-
Notifications
You must be signed in to change notification settings - Fork 0
/
back.html
475 lines (401 loc) · 22.4 KB
/
back.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
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
470
471
472
473
474
475
<!DOCTYPE html>
<html>
<!-- Mirrored from sfa.cs.columbia.edu/ by HTTrack Website Copier/3.x [XR&CO'2014], Fri, 21 Jul 2023 10:56:34 GMT -->
<head>
<title>
A Dataset and Benchmark for Copyright Protection from Text-to-Image Diffusion Models
</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=1000" />
<link rel="stylesheet" href="assets/css/main.css" />
<script>
(function (i, s, o, g, r, a, m) {
i["GoogleAnalyticsObject"] = r;
(i[r] =
i[r] ||
function () {
(i[r].q = i[r].q || []).push(arguments);
}),
(i[r].l = 1 * new Date());
(a = s.createElement(o)), (m = s.getElementsByTagName(o)[0]);
a.async = 1;
a.src = g;
m.parentNode.insertBefore(a, m);
})(
window,
document,
"script",
"https://www.google-analytics.com/analytics.js",
"ga"
);
ga("create", "UA-89797207-1", "auto");
ga("send", "pageview");
</script>
<meta property="og:url" content="https://sfa.cs.columbia.edu/" />
<meta property="og:type" content="website" />
<meta property="og:title"
content="Structure From Action: Learning Interactions for Articulated Object 3D Structure Discovery" />
<meta property="og:description"
content="Articulated objects make up a significant portion of our environment. Discovering their parts, joints, and kinematics is crucial for robots to interact with these objects. We introduce Structure from Action (SfA), a framework that discovers the 3D part geometry and joint parameters of unseen articulated objects via a sequence of inferred interactions. Our key insight is that 3D interaction and perception should be considered in conjunction to construct 3D articulated CAD models, especially in the case of categories not seen during training. By selecting informative interactions, SfA discovers parts and reveals initially occluded surfaces, like the inside of a closed drawer. By aggregating visual observations in 3D, SfA accurately segments multiple parts, reconstructs part geometry, and infers all joint parameters in a canonical coordinate frame. Our experiments demonstrate that a single SfA model trained in simulation can generalize to many unseen object categories with unknown kinematic structures and to real-world objects. Code and data will be publicly available." />
</head>
<body id="top">
<!-- Main -->
<div id="main" style="
padding-bottom: 1em;
padding-top: 5em;
width: 60em;
max-width: 70em;
margin-left: auto;
margin-right: auto;
">
<section id="four">
<h1 style="text-align: center; margin-bottom: 0">
<font color="4e79a7">CPDM:Copyright Protection in Diffusion Models</font>
</h1>
<h3 style="text-align: center">
A Dataset and Benchmark for Copyright Protection from Text-to-Image Diffusion Models
</h2>
<span class="image right" style="
max-width: 50%;
margin-top: 0.5em;
margin-bottom: 0;
border: 0px solid #415161;
">
<!-- <img src="images/intra_1.jpg" width="20%" alt="" /> -->
</span>
<span class="image center" style="
max-width: 100%;
max-height: 50%;
margin-top: 0.5em;
margin-bottom: 0;
border: 0px solid #415161;
">
<img src="images/intra_1.jpg" width="100%" alt="" />
</span>
<p >
Copyright is a legal right that grants creators the exclusive authority to
reproduce, distribute, and profit from their creative works. However, the
recent advancements in text-to-image generation techniques have posed
significant challenges to copyright protection, as these methods have
facilitated the learning of unauthorized content, artistic creations,
and portraits, which are subsequently utilized to generate and disseminate
uncontrolled content. Especially, the use of stable diffusion, an emerging
model for text-to-image generation, poses an increased risk of unauthorized
copyright infringement and distribution. Currently, there is a lack of
systematic studies evaluating the potential correlation between content
generated by stable diffusion and those under copyright protection.
Conducting such studies faces several challenges, including i) t
he intrinsic ambiguity related to copyright infringement in text-to-image models,
ii) the absence of a comprehensive large-scale dataset, and
iii) the lack of standardized metrics for defining copyright infringement.
This work provides the first large-scale standardized dataset and
benchmark on copyright protection. Specifically, we propose a pipeline
to coordinate CLIP, ChatGPT, and diffusion models to generate a dataset
that contains anchor images, corresponding prompts, and images generated
by text-to-image models, reflecting the potential abuses of copyright.
Furthermore, we explore a suite of evaluation metrics to judge the
effectiveness of copyright protection methods. The proposed dataset,
benchmark library, and evaluation metrics will be open-sourced to
facilitate future research and application.
</p>
<!-- <hr/ style="margin-top: 1em">
<h3>Highlights</h3>
<section>
<div class="box alt" style="margin-bottom: 0em;">
<div class="row 50% uniform" style="width: 100%;">
<div class="3u" style="margin-top: -5em; font-size: 0.7em; line-height: 1.5em; text-align: center;"><a href="https://ai.googleblog.com/2019/03/unifying-physics-and-deep-learning-with.html"><span class="image fit" style="margin-bottom: 0.5em;"><img src="images/logo-GoogleAI.png" alt="" style="height: 19em; width: auto;" /></span></a></div>
<div class="3u" style="margin-left: 3em; margin-top: 1.6em; font-size: 0.7em; line-height: 1.5em; text-align: center;"><a href="https://www.nytimes.com/2019/03/26/technology/google-robotics-lab.html"><span class="image fit" style="margin-bottom: 0.5em;"><img src="images/logo-nyt.jpg" alt="" style="height: 6em; width: auto;" /></span></a></div>
<div class="3u" style="margin-left: 6em; margin-top: 2.6em; font-size: 0.7em; line-height: 1.5em; text-align: center;"><a href="https://spectrum.ieee.org/automaton/robotics/artificial-intelligence/google-teaches-robot-to-toss-bananas-better-than-you-do"><span class="image fit" style="margin-bottom: 0.5em;"><img src="images/logo-ieeespectrum.png" alt="" style="height: 3em; width: auto;" /></span></a></div>
</div>
</div>
</section> -->
<hr />
<!-- <hr/ style="margin-top: 1em"> -->
<!-- <div class="row">
<div class="12u$ 12u$(xsmall)" style="text-align: center">
<h3>Technical Summary Video</h3>
<iframe
id="match-video"
width="640"
height="360"
style="
margin-bottom: 2em;
margin-left: auto;
margin-right: auto;
display: block;
"
src="https://youtu.be/4Oz2Q5hxtnE"
frameborder="0"
allowfullscreen
></iframe>
</div>
</div> -->
<!-- Reconstruction Results -->
<h2>Pipeline of Dataset Generation</h2>
<!-- <p>
To validate the generalization of our approach to real-world
data. The model performs well on previously unseen instances in the real world despite
challenging noise artifacts from the real RGBD camera.
</p>
-->
<!-- <center>
<img style="width:100%" src="images/sfa-real-world.png" alt="" />
</center> -->
<span class="image center" style="
max-width: 100%;
max-height: 100%;
margin-top: 0.5em;
margin-bottom: 0;
border: 0px solid #415161;
">
<img src="images/data-generation.png" height="90%" width="90%" alt="" style="margin: 0 auto;"/>
</span>
<p>
<strong><em>Pipeline for generating CPDM datasets</em></strong> : The clip interrogator is employed to convert
copyrighted images into textual information that corresponds to them. This text is subsequently
refined and transformed into prompts, which are then inputted into a diffusion model to generate the
corresponding infringing images.
</p>
<!-- <video autoplay="true" loop="" muted="" style="width: 80%; margin-left: 10%;">
<source src="videos/real-world-video.mp4" type="video/mp4">
</video> -->
<hr style="margin-top: 0em" />
<h2>Statistics and Details of the Dataset</h2>
<!-- <p>
To validate the generalization of our approach to real-world
data. The model performs well on previously unseen instances in the real world despite
challenging noise artifacts from the real RGBD camera.
</p>
-->
<!-- <center>
<img style="width:100%" src="images/sfa-real-world.png" alt="" />
</center> -->
<span class="image center" style="
max-width: 100%;
max-height: 50%;
margin-top: 0.5em;
margin-bottom: 0;
border: 0px solid #415161;
">
<img src="images/statistics.jpg" width="90%" alt="" style="margin: 0 auto;" />
</span>
<p>
<strong><em>Style</em></strong> : Painting artworks often embody the distinctive style of the artist, encompassing aspects such
as brushstrokes, lines, colors, and compositions.<br>
<strong><em>Portrait</em></strong> : An individual’s control and use of their own portrait, including
facial features, image, and posture.<br>
<strong><em>Artistic Creation Figure</em></strong> : Artistic creations, including characters from animations and cartoons, are
often protected by law.<br>
<strong><em>Licensed Illustration</em></strong> : We have obtained authorization to use a portion of Yi Qu’s artworks in this
study.
</p>
<!-- <video autoplay="true" loop="" muted="" style="width: 80%; margin-left: 10%;">
<source src="videos/real-world-video.mp4" type="video/mp4">
</video> -->
<!-- Reconstruction Results -->
<hr style="margin-top: 0em" />
<h2>Experiments</h2>
<p>
We conducted testing for unlearning utilizing <strong>Gradient Ascent-based Approach</strong> and <strong>Weight Pruning-based Approach</strong>, while assessing the efficacy of our metric.
</p>
<span class="image center" style="
max-width: 100%;
max-height: 50%;
margin-top: 0.5em;
margin-bottom: 0;
border: 0px solid #415161;
">
<img src="images/illustration_1.jpg" width="100%" alt="" />
</span>
<p>
Experimental Results of Model Unlearning. Each row represents an illustration sample,
where from left to right, it denotes the original image, the image generated after fine-tuning with
Stable Diffusion, the image generated with SD-v2.1, and the image generated after unlearning using
gradient ascent and pruning methods.
</p>
<!--
<p>
Given a raw RGB point
cloud, SfA infers and executes informative actions to construct an articulated CAD model, which consists
of multiple 3D part meshes and the revolute, and prismatic joints connecting them.
The SfA framework consists of four components: an
interaction policy, which chooses informative actions that move parts, a part aggregation module,
which tracks part discoveries over a sequence of interactions, a joint estimation module,
which predicts joint parameters and kinematic constraints of the articulation, and finally, the pipeline
for
the construction of the articulated CAD model.
</p>
<center>
<img style="width:100%" src="images/sfa_approach.png" alt="" />
</center> -->
<!-- Reconstruction Results -->
<hr style="margin-top: 0em" />
<h2>SfA Interaction and Perception Pipeline</h2>
<p>
The video below demonstrates
SfA's ability to infer informative multi-step interactions given an articulated object,
and generate the articulated 3D CAD model of the object overtime. By first inferring the push
and hold actions, and then inferring the parts reconstruction and joint parameters, SfA is
able to construct the full 3D articulated CAD model in 3 steps.
<!-- Given a raw RGB point
cloud, SfA infers and executes informative actions to construct an articulated CAD model, which consists
of multiple 3D part meshes and the revolute, and prismatic joints connecting them. -->
<!-- The SfA framework consists of four components: an
interaction policy, which chooses informative actions that move parts, a part aggregation module,
which tracks part discoveries over a sequence of interactions, a joint estimation module,
which predicts joint parameters and kinematic constraints of the articulation, and finally, the pipeline for
the construction of the articulated CAD model. -->
</p>
<video autoplay="" loop="" muted="" style="width: 80%; margin-left: 10%;">
<source src="videos/sim_sequence.mp4" type="video/mp4">
</video>
<!-- <hr /> -->
<!-- Reconstruction Results -->
<hr style="margin-top: 0em" />
<h2>3D Articulated CAD Models Results</h2>
<center>
<img style="width:100%" src="images/sfa-fig-sequence-results-sim-cvpr-version.png" alt="" />
</center>
<p>
On the top, we show the step-by-step results from the SfA pipeline. The inferred actions prioritize new
parts discovery and expose articulations. Below we show
SfA 3D reconstruction result on unseen objects with different shapes, sizes, and kinematic structures.
The pipeline can handle both large (furniture) and small (scissor) objects, as well as prismatic
(drawers, pots)
and revolute (microwave, chair) joints. Our method outperforms the Ditto
on both parts reconstruction and joints estimation (revolute: red, prismatic: blue).
</p>
<p>
We show the complete and animated 3D articulated CAD model from the SfA pipeline.
</p>
<video autoplay="" loop="" muted="" style="width: 90%; margin-left: 10%;">
<source src="videos/results-1.mp4" type="video/mp4">
</video>
<video autoplay="" loop="" muted="" style="width: 90%; margin-left: 10%;">
<source src="videos/results-2.mp4" type="video/mp4">
</video>
<hr style="margin-top: 0em" />
<h3>Paper</h3>
<!-- <hr/> -->
<p style="margin-bottom: 1em">
Latest version:
<a href="http://arxiv.org/abs/2207.08997">arXiv: [cs.CV]</a>
or <a href="paper.pdf">here</a>
</p>
<div class="12u$">
<a href="https://arxiv.org/abs/2207.08997"><span class="image fit"
style="border: 1px solid; border-color: #888888"><img src="images/paper-thumbnail.jpg"
alt="" /></span></a>
</div>
<p style="margin-bottom: 1em">
Code and instructions will be avaliable.
<!-- <a href="https://github.com/columbia-ai-robotics/SfA">here</a>. -->
</p>
<hr style="margin-top: 0em" />
<h2>Team</h2>
<section>
<div class="box alt" style="margin-bottom: 1em">
<div class="row 50% uniform" style="width: 90%">
<div class="2u" style="
font-size: 0.7em;
line-height: 1.5em;
text-align: center;
">
<a href="https://www.neilnie.com/">
<span class="image fit" style="margin-bottom: 0.5em">
<img src="images/neil-thumbnail.jpg" alt="" style="border-radius: 50%" />
</span>Neil Nie<sup>1</sup>
</a>
</div>
<div class="2u" style="
font-size: 0.7em;
line-height: 1.5em;
text-align: center;
">
<a href="https://sagadre.github.io/">
<span class="image fit" style="margin-bottom: 0.5em">
<img src="images/samir-thumbnail.jpg" alt="" style="border-radius: 50%" />
</span>Samir Yitzhak Gadre <sup>1</sup>
</a>
</div>
<div class="2u" style="
font-size: 0.7em;
line-height: 1.5em;
text-align: center;
">
<a href="https://sites.google.com/view/ehsanik-personal-website">
<span class="image fit" style="margin-bottom: 0.5em">
<img src="images/kiana-thumbnail.jpg" alt="" style="border-radius: 50%" />
</span>Kiana Ehsani<sup>2</sup>
</a>
</div>
<div class="2u" style="
font-size: 0.7em;
line-height: 1.5em;
text-align: center;
">
<a href="https://www.cs.columbia.edu/~shurans/"><span class="image fit"
style="margin-bottom: 0.5em"><img src="images/shuran-thumbnail.jpg" alt=""
style="border-radius: 50%" /></span>Shuran Song <sup>1</sup></a>
</div>
</div>
</div>
</section>
<sup>1</sup> Columbia
University <sup>2</sup>
Allen Institute for AI
<hr style="margin-top: 1em" />
<h3>Acknowledgements</h3>
<p>
This work was supported in part by National Science Foundation under 2143601, 2037101, and 2132519.
Thank you Cheng Chi, Huy Ha, Zhenjia Xu, Zeyi Liu, and other colleagues of the CAIR lab for your
valuable feedback and support. Thanks to Cheng Chi and Zhenjia Xu for your help with
the UR5 robot experiments.
We would like to thank Google for the UR5 robot hardware.
</p>
<hr style="margin-top: 1em">
<h3>Contact</h3>
<p>
If you have any questions, please feel free to contact
<a href="mailto:neil.nie@columbia.edu">Neil</a>
</p>
<hr />
<div class="row" style="margin-top: 1em">
<div class="12u$ 12u$(xsmall)">
<h3>Bibtex</h3>
<pre><code>
@article{nie2022sfa,
title={Structure from Action: Learning Interactions for Articulated Object 3D Structure Discovery},
author={Nie, Neil and Gadre, Samir Yitzhak and Ehsani, Kiana and Song, Shuran},
journal={arxiv},
year={2022} }
</code>
</pre>
</div>
</div>
</section>
</div>
<!-- Footer -->
<footer id="footer">
<div class="inner">
<ul class="copyright">
<li>
Meet
<a href="https://en.wikipedia.org/wiki/Danbo_(character)">Danbo</a>
the cardboard robot.
</li>
</ul>
</div>
</footer>
<!-- Scripts -->
<script src="assets/js/jquery.min.js"></script>
<script src="assets/js/jquery.poptrox.min.js"></script>
<script src="assets/js/skel.min.js"></script>
<script src="assets/js/util.js"></script>
<!--[if lte IE 8
]><script src="assets/js/ie/respond.min.js"></script
><![endif]-->
<script src="assets/js/main.js"></script>
</body>
<!-- Mirrored from sfa.cs.columbia.edu/ by HTTrack Website Copier/3.x [XR&CO'2014], Fri, 21 Jul 2023 10:57:11 GMT -->
</html>