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104                <h1 class="title is-1 publication-title">
105                    <u style="color: hotpink; text-decoration: underline dotted hotpink;">Clearer Frames,</u>
106                    <u style="color: dodgerblue; text-decoration: underline dotted dodgerblue;">Anytime:</u> Resolving
107                    Velocity Ambiguity in Video Frame
108                    Interpolation </h1>
109                <div class="is-size-5 publication-authors">
110                        <span class="author-block"><a
111                                href="https://zzh-tech.github.io/">Zhihang Zhong</a><sup>1,*</sup>,  </span>
112                    <span class="author-block"><a
113                            href="https://scholar.google.com/citations?user=BKYVv4MAAAAJ&hl=en">Gurunandan Krishnan</a><sup>2</sup>,  </span>
114                    <span class="author-block"><a
115                            href="https://jimmysuen.github.io/">Xiao Sun</a><sup>1</sup>,  </span>
116                    <span class="author-block"><a
117                            href="https://scholar.google.com/citations?user=gFtI-8QAAAAJ&hl=en">Yu Qiao</a><sup>1</sup>,  </span>
118                    <span class="author-block"><a
119                            href="https://sizhuoma.netlify.app/">Sizhuo Ma</a><sup>2,†</sup>,  </span>
120                    <span class="author-block"><a
121                            href="https://jianwang-cmu.github.io/">Jian Wang</a><sup>2,†</sup></span>
122                </div>
123
124                <div class="is-size-5 publication-authors">
125                    <span class="author-block"><sup>1</sup><a href="https://github.com/OpenGVLab">Shanghai AI Laboratory, OpenGVLab</a>, </span>
126                    <span class="author-block"><sup>2</sup><a href="https://snap.com/en-US">Snap Inc.</a></span>
127                    <br>
128                    <span class="author-block"><sup>*</sup>First author,</span>
129                    <span class="author-block"><sup>†</sup>Co-corresponding authors</span>
130                </div>
131
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144                            <a href="http://arxiv.org/abs/2311.08007"
145                               class="external-link button is-normal is-rounded is-dark"
146                               target="_blank">
147                              <span class="icon">
148                                  <i class="ai ai-arxiv"></i>
149                              </span>
150                              <span>arXiv</span>
151                            </a>
152                        </span>
153                        <span class="link-block">
154                          <a href="https://github.com/zzh-tech/InterpAny-Clearer"
155                             class="external-link button is-normal is-rounded is-dark"
156                             target="_blank">
157                            <span class="icon">
158                                <i class="fab fa-github"></i>
159                            </span>
160                            <span>Code</span>
161                            </a>
162                        </span>
163                        <span class="link-block">
164                          <a href="http://ai4sports.opengvlab.com/interpany-clearer/"
165                             class="external-link button is-normal is-rounded is-dark"
166                             target="_blank">
167                            <span class="icon">
168                                <i class="fa fa-star"></i>
169                            </span>
170                            <span>Demo</span>
171                            </a>
172                        </span>
173                        <div class="is-size-5 publication-authors">
174                            <img src="./static/images/eccv-navbar-logo.svg"
175                                 style="width: 18%;text-align: center">
176                            <br>
177                            <span style="color:darkred"><b>Accepted to ECCV'2024 as Oral</b></span>
178                        </div>
179                    </div>
180                </div>
181            </div>
182        </div>
183    </div>
184</section>
185
186<section class="hero teaser">
187    <div class="hero-body">
188        <div class="container is-max-desktop">
189            <img src="./static/images/teaser.jpg" alt="teaser">
190            <br>
191            <br>
192            <h2 class="subtitle has-text-centered">
193                <b>
194                    We addressed velocity ambiguity in video frame interpolation, leading to: <br>
195                    <em style="color: red">Clearer anytime frame interpolation</em> & <em style="color: red">Manipulated
196                    interpolation of anything</em>.
197                </b>
198            </h2>
199        </div>
200    </div>
201</section>
202
203<br>
204
205<div class="columns is-centered has-text-centered">
206    <div class="column is-four-fifths">
207        <h2 class="title is-3">Clearer anytime frame interpolation</h2>
208    </div>
209</div>
210<br>
211
212<section class="hero is-dark is-small">
213    <div class="hero-body">
214        <div class="container has-text-centered">
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217                    <video poster="" id="00006/0277" autoplay controls muted loop playsinline height="100%">
218                        <source src="./static/webapp_demo/00006/0277_demo_converted.mp4"
219                                type="video/mp4">
220                    </video>
221                </div>
222
223                <div class="results-item">
224                    <video poster="" id="00029/0419" autoplay controls muted loop playsinline height="100%">
225                        <source src="./static/webapp_demo/00029/0419_demo_converted.mp4"
226                                type="video/mp4">
227                    </video>
228                </div>
229
230                <div class="results-item">
231                    <video poster="" id="00036/0310" autoplay controls muted loop playsinline height="100%">
232                        <source src="./static/webapp_demo/00036/0310_demo_converted.mp4"
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239                        <source src="./static/webapp_demo/00036/0320_demo_converted.mp4"
240                                type="video/mp4">
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247                                type="video/mp4">
248                    </video>
249                </div>
250
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252                    <video poster="" id="00068/0261" autoplay controls muted loop playsinline height="100%">
253                        <source src="./static/webapp_demo/00068/0261_demo_converted.mp4"
254                                type="video/mp4">
255                    </video>
256                </div>
257
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259                    <video poster="" id="00071/0366" autoplay controls muted loop playsinline height="100%">
260                        <source src="./static/webapp_demo/00071/0366_demo_converted.mp4"
261                                type="video/mp4">
262                    </video>
263                </div>
264
265                <div class="results-item">
266                    <video poster="" id="00071/0808" autoplay controls muted loop playsinline height="100%">
267                        <source src="./static/webapp_demo/00071/0808_demo_converted.mp4"
268                                type="video/mp4">
269                    </video>
270                </div>
271
272                <div class="results-item">
273                    <video poster="" id="00080/0050" autoplay controls muted loop playsinline height="100%">
274                        <source src="./static/webapp_demo/00080/0050_demo_converted.mp4"
275                                type="video/mp4">
276                    </video>
277                </div>
278
279            </div>
280
281            <div class="columns is-centered has-text-centered">
282                <div class="column is-three-quarters">
283                    <p>
284                        <em>
285                            <b>When integrating our plug-and-play training strategies ([D,R]) into the state-of-the-art
286                                learning-based models such as
287                                <a href="https://github.com/megvii-research/ECCV2022-RIFE">RIFE [1]</a>,
288                                <a href="https://github.com/ltkong218/IFRNet">IFRNet [2]</a>,
289                                <a href="https://github.com/MCG-NKU/AMT">AMT [3]</a>, and
290                                <a href="https://github.com/ltkong218/IFRNet">EMA-VFI [4]</a>, they exhibit markedly
291                                sharper outputs and superior perceptual quality in arbitrary time interpolations. <br>
292                                (Here, we employ RIFE as an illustrative example, generating 128 interpolated frames
293                                using just two images.)
294                            </b>
295                        </em>
296                    </p>
297                </div>
298            </div>
299        </div>
300    </div>
301</section>
302
303<br>
304<br>
305
306<section class="section">
307    <!-- Paper video. -->
308    <div style="display: flex; justify-content: center; align-items: center; height: 75vh; width: 100%;">
309        <div style="width: 66%; height: auto;">
310            <div class="columns is-centered has-text-centered">
311                <div class="column is-four-fifths">
312                    <h2 class="title is-3">Application in CCTV</h2>
313                    <br>
314                    <div class="publication-video">
315                        <iframe src="./static/webapp_demo/cctv5_interpany-clearer.mp4" encrypted-media allowfullscreen></iframe>
316                    </div>
317                    <br>
318                    <div class="columns is-centered has-text-centered">
319                        <div class="column is-three-quarters">
320                            <p>
321                                <em>
322                                    <b>
323                                        <u style="color: black; text-decoration: underline black;">
324                                            Our technology was used by CCTV5 as well as CCTV5+ for slow motion
325                                            demonstrations of athletes jumping in the 2024 Thomas & Uber Cup.</u>
326                                    </b>
327                                </em>
328                            </p>
329                        </div>
330                    </div>
331                </div>
332            </div>
333        </div>
334    </div>
335    <!--/ Paper video. -->
336</section>
337
338<br>
339<br>
340
341<section class="section">
342    <!-- Paper video. -->
343    <div style="display: flex; justify-content: center; align-items: center; height: 75vh; width: 100%;">
344        <div style="width: 66%; height: auto;">
345            <div class="columns is-centered has-text-centered">
346                <div class="column is-four-fifths">
347                    <h2 class="title is-3">Manipulated interpolation of anything</h2>
348                    <br>
349                    <div class="publication-video">
350                        <iframe src="./static/webapp_demo/InterpAny-Clearer-Demo.mp4"
351                                allow="autoplay; encrypted-media" allowfullscreen></iframe>
352                    </div>
353                    <br>
354                    <div class="columns is-centered has-text-centered">
355                        <div class="column is-three-quarters">
356                            <p>
357                                <em>
358                                    <b>
359                                        Additionally, our strategies enable temporal manipulation of each object
360                                        independently during the inference stage, offering a novel tool for video
361                                        editing tasks like re-timing. <u
362                                            style="color: black; text-decoration: underline black;">Our <a
363                                            href="http://ai4sports.opengvlab.com/interpany-clearer/"
364                                            target="_blank">APP</a> also
365                                        supports video as an input to classic video frame interpolation.</u>
366                                    </b>
367                                </em>
368                            </p>
369                        </div>
370                    </div>
371                </div>
372            </div>
373        </div>
374    </div>
375    <!--/ Paper video. -->
376</section>
377
378<hr/>
379
380<section class="section">
381    <div class="container is-max-desktop">
382        <!-- Abstract. -->
383        <div class="columns is-centered has-text-centered">
384            <div class="column is-four-fifths">
385                <h2 class="title is-3">Abstract</h2>
386                <div class="content has-text-justified">
387                    <p>
388                        Existing video frame interpolation (VFI) methods blindly predict where each object is at a
389                        specific timestep t ("time indexing"), which struggles to predict precise object movements.
390                        Given two images of a &#9918, there are infinitely many possible trajectories: accelerating or
391                        decelerating, straight or curved. This often results in blurry frames as the method averages out
392                        these possibilities. Instead of forcing the network to learn this complicated time-to-location
393                        mapping implicitly together with predicting the frames, we provide the network with an explicit
394                        hint on how far the object has traveled between start and end frames, a novel approach termed
395                        "distance indexing". This method offers a clearer learning goal for models, reducing the
396                        uncertainty tied to object speeds. We further observed that, even with this extra guid
396ance,
397                        objects can still be blurry especially when they are equally far from both input frames
398                        (<i>i.e.</i>, halfway in-between), due to the directional ambiguity in long-range motion. To
399                        solve this, we propose an iterative reference-based estimation strategy that breaks down a
400                        long-range prediction into several short-range steps. When integrating our plug-and-play
401                        strategies into state-of-the-art learning-based models, they exhibit markedly sharper outputs
402                        and superior perceptual quality in arbitrary time interpolations, using a uniform distance
403                        indexing map in the same format as time indexing. Additionally, distance indexing can be
404                        specified pixel-wise, which enables temporal manipulation of each object independently, offering
405                        a novel tool for video editing tasks like re-timing.
406                    </p>
407                </div>
408            </div>
409        </div>
410        <!--/ Abstract. -->
411
412        <!--    &lt;!&ndash; Paper video. &ndash;&gt;-->
413        <!--    <div class="columns is-centered has-text-centered">-->
414        <!--      <div class="column is-four-fifths">-->
415        <!--        <h2 class="title is-2">Video</h2>-->
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424        <!--    &lt;!&ndash;/ Paper video. &ndash;&gt;-->
425    </div>
426</section>
427
428<section>
429    <div class="container is-max-desktop">
430        <div class="content has-text-justified">
431            <h3 class="title is-4">Velocity ambiguity problem</h3>
432            <p>
433                The current mainstream algorithm for arbitrary-time frame interpolation, denoted as \(\mathcal{F}\),
434                predicts the target frame \(I_t\) using the starting frame \(I_0\), the ending frame \(I_1\), and a time
435                index
436                \(t\) as inputs:
437
438                $$I_t = \mathcal{F}\left(I_0, I_1, t\right)$$
439
440                However, the unknown motion velocity of each independently moving object introduces the issue of
441                <b>"velocity ambiguity"</b>. This means there are multiple possible mappings from the same inputs to
442                different locations:
443
444                $$\left\{I_t^1, I_t^2, \ldots, I_t^n\right\} = \mathcal{F}(I_0, I_1, t)$$
445
446                Taking a &#9918 as an example, there are countless potential landing spots for the ball in mid-air,
447                leading to conflicts in learning during the training process.
448
449                In short, the algorithm cannot discern which scenario is correct to learn, so it settles for an average
450                state:
451
452                $$ \hat{I}_t = \mathbb{E}_{I_t \sim \mathcal{F}(I_0, I_1, t)}[I_t] $$
453
454                This results in the algorithm's prediction \(\hat{I}_t\) being blurry during testing:
455            </p>
456            <div class="has-text-centered">
457                <img style="width: 100%;" src="./static/images/velocity_ambiguity.jpg"
458                     alt="Velocity ambiguity"/>
459            </div>
460        </div>
461    </div>
462</section>
463
464<br>
465<br>
466
467<section>
468    <div class="container is-max-desktop">
469        <div class="content has-text-justified">
470            <h3 class="title is-4">Disambiguation</h3>
471            <p>
472                To resolve velocity ambiguity, a new paradigm for index-based learning is required.
473                <i><b>We need to guide the algorithm on why objects land in certain positions:</b></i>
474                $$I_{t} = \mathcal{F}\left(I_0, I_1, \text{motion hint}\right)$$
475            </p>
476            <h4 class="title is-5">Distance indexing</h4>
477            <p>
478                In fact, the training approach using time indexing requires the algorithm to not only learn how to
479                interpolate frames but also to guess the mapping relationship from time to position, denoted as
480                \(\mathcal{D}\):
481
482                $$I_t = \mathcal{F}(I_0, I_1, t) \to I_t = \mathcal{F}(I_0, I_1, \mathcal{D}(t))$$
483
484                Our solution involves calculating a path distance ratio map \(D_t\) to replace the time \(t\) for
485                index-based learning:
486
487                $$I_t = \mathcal{F}(I_0, I_1, \mathcal{D}(t)) \to I_t = \mathcal{F}(I_0, I_1, D_t)$$
488            </p>
489            <div class="has-text-centered">
490                <img style="width: 100%;" src="./static/images/distance_indexing.jpg"
491                     alt="Distance indexing"/>
492            </div>
493            <br>
494            <p>
495                We first compute the optical flows from \(I_0\) to \(I_t\) and from \(I_0\) to \(I_1\), denoted as
496                \(V_{0\to t}\) and \(V_{0\to 1}\), respectively. Then, for each pixel location \((x,y)\), we calculate
497                the proportion of the optical flow \(V_{0\to t}\) projected onto \(V_{0\to 1}\), denoted as the "path
498                distance ratio":
499
500                $$D_t(x,y) = \frac{\left\Vert \mathbf{V}_{0\to t}(x,y)\right\Vert \cos{\theta}}{\left\Vert
501                \mathbf{V}_{0\to 1}(x,y) \right\Vert}$$
502
503                <i><b>With \(D_t\), the algorithm avoids the ambiguous time-to-position mappings during training caused
504                    by varying velocities, allowing for clearer predictions during testing. Importantly, even without
505                    the ability to calculate the exact \(D_t\) using ground truth labels during inference, providing a
506                    uniform index map similar to time indexing, <i>i.e.</i>, \(D_t=t\), the algorithm can still predict
507                    clearer images (simulating uniform motion of objects).</b></i>
508            </p>
509            <br>
510            <h4 class="title is-5">Iterative reference-based estimation</h4>
511            <p>
512                While path distance indexing helps us sidestep ambiguities in speed, it does not resolve directional
513                ambiguities. We apply a classic divide-and-conquer strategy to minimize the impact of directional
514                ambiguities, further improving the predicted outcome.
515                <br>
516                <b>In practice, we break down a long-distance inference into a series of short-distance inferences from
517                    near to far, using the previous inference, along with the starting and ending frames, as references
518                    to avoid accumulated errors:</b>
519
520                $$I_t = \mathcal{F}(I_0, I_1, D_t, I_{\text{ref}}, D_{\text{ref}})$$
521
522                For example, dividing the inference into two steps would look like:
523
524                $$I_{t/2} = \mathcal{F}(I_0, I_1, D_{t/2}, I_{0}, D_{0})$$
525
526                $$I_{t} = \mathcal{F}(I_0, I_1, D_{t}, I_{t/2}, D_{t/2})$$
527
528                Similarly, taking a &#9918 as an example, our proposed strategies are illustrated in the following
529                figure:
530            </p>
531            <div class="has-text-centered">
532                <img style="width: 100%;" src="./static/images/disambiguation.jpg"
533                     alt="Disambiguation"/>
534            </div>
535            <br>
536            <br>
537            <h4 class="title is-5">Editable interpolation</h4>
538            <p>
539                <b>Beyond using a uniform index map like time indexing, we can also take advantage of the 2D editable
540                    nature of path distance indexing to implement editable frame interpolation techniques.</b>
541                Initially, we can obtain masks for objects of interest using the Segment Anything Model (SAM) [5]. We
542                then
543                customize the path distance curves for different object regions to achieve manipulated interpolation of
544                anything.
545            </p>
546            <div class="has-text-centered">
547                <img style="width: 100%;" src="./static/images/manipulation.jpg"
548                     alt="Manipulation"/>
549            </div>
550        </div>
551    </div>
552</section>
553
554<br>
555<br>
556
557<section>
558    <div class="container is-max-desktop">
559        <div class="content has-text-justified">
560            <h2 class="title is-3">Ending</h2>
561            <p>
562                This work presents the next generation of video frame interpolation technology, aiming to inspire
563                readers and contribute to fields like video enhancement, editing, and generation! 🔥🔥🔥
564                <br>
565                Welcome to &#127775; <a href="https://github.com/zzh-tech/InterpAny-Clearer">this project</a>
566                and follow <a href="https://github.com/zzh-tech">the author's GitHub</a>~
567            </p>
568        </div>
569    </div>
570</section>
571
572<br>
573<hr/>
574
575<section class="section" id="BibTeX">
576    <div class="container is-max-desktop content">
577        <h2 class="title">BibTeX</h2>
578        <pre><code>@article{zhong2023clearer,
579    title={Clearer Frames, Anytime: Resolving Velocity Ambiguity in Video Frame Interpolation},
580    author={Zhong, Zhihang and Krishnan, Gurunandan and Sun, Xiao and Qiao, Yu and Ma, Sizhuo and Wang, Jian},
581    journal={arXiv preprint arXiv:2311.08007},
582    year={2023}}</code></pre>
583    </div>
584</section>
585
586<section class="section" id="acknowledgements">
587    <div class="container content is-max-desktop">
588        <h2 class="title">Acknowledgements</h2>
589        <p>
590            We thank Dorian Chan, Zhirong Wu, and Stephen Lin for their insightful feedback and advice. Our thanks also
591            go to Vu An Tran for developing the web application, and to Wei Wang for coordinating the user study.
592        </p>
593    </div>
594</section>
595
596<section class="section" id="reference">
597    <div class="container content is-max-desktop">
598        <h2 class="title">Reference</h2>
599        <p>
600            [1] Huang, Zhewei, Tianyuan Zhang, Wen Heng, Boxin Shi, and Shuchang Zhou. "Real-time intermediate flow
601            estimation for video frame interpolation." In European Conference on Computer Vision, pp. 624-642. Cham:
602            Springer Nature Switzerland, 2022.
603            <br>
604            [2] Kong, Lingtong, Boyuan Jiang, Donghao Luo, Wenqing Chu, Xiaoming Huang, Ying Tai, Chengjie Wang, and Jie
605            Yang. "Ifrnet: Intermediate feature refine network for efficient frame interpolation." In Proceedings of the
606            IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1969-1978. 2022.
607            <br>
608            [3] Li, Zhen, Zuo-Liang Zhu, Ling-Hao Han, Qibin Hou, Chun-Le Guo, and Ming-Ming Cheng. "AMT: All-Pairs
609            Multi-Field Transforms for Efficient Frame Interpolation." In Proceedings of the IEEE/CVF Conference on
610            Computer Vision and Pattern Recognition, pp. 9801-9810. 2023.
611            <br>
612            [4] Zhang, Guozhen, Yuhan Zhu, Haonan Wang, Youxin Chen, Gangshan Wu, and Limin Wang. "Extracting motion and
613            appearance via inter-frame attention for efficient video frame interpolation." In Proceedings of the
614            IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5682-5692. 2023.
615            <br>
616            [5] Kirillov, Alexander, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao,
617            Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick. "Segment Anything." In
618            Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4015-4026. 2023
619        </p>
620    </div>
621</section>
622
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