          <div class="w-col">
            <video class="results" loop="" playsinline="" controls="" width="100%" poster="./Fig6.png">
              <source src="./Fig6.mp4" type="video/mp4">
            </video>
            <h1 class="nerf_affiliation_v3" style="margin-right: 40px;font-size: 18px">Qualitative comparison of baseline method (a), TCMR (b), and our ADAM-Net (c) on the challenging in-the-wild 3DPW dataset. The first and last photos in the sequence are given keyframes, which correspond to photos with bright backgrounds and red windows. The photos in-between keyframes are the original image references (i.e., dark background photos). The transition length is set to 5 in this experiment. Bright colors represent the generated 3D human pose and shape of given photos, while dark colors represent the generated transitions. (Corresponding to Fig. 6 in our paper)
          </h1>
          </div>
        </div>
      </div>
    </div>


    <div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <div class="w-row">
          <div class="w-col">
            <video class="results" loop="" playsinline="" controls="" width="100%" poster="./Fig7.png">
              <source src="./Fig7.mp4" type="video/mp4">
            </video>
            <h1 class="nerf_affiliation_v3" style="margin-right: 40px;font-size: 18px">Qualitative comparison of TCMR (a) and our ADAM-Net (b) on the challenging in-the-wild MPI-INF-3DHP dataset, where the transition length is set to 20. Bright colors represent the generated 3D human pose and shape of given photos, while dark colors represent the generated transitions preserved after sampling. (Corresponding to Fig. 7 in our paper)
            </h1>
          </div>
        </div>
      </div>
    </div>

    <br>
    <br>
    <div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <div class="w-row">
          <div class="w-col">
            <video class="results" loop="" playsinline="" controls="" width="100%" poster="./Fig8.png">
              <source src="./Fig8.mp4" type="video/mp4">
            </video>
            <h1 class="nerf_affiliation_v3" style="margin-right: 40px;font-size: 18px">Qualitative comparison of baseline method (a), TCMR (b), MPS-Net (c), and our ADAM-Net (d) on photos downloaded from the Internet, where the transition length is set to 31. Bright colors represent the generated 3D human pose and shape of given photos, while dark colors represent the generated transitions preserved after sampling. (Corresponding to Fig. 8 in our paper)
            </h1>
          </div>
        </div>
      </div>
    </div>

    <br>
    <br>
    <div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <div class="w-row">
          <div class="w-col">
            <video class="results" loop="" playsinline="" controls="" width="100%" poster="./Fig9.png">
              <source src="./Fig9.mp4" type="video/mp4">
            </video>
            <h1 class="nerf_affiliation_v3" style="margin-right: 40px;font-size: 18px">Qualitative comparison among our ADAM-Net, ADAM-Net-only MHA [21], and a linear interpolation method where the 3D human meshes for the start and end keyframes of the linear interpolation method are taken from ADAM-Net. Bright colors represent the generated 3D human pose and shape of given photos, while dark colors represent the generated transitions preserved after sampling. (Corresponding to Fig. 9 in our paper)
            </h1>
          </div>
        </div>
      </div>
    </div>
    <br>
    <br>

    <hr />
    <div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <h2 class="white_section_nerf">Qualitative Evaluation</h2>
        <h1 class="nerf_affiliation_v2" style="font-size: 24px;text-align: left;">- Visual effects of our <font color="red">ADAM-Net</font> under different transition lengths, inserting/replacing different photos.</h1>
      </div>
    </div>

    <div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <div class="w-row">
          <div class="w-col">
            <video class="results" loop="" playsinline="" controls="" width="100%" poster="./Fig11.png">
              <source src="./Fig11.mp4" type="video/mp4">
            </video>
            <h1 class="nerf_affiliation_v3" style="margin-right: 40px;font-size: 18px">Qualitative results of 3D pose and shape transitions generated by ADAM-Net under arbitrarily edited input photos and transition lengths. As compared to (a), (b) inserts one more photo (keyframe), as compared to (b), (c) replaces one of the photos in (b), and as compared to (c), (d) changes the photo positions in (c), which also has a different transition length. Bright colors represent the generated 3D human pose and shape of given photos, while dark colors represent the generated transitions preserved after sampling. (Corresponding to Fig. 11 in our paper)
            </h1>
          </div>
        </div>
      </div>
    </div>


    <div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <div class="w-row">
          <div class="w-col">
            <video class="results" loop="" playsinline="" controls="" width="100%" poster="./Fig13.png">
              <source src="./Fig13.mp4" type="video/mp4">
            </video>
            <h1 class="nerf_affiliation_v3" style="margin-right: 40px;font-size: 18px">Qualitative results of animating 3D pose transitions (generated by our ADAM-Net) into avatars. Photos (keyframes) are downloaded from the Internet and edited arbitrarily by the author. The transition length in-between keyframes is set to 10. From top to bottom, each example displays the given keyframes, the unfolded generated 3D pose and shape transitions, and the animated character. (Corresponding to Fig. 13 in our paper)
            </h1>
          </div>
        </div>
      </div>
    </div>
    <br>
    <br>

    <hr />
    <div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <h2 class="white_section_nerf">EXPERIMENTS</h2>
        <h1 class="nerf_affiliation_v2" style="font-size: 24px;text-align: left;">- 3D Joint Transition Generation In-Betweening</h1>
      </div>
    </div>

    <div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <div class="w-row">
          <div class="w-col">
            <video class="results" loop="" playsinline="" controls="" width="100%" poster="./Fig15.png">
              <source src="./Fig15.mp4" type="video/mp4">
            </video>
            <h1 class="nerf_affiliation_v3" style="margin-right: 40px;font-size: 18px">Qualitative comparison of RMIB [7] with pose estimator [2], RMIB [7] with pose estimator ADAM-Net, and our ADAM-Net on the challenging in-the-wild 3DPW dataset. The red and blue skeletons respectively represent the starting and ending keyframes, while the pink signifies the generated transition of the 3D joints. (Corresponding to Fig. 15 in our paper)
            </h1>
          </div>
        </div>
      </div>
    </div>
    <br>
    <br>

    <div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <div class="w-row">
          <div class="w-col">
            <video class="results" loop="" playsinline="" controls="" width="100%" poster="./Fig16.png">
              <source src="./Fig16.mp4" type="video/mp4">
            </video>
            <h1 class="nerf_affiliation_v3" style="margin-right: 40px;font-size: 18px">Qualitative comparison of RMIB [7] with pose estimator [56], and our ADAM-Net on the challenging EMDB dataset. The red and blue skeletons respectively represent the starting and ending keyframes, while the pink signifies the generated transition of the 3D joints. (Corresponding to Fig. 16 in our paper)
            </h1>
          </div>
        </div>
      </div>
    </div>
    <br>
    <br>

    <hr />
    <div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <h2 class="white_section_nerf">EXPERIMENTS</h2>
        <h1 class="nerf_affiliation_v2" style="font-size: 24px;text-align: left;">- 3D Human Pose and Shape Estimation from Monocular Video</h1>
      </div>
    </div>

    <div data-anchor="slide1" class="section nerf_section">
      <div class="w-container">
        <div class="w-row">
          <div class="w-col">
            <video class="results" loop="" playsinline="" controls="" width="100%" poster="./Fig17.png">
              <source src="./Fig17.mp4" type="video/mp4">
            </video>
            <h1 class="nerf_affiliation_v3" style="margin-right: 40px;font-size: 18px">Qualitative comparison of TCMR (left), MPS-Net (middle), and our ADAM-Net (right) for pose and shape estimation on the challenging in-the-wild 3DPW dataset and video downloaded from the Internet. (Corresponding to Fig. 17 in our paper)
            </h1>
          </div>
        </div>
      </div>
    </div>

    <hr>
 	  <a name="related_work"></a>
   	<table align=center width=1100px>
 	      <tr>
  	      	<td width=400px>
 		           	<left>
  	   		    	<center><h1>References</h1></center>
 		 	   	      <br> [2] N. Kolotouros, G. Pavlakos, M. J. Black, and K. Daniilidis, “Learning to reconstruct 3D human pose and shape via model-fitting in the loop,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019.</br>
			   	    	<br> [3] H. Choi, G. Moon, J. Y. Chang, and K. M. Lee, “Beyond static features for temporally consistent 3D human pose and shape from a video,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.</br>
		  	   	  	<br> [7] F. G. Harvey, M. Yurick, D. Nowrouzezahrai, and C. Pal, “Robust motion in-betweening,” ACM Transactions on Graphics (TOG), vol. 39, no. 4, 2020.</br>
		  	     		<br> [10] W.-L. Wei, J.-C. Lin, T.-L. Liu, and H.-Y. M. Liao, “Capturing humans in motion: Temporal-attentive 3D human pose and shape estimation from monocular video,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.</br>
	  		     		<br> [21] A. Vaswani, N. M. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems (NeurIPS), 2017.</br>
	  		     		<br> [56] Z. Li, J. Liu, Z. Zhang, S. Xu, and Y. Yan, “CLIFF: Carrying location information in full frames into human pose and shape estimation,”in Proceedings of the European Conference on Computer Vision (ECCV), 2022.</br>
           </td>
	      </tr>
  	</table>

  <hr>
  <table align=center width=1100px>
      <tr>
	  <td width=400px>
 	      <!--<left>-->
  	      <center>
		  <h1>Acknowledgements</h1>
		  This webpage template was refered from https://netease-gameai.github.io/ChoreoMaster/. Thanks a lot.
		<br>
		  All images and videos on the website are for research purposes only.
	      </center>
	  </td>
      </tr>
  </table>

    <hr />
    <!--<div class="white_section_nerf">-->
      <div class="w-container">
        <h2 class="grey-heading_nerf">BibTeX</h2>
        <div class="grey_container w-container">
          <div class="bibtex">
            <pre><code>@article{In-BetweenPhotos2024,
    author = {Wen-Li Wei and Jen-Chun Lin},
    title = {Bridging Actions: Generate 3D Poses and Shapes In-Between Photos},
    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
    volume = {},
    number = {},
    year = {2024},
    publisher = {IEEE}
}</code></pre>
          </div>
        </div>
      </div>
    <!--</div>-->
  </div>


  <script src="https://d3e54v103j8qbb.cloudfront.net/js/jquery-3.4.1.min.220afd743d.js?site=51e0d73d83d06baa7a00000f"
    type="text/javascript" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo="
    crossorigin="anonymous"></script>
  <script src="https://uploads-ssl.webflow.com/51e0d73d83d06baa7a00000f/js/webflow.3057c11af.js"
    type="text/javascript"></script>
  <!--[if lte IE 9]><script src="//cdnjs.cloudflare.com/ajax/libs/placeholders/3.0.2/placeholders.min.js"></script><![endif]-->
</body>

</html>