-
Notifications
You must be signed in to change notification settings - Fork 12
/
Copy pathindex.html
433 lines (400 loc) · 16.5 KB
/
index.html
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
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="MemFlow: Optical Flow Estimation and Prediction with Memory.">
<meta name="keywords" content="Optical Flow">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>MemFlow: Optical Flow Estimation and Prediction with Memory</title>
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=G-PYVRSFMDRL"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag() {
dataLayer.push(arguments);
}
gtag('js', new Date());
gtag('config', 'G-PYVRSFMDRL');
</script>
<link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
rel="stylesheet">
<link rel="stylesheet" href="./static/css/bulma.min.css">
<link rel="stylesheet" href="./static/css/bulma-carousel.min.css">
<link rel="stylesheet" href="./static/css/bulma-slider.min.css">
<link rel="stylesheet" href="./static/css/fontawesome.all.min.css">
<link rel="stylesheet"
href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css">
<link rel="stylesheet" href="./static/css/index.css">
<!-- <link rel="icon" href="./static/images/favicon.svg">-->
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<script defer src="./static/js/fontawesome.all.min.js"></script>
<script src="./static/js/bulma-carousel.min.js"></script>
<script src="./static/js/bulma-slider.min.js"></script>
<script src="./static/js/index.js"></script>
</head>
<body>
<nav class="navbar" role="navigation" aria-label="main navigation">
<div class="navbar-brand">
<a role="button" class="navbar-burger" aria-label="menu" aria-expanded="false">
<span aria-hidden="true"></span>
<span aria-hidden="true"></span>
<span aria-hidden="true"></span>
</a>
</div>
<div class="navbar-menu">
<div class="navbar-start" style="flex-grow: 1; justify-content: center;">
<a class="navbar-item" href="https://github.com/DQiaole">
<span class="icon">
<i class="fas fa-home"></i>
</span>
</a>
</div>
</div>
</nav>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">MemFlow: Optical Flow Estimation and Prediction with Memory</h1>
<h2><font color="gray" size="5">CVPR 2024</font></h2>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://dqiaole.github.io/">Qiaole Dong</a> and </span>
<span class="author-block">
<a href="http://yanweifu.github.io/">Yanwei Fu</a>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">Fudan University</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link. -->
<span class="link-block">
<a href="https://arxiv.org/abs/2404.04808"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<!-- Video Link. -->
<!-- <span class="link-block">-->
<!-- <a href="https://www.youtube.com/watch?v=MrKrnHhk8IA"-->
<!-- class="external-link button is-normal is-rounded is-dark">-->
<!-- <span class="icon">-->
<!-- <i class="fab fa-youtube"></i>-->
<!-- </span>-->
<!-- <span>Video</span>-->
<!-- </a>-->
<!-- </span>-->
<!-- Code Link. -->
<span class="link-block">
<a href="https://github.com/DQiaole/MemFlow"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<!-- Dataset Link. -->
<!-- <span class="link-block">-->
<!-- <a href="https://github.com/google/nerfies/releases/tag/0.1"-->
<!-- class="external-link button is-normal is-rounded is-dark">-->
<!-- <span class="icon">-->
<!-- <i class="far fa-images"></i>-->
<!-- </span>-->
<!-- <span>Data</span>-->
<!-- </a>-->
<!-- </span>-->
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<center>
<img src="./imgs/teaser.png" width="600px"/>
</center>
<!-- <video id="teaser" autoplay muted loop height="100%">-->
<!-- <source src="https://homes.cs.washington.edu/~kpar/nerfies/videos/teaser.mp4"-->
<!-- type="video/mp4">-->
<!-- </video>-->
<h2 class="subtitle has-text-centered">
EPE on Sintel (clean) vs. inference time (ms) and model size (M). MemFlow(-T) (x it) indicates running our
network with only x iterations of GRU.
</h2>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column is-full-width">
<center>
<h2 class="title is-3">Comparison with Previous SOTA</h2>
<h2 class="subtitle has-text-left">
Please zoom in for better visualization.
</h2>
<div class="columns is-vcentered interpolation-panel">
<img src="./imgs/fig4.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
<div class="columns is-vcentered interpolation-panel">
<img src="./imgs/fig5.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
<div class="columns is-vcentered interpolation-panel">
<img src="./imgs/fig6.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
</center>
</div>
</div>
</div>
</section>>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<h2 class="title is-3">Super Efficient/Effective though with Same Architecture</h2>
<h2 class="subtitle has-text-left">
Our method outperforms 15-iteration SKFlow’s performance, after using only 2 iterations.
</h2>
<center>
<img src="./imgs/fig3.png" width="500px"/>
</center>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column is-full-width">
<center>
<h2 class="title is-3">Beyond Optical Flow Estimation: Future Prediction</h2>
<h2 class="subtitle has-text-left">
Repurposing MemFlow for optical flow future prediction. Following videos show the qualitative results of
future prediction (one time step ahead). From left to right are: Predicted optical
flow into next frame superimposed on the video frame, Synthesized next video frame based on our predicted
flow, and Groundtruth next frame.
</h2>
<div class="columns is-vcentered interpolation-panel">
<video autoplay="autoplay" loop="loop" id="video0" muted="" controls>
<source src="./imgs/ofp_0.mp4" type="video/mp4">
</video>
</div>
<div class="columns is-vcentered interpolation-panel">
<video autoplay="autoplay" loop="loop" id="video1" muted="" controls>
<source src="./imgs/ofp_1.mp4" type="video/mp4">
</video>
</div>
<div class="columns is-vcentered interpolation-panel">
<video autoplay="autoplay" loop="loop" id="video2" muted="" controls>
<source src="./imgs/ofp_2.mp4" type="video/mp4">
</video>
</div>
<div class="columns is-vcentered interpolation-panel">
<video autoplay="autoplay" loop="loop" id="video3" muted="" controls>
<source src="./imgs/ofp_3.mp4" type="video/mp4">
</video>
</div>
</center>
</div>
</div>
</div>
</section>>
<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Optical flow is a classical task that is important to the vision community. Classical optical flow estimation uses two
frames as input, whilst some recent methods consider multiple frames to explicitly model long-range information.
The former ones limit their ability to fully leverage temporal coherence along the video sequence; and the latter
ones incur heavy computational overhead, typically not possible for real-time flow estimation. Some multi-frame-based
approaches even necessitate unseen future frames for current estimation, compromising real-time applicability in
safety-critical scenarios. To this end, we present MemFlow, a real-time method for optical flow estimation and prediction
with memory. Our method enables memory read-out and update modules for aggregating historical motion information in real-time.
Furthermore, we integrate resolution-adaptive re-scaling to accommodate diverse video resolutions. Besides, our approach
seamlessly extends to the future prediction of optical flow based on past observations. Leveraging effective historical
motion aggregation, our method outperforms VideoFlow with fewer parameters and faster inference speed on Sintel and
KITTI-15 datasets in terms of generalization performance. At the time of submission, MemFlow also leads in performance
on the 1080p Spring dataset.
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
<!-- Paper video. -->
<!-- <div class="columns is-centered has-text-centered">-->
<!-- <div class="column is-four-fifths">-->
<!-- <h2 class="title is-3">Video</h2>-->
<!-- <div class="publication-video">-->
<!-- <iframe src="https://www.youtube.com/embed/MrKrnHhk8IA?rel=0&showinfo=0"-->
<!-- frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>-->
<!-- </div>-->
<!-- </div>-->
<!-- </div>-->
<!--/ Paper video. -->
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<center>
<h2 class="title is-3">Superior Generalization Performance</h2>
<h2 class="subtitle has-text-left">
Generalization performance of optical flow estimation on Sintel and KITTI-15 after trained on FlyingChairs and FlyingThings3D.
</h2>
<img src="./imgs/table1.png" width="500px"/>
</center>
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<center>
<h2 class="title is-3">Comparable with SOTA on Standard Benchmark</h2>
<h2 class="subtitle has-text-left">
Optical flow finetuning evaluation on the public benchmark.
</h2>
<img src="./imgs/table2.png" width="500px"/>
</center>
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<center>
<h2 class="title is-3">Test on HD Video Benchmark (Spring)</h2>
<h2 class="subtitle has-text-left">
Optical flow generalization and finetuning results on 1080p Spring dataset.
</h2>
<img src="./imgs/table3.png" width="1200px"/>
</center>
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<center>
<h2 class="title is-3">Results of Future Prediction</h2>
<h2 class="subtitle has-text-left">
Left: EPE of flow prediction on FlyingThings3D
(Final), Sintel (Final), and KITTI-15. Right: Comparison
of next frame prediction on KITTI test set (256x832). Note that
our method is not trained for video prediction specifically.
</h2>
<img src="./imgs/table4.png" width="500px"/>
</center>
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<center>
<h2 class="title is-3">Framework</h2>
<img src="./imgs/overview.png" width="800px"/>
</center>
<!-- <video id="teaser" autoplay muted loop height="100%">-->
<!-- <source src="https://homes.cs.washington.edu/~kpar/nerfies/videos/teaser.mp4"-->
<!-- type="video/mp4">-->
<!-- </video>-->
<h2 class="subtitle has-text-left">
MemFlow maintains a memory buffer to store historical motion states of video, together with an efficient
update and read-out process that retrieves useful motion information for the current frame’s optical flow estimation.
</h2>
</div>
</div>
</section>
<!--<section class="section">-->
<!-- <div class="container is-max-desktop">-->
<!-- <div class="columns is-centered">-->
<!-- <!– Visual Effects. –>-->
<!-- <div class="column">-->
<!-- <div class="content">-->
<!-- <h2 class="title is-3">Multi-View Deformation Network</h2>-->
<!-- <!– <p>–>-->
<!-- <!– Using <i>nerfies</i> you can create fun visual effects. This Dolly zoom effect–>-->
<!-- <!– would be impossible without nerfies since it would require going through a wall.–>-->
<!-- <!– </p>–>-->
<!-- <img src="./static/images/pool_sample.png" width="600"/>-->
<!-- </div>-->
<!-- </div>-->
<!-- <!–/ Visual Effects. –>-->
<!-- <!– Matting. –>-->
<!-- <div class="column">-->
<!-- <h2 class="title is-3">Camera Pose Estimation Network</h2>-->
<!-- <div class="columns is-centered">-->
<!-- <div class="column content">-->
<!-- <img src="./static/images/camnet.png">-->
<!-- </div>-->
<!-- </div>-->
<!-- </div>-->
<!-- </div>-->
<!-- </div>-->
<!--</section>-->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>
@inproceedings{dong2024memflow,
title={MemFlow: Optical Flow Estimation and Prediction with Memory},
author={Dong, Qiaole and Fu, Yanwei},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2024}
}
</code></pre>
</div>
</section>
<footer class="footer">
<div class="container">
<div class="content has-text-centered">
<a class="icon-link"
href="https://arxiv.org/abs/2404.04808">
<i class="fas fa-file-pdf"></i>
</a>
<a class="icon-link" href="https://github.com/DQiaole/MemFlow" class="external-link" disabled>
<i class="fab fa-github"></i>
</a>
</div>
<div class="columns is-centered">
<div class="column is-8">
<div class="content">
<p>
This website is licensed under a <a rel="license"
href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
Commons Attribution-ShareAlike 4.0 International License</a>.
</p>
<p>
This means you are free to borrow the <a
href="https://github.com/nerfies/nerfies.github.io">source code</a> of this website,
we just ask that you link back to this page in the footer.
Please remember to remove the analytics code included in the header of the website which
you do not want on your website.
</p>
</div>
</div>
</div>
</div>
</footer>
</body>
</html>