---
_id: '14320'
abstract:
- lang: eng
text: The development of two-dimensional materials has resulted in a diverse range
of novel, high-quality compounds with increasing complexity. A key requirement
for a comprehensive quantitative theory is the accurate determination of these
materials' band structure parameters. However, this task is challenging due to
the intricate band structures and the indirect nature of experimental probes.
In this work, we introduce a general framework to derive band structure parameters
from experimental data using deep neural networks. We applied our method to the
penetration field capacitance measurement of trilayer graphene, an effective probe
of its density of states. First, we demonstrate that a trained deep network gives
accurate predictions for the penetration field capacitance as a function of tight-binding
parameters. Next, we use the fast and accurate predictions from the trained network
to automatically determine tight-binding parameters directly from experimental
data, with extracted parameters being in a good agreement with values in the literature.
We conclude by discussing potential applications of our method to other materials
and experimental techniques beyond penetration field capacitance.
acknowledgement: A.F.Y. acknowledges primary support from the Department of Energy
under award DE-SC0020043, and additional support from the Gordon and Betty Moore
Foundation under award GBMF9471 for group operations.
article_number: '125411'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Paul M
full_name: Henderson, Paul M
id: 13C09E74-18D9-11E9-8878-32CFE5697425
last_name: Henderson
orcid: 0000-0002-5198-7445
- first_name: Areg
full_name: Ghazaryan, Areg
id: 4AF46FD6-F248-11E8-B48F-1D18A9856A87
last_name: Ghazaryan
orcid: 0000-0001-9666-3543
- first_name: Alexander A.
full_name: Zibrov, Alexander A.
last_name: Zibrov
- first_name: Andrea F.
full_name: Young, Andrea F.
last_name: Young
- first_name: Maksym
full_name: Serbyn, Maksym
id: 47809E7E-F248-11E8-B48F-1D18A9856A87
last_name: Serbyn
orcid: 0000-0002-2399-5827
citation:
ama: 'Henderson PM, Ghazaryan A, Zibrov AA, Young AF, Serbyn M. Deep learning extraction
of band structure parameters from density of states: A case study on trilayer
graphene. Physical Review B. 2023;108(12). doi:10.1103/physrevb.108.125411'
apa: 'Henderson, P. M., Ghazaryan, A., Zibrov, A. A., Young, A. F., & Serbyn,
M. (2023). Deep learning extraction of band structure parameters from density
of states: A case study on trilayer graphene. Physical Review B. American
Physical Society. https://doi.org/10.1103/physrevb.108.125411'
chicago: 'Henderson, Paul M, Areg Ghazaryan, Alexander A. Zibrov, Andrea F. Young,
and Maksym Serbyn. “Deep Learning Extraction of Band Structure Parameters from
Density of States: A Case Study on Trilayer Graphene.” Physical Review B.
American Physical Society, 2023. https://doi.org/10.1103/physrevb.108.125411.'
ieee: 'P. M. Henderson, A. Ghazaryan, A. A. Zibrov, A. F. Young, and M. Serbyn,
“Deep learning extraction of band structure parameters from density of states:
A case study on trilayer graphene,” Physical Review B, vol. 108, no. 12.
American Physical Society, 2023.'
ista: 'Henderson PM, Ghazaryan A, Zibrov AA, Young AF, Serbyn M. 2023. Deep learning
extraction of band structure parameters from density of states: A case study on
trilayer graphene. Physical Review B. 108(12), 125411.'
mla: 'Henderson, Paul M., et al. “Deep Learning Extraction of Band Structure Parameters
from Density of States: A Case Study on Trilayer Graphene.” Physical Review
B, vol. 108, no. 12, 125411, American Physical Society, 2023, doi:10.1103/physrevb.108.125411.'
short: P.M. Henderson, A. Ghazaryan, A.A. Zibrov, A.F. Young, M. Serbyn, Physical
Review B 108 (2023).
date_created: 2023-09-12T07:12:12Z
date_published: 2023-09-15T00:00:00Z
date_updated: 2023-09-20T09:38:24Z
day: '15'
department:
- _id: MaSe
- _id: ChLa
- _id: MiLe
doi: 10.1103/physrevb.108.125411
external_id:
arxiv:
- '2210.06310'
intvolume: ' 108'
issue: '12'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2210.06310
month: '09'
oa: 1
oa_version: Preprint
publication: Physical Review B
publication_identifier:
eissn:
- 2469-9969
issn:
- 2469-9950
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Deep learning extraction of band structure parameters from density of states:
A case study on trilayer graphene'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 108
year: '2023'
...
---
_id: '8562'
abstract:
- lang: eng
text: "Cold bent glass is a promising and cost-efficient method for realizing doubly
curved glass facades. They are produced by attaching planar glass sheets to curved
frames and require keeping the occurring stress within safe limits.\r\nHowever,
it is very challenging to navigate the design space of cold bent glass panels
due to the fragility of the material, which impedes the form-finding for practically
feasible and aesthetically pleasing cold bent glass facades. We propose an interactive,
data-driven approach for designing cold bent glass facades that can be seamlessly
integrated into a typical architectural design pipeline. Our method allows non-expert
users to interactively edit a parametric surface while providing real-time feedback
on the deformed shape and maximum stress of cold bent glass panels. Designs are
automatically refined to minimize several fairness criteria while maximal stresses
are kept within glass limits. We achieve interactive frame rates by using a differentiable
Mixture Density Network trained from more than a million simulations. Given a
curved boundary, our regression model is capable of handling multistable\r\nconfigurations
and accurately predicting the equilibrium shape of the panel and its corresponding
maximal stress. We show predictions are highly accurate and validate our results
with a physical realization of a cold bent glass surface."
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "We thank IST Austria’s Scientific Computing team for their support,
Corinna Datsiou and Sophie Pennetier for their expert input on the practical applications
of cold bent glass, and Zaha Hadid Architects and Waagner Biro for providing the
architectural datasets. Photo of Fondation Louis Vuitton by Francisco Anzola / CC
BY 2.0 / cropped.\r\nPhoto of Opus by Danica O. Kus. This project has received funding
from the European Union’s\r\nHorizon 2020 research and innovation program under
grant agreement No 675789 - Algebraic Representations in Computer-Aided Design for
complEx Shapes (ARCADES), from the European Research Council (ERC) under grant agreement
No 715767 - MATERIALIZABLE: Intelligent fabrication-oriented Computational Design
and Modeling, and SFB-Transregio “Discretization in Geometry and Dynamics” through
grant I 2978 of the Austrian Science Fund (FWF). F. Rist and K. Gavriil have been
partially supported by KAUST baseline funding."
article_number: '208'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Konstantinos
full_name: Gavriil, Konstantinos
last_name: Gavriil
- first_name: Ruslan
full_name: Guseinov, Ruslan
id: 3AB45EE2-F248-11E8-B48F-1D18A9856A87
last_name: Guseinov
orcid: 0000-0001-9819-5077
- first_name: Jesus
full_name: Perez Rodriguez, Jesus
id: 2DC83906-F248-11E8-B48F-1D18A9856A87
last_name: Perez Rodriguez
- first_name: Davide
full_name: Pellis, Davide
last_name: Pellis
- first_name: Paul M
full_name: Henderson, Paul M
id: 13C09E74-18D9-11E9-8878-32CFE5697425
last_name: Henderson
orcid: 0000-0002-5198-7445
- first_name: Florian
full_name: Rist, Florian
last_name: Rist
- first_name: Helmut
full_name: Pottmann, Helmut
last_name: Pottmann
- first_name: Bernd
full_name: Bickel, Bernd
id: 49876194-F248-11E8-B48F-1D18A9856A87
last_name: Bickel
orcid: 0000-0001-6511-9385
citation:
ama: Gavriil K, Guseinov R, Perez Rodriguez J, et al. Computational design of cold
bent glass façades. ACM Transactions on Graphics. 2020;39(6). doi:10.1145/3414685.3417843
apa: Gavriil, K., Guseinov, R., Perez Rodriguez, J., Pellis, D., Henderson, P. M.,
Rist, F., … Bickel, B. (2020). Computational design of cold bent glass façades.
ACM Transactions on Graphics. Association for Computing Machinery. https://doi.org/10.1145/3414685.3417843
chicago: Gavriil, Konstantinos, Ruslan Guseinov, Jesus Perez Rodriguez, Davide Pellis,
Paul M Henderson, Florian Rist, Helmut Pottmann, and Bernd Bickel. “Computational
Design of Cold Bent Glass Façades.” ACM Transactions on Graphics. Association
for Computing Machinery, 2020. https://doi.org/10.1145/3414685.3417843.
ieee: K. Gavriil et al., “Computational design of cold bent glass façades,”
ACM Transactions on Graphics, vol. 39, no. 6. Association for Computing
Machinery, 2020.
ista: Gavriil K, Guseinov R, Perez Rodriguez J, Pellis D, Henderson PM, Rist F,
Pottmann H, Bickel B. 2020. Computational design of cold bent glass façades. ACM
Transactions on Graphics. 39(6), 208.
mla: Gavriil, Konstantinos, et al. “Computational Design of Cold Bent Glass Façades.”
ACM Transactions on Graphics, vol. 39, no. 6, 208, Association for Computing
Machinery, 2020, doi:10.1145/3414685.3417843.
short: K. Gavriil, R. Guseinov, J. Perez Rodriguez, D. Pellis, P.M. Henderson, F.
Rist, H. Pottmann, B. Bickel, ACM Transactions on Graphics 39 (2020).
date_created: 2020-09-23T11:30:02Z
date_published: 2020-11-26T00:00:00Z
date_updated: 2024-02-21T12:43:21Z
day: '26'
ddc:
- '000'
department:
- _id: BeBi
doi: 10.1145/3414685.3417843
ec_funded: 1
external_id:
arxiv:
- '2009.03667'
isi:
- '000595589100048'
file:
- access_level: open_access
checksum: c7f67717ad74e670b7daeae732abe151
content_type: application/pdf
creator: bbickel
date_created: 2023-05-23T20:54:43Z
date_updated: 2023-05-23T20:54:43Z
file_id: '13084'
file_name: coldglass.pdf
file_size: 28964641
relation: main_file
success: 1
file_date_updated: 2023-05-23T20:54:43Z
has_accepted_license: '1'
intvolume: ' 39'
isi: 1
issue: '6'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Submitted Version
project:
- _id: 24F9549A-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '715767'
name: 'MATERIALIZABLE: Intelligent fabrication-oriented Computational Design and
Modeling'
publication: ACM Transactions on Graphics
publication_identifier:
eissn:
- 1557-7368
issn:
- 0730-0301
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
related_material:
link:
- description: News on IST Homepage
relation: press_release
url: https://ist.ac.at/en/news/bend-dont-break/
record:
- id: '8366'
relation: dissertation_contains
status: public
- id: '8761'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Computational design of cold bent glass façades
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 39
year: '2020'
...
---
_id: '6952'
abstract:
- lang: eng
text: 'We present a unified framework tackling two problems: class-specific 3D reconstruction
from a single image, and generation of new 3D shape samples. These tasks have
received considerable attention recently; however, most existing approaches rely
on 3D supervision, annotation of 2D images with keypoints or poses, and/or training
with multiple views of each object instance. Our framework is very general: it
can be trained in similar settings to existing approaches, while also supporting
weaker supervision. Importantly, it can be trained purely from 2D images, without
pose annotations, and with only a single view per instance. We employ meshes as
an output representation, instead of voxels used in most prior work. This allows
us to reason over lighting parameters and exploit shading information during training,
which previous 2D-supervised methods cannot. Thus, our method can learn to generate
and reconstruct concave object classes. We evaluate our approach in various settings,
showing that: (i) it learns to disentangle shape from pose and lighting; (ii)
using shading in the loss improves performance compared to just silhouettes; (iii)
when using a standard single white light, our model outperforms state-of-the-art
2D-supervised methods, both with and without pose supervision, thanks to exploiting
shading cues; (iv) performance improves further when using multiple coloured lights,
even approaching that of state-of-the-art 3D-supervised methods; (v) shapes produced
by our model capture smooth surfaces and fine details better than voxel-based
approaches; and (vi) our approach supports concave classes such as bathtubs and
sofas, which methods based on silhouettes cannot learn.'
acknowledgement: Open access funding provided by Institute of Science and Technology
(IST Austria).
article_processing_charge: Yes (via OA deal)
article_type: original
arxiv: 1
author:
- first_name: Paul M
full_name: Henderson, Paul M
id: 13C09E74-18D9-11E9-8878-32CFE5697425
last_name: Henderson
orcid: 0000-0002-5198-7445
- first_name: Vittorio
full_name: Ferrari, Vittorio
last_name: Ferrari
citation:
ama: Henderson PM, Ferrari V. Learning single-image 3D reconstruction by generative
modelling of shape, pose and shading. International Journal of Computer Vision.
2020;128:835-854. doi:10.1007/s11263-019-01219-8
apa: Henderson, P. M., & Ferrari, V. (2020). Learning single-image 3D reconstruction
by generative modelling of shape, pose and shading. International Journal of
Computer Vision. Springer Nature. https://doi.org/10.1007/s11263-019-01219-8
chicago: Henderson, Paul M, and Vittorio Ferrari. “Learning Single-Image 3D Reconstruction
by Generative Modelling of Shape, Pose and Shading.” International Journal
of Computer Vision. Springer Nature, 2020. https://doi.org/10.1007/s11263-019-01219-8.
ieee: P. M. Henderson and V. Ferrari, “Learning single-image 3D reconstruction by
generative modelling of shape, pose and shading,” International Journal of
Computer Vision, vol. 128. Springer Nature, pp. 835–854, 2020.
ista: Henderson PM, Ferrari V. 2020. Learning single-image 3D reconstruction by
generative modelling of shape, pose and shading. International Journal of Computer
Vision. 128, 835–854.
mla: Henderson, Paul M., and Vittorio Ferrari. “Learning Single-Image 3D Reconstruction
by Generative Modelling of Shape, Pose and Shading.” International Journal
of Computer Vision, vol. 128, Springer Nature, 2020, pp. 835–54, doi:10.1007/s11263-019-01219-8.
short: P.M. Henderson, V. Ferrari, International Journal of Computer Vision 128
(2020) 835–854.
date_created: 2019-10-17T13:38:20Z
date_published: 2020-04-01T00:00:00Z
date_updated: 2023-08-17T14:01:16Z
day: '01'
ddc:
- '004'
department:
- _id: ChLa
doi: 10.1007/s11263-019-01219-8
external_id:
arxiv:
- '1901.06447'
isi:
- '000491042100002'
file:
- access_level: open_access
checksum: a0f05dd4f5f64e4f713d8d9d4b5b1e3f
content_type: application/pdf
creator: dernst
date_created: 2019-10-25T10:28:29Z
date_updated: 2020-07-14T12:47:46Z
file_id: '6973'
file_name: 2019_CompVision_Henderson.pdf
file_size: 2243134
relation: main_file
file_date_updated: 2020-07-14T12:47:46Z
has_accepted_license: '1'
intvolume: ' 128'
isi: 1
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: 835-854
project:
- _id: B67AFEDC-15C9-11EA-A837-991A96BB2854
name: IST Austria Open Access Fund
publication: International Journal of Computer Vision
publication_identifier:
eissn:
- 1573-1405
issn:
- 0920-5691
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning single-image 3D reconstruction by generative modelling of shape, pose
and shading
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 128
year: '2020'
...
---
_id: '8063'
abstract:
- lang: eng
text: "We present a generative model of images that explicitly reasons over the
set\r\nof objects they show. Our model learns a structured latent representation
that\r\nseparates objects from each other and from the background; unlike prior
works,\r\nit explicitly represents the 2D position and depth of each object, as
well as\r\nan embedding of its segmentation mask and appearance. The model can
be trained\r\nfrom images alone in a purely unsupervised fashion without the need
for object\r\nmasks or depth information. Moreover, it always generates complete
objects,\r\neven though a significant fraction of training images contain occlusions.\r\nFinally,
we show that our model can infer decompositions of novel images into\r\ntheir
constituent objects, including accurate prediction of depth ordering and\r\nsegmentation
of occluded parts."
article_number: '2004.00642'
article_processing_charge: No
arxiv: 1
author:
- first_name: Titas
full_name: Anciukevicius, Titas
last_name: Anciukevicius
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Paul M
full_name: Henderson, Paul M
id: 13C09E74-18D9-11E9-8878-32CFE5697425
last_name: Henderson
orcid: 0000-0002-5198-7445
citation:
ama: Anciukevicius T, Lampert C, Henderson PM. Object-centric image generation with
factored depths, locations, and appearances. arXiv.
apa: Anciukevicius, T., Lampert, C., & Henderson, P. M. (n.d.). Object-centric
image generation with factored depths, locations, and appearances. arXiv.
chicago: Anciukevicius, Titas, Christoph Lampert, and Paul M Henderson. “Object-Centric
Image Generation with Factored Depths, Locations, and Appearances.” ArXiv,
n.d.
ieee: T. Anciukevicius, C. Lampert, and P. M. Henderson, “Object-centric image generation
with factored depths, locations, and appearances,” arXiv. .
ista: Anciukevicius T, Lampert C, Henderson PM. Object-centric image generation
with factored depths, locations, and appearances. arXiv, 2004.00642.
mla: Anciukevicius, Titas, et al. “Object-Centric Image Generation with Factored
Depths, Locations, and Appearances.” ArXiv, 2004.00642.
short: T. Anciukevicius, C. Lampert, P.M. Henderson, ArXiv (n.d.).
date_created: 2020-06-29T23:55:23Z
date_published: 2020-04-01T00:00:00Z
date_updated: 2021-01-12T08:16:44Z
day: '01'
ddc:
- '004'
department:
- _id: ChLa
external_id:
arxiv:
- '2004.00642'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2004.00642
month: '04'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Object-centric image generation with factored depths, locations, and appearances
tmp:
image: /images/cc_by_sa.png
legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode
name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC
BY-SA 4.0)
short: CC BY-SA (4.0)
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '8186'
abstract:
- lang: eng
text: "Numerous methods have been proposed for probabilistic generative modelling
of\r\n3D objects. However, none of these is able to produce textured objects,
which\r\nrenders them of limited use for practical tasks. In this work, we present
the\r\nfirst generative model of textured 3D meshes. Training such a model would\r\ntraditionally
require a large dataset of textured meshes, but unfortunately,\r\nexisting datasets
of meshes lack detailed textures. We instead propose a new\r\ntraining methodology
that allows learning from collections of 2D images without\r\nany 3D information.
To do so, we train our model to explain a distribution of\r\nimages by modelling
each image as a 3D foreground object placed in front of a\r\n2D background. Thus,
it learns to generate meshes that when rendered, produce\r\nimages similar to
those in its training set.\r\n A well-known problem when generating meshes with
deep networks is the\r\nemergence of self-intersections, which are problematic
for many use-cases. As a\r\nsecond contribution we therefore introduce a new generation
process for 3D\r\nmeshes that guarantees no self-intersections arise, based on
the physical\r\nintuition that faces should push one another out of the way as
they move.\r\n We conduct extensive experiments on our approach, reporting quantitative
and\r\nqualitative results on both synthetic data and natural images. These show
our\r\nmethod successfully learns to generate plausible and diverse textured 3D\r\nsamples
for five challenging object classes."
article_processing_charge: No
arxiv: 1
author:
- first_name: Paul M
full_name: Henderson, Paul M
id: 13C09E74-18D9-11E9-8878-32CFE5697425
last_name: Henderson
orcid: 0000-0002-5198-7445
- first_name: Vagia
full_name: Tsiminaki, Vagia
last_name: Tsiminaki
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Henderson PM, Tsiminaki V, Lampert C. Leveraging 2D data to learn textured
3D mesh generation. In: Proceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition. IEEE; 2020:7498-7507. doi:10.1109/CVPR42600.2020.00752'
apa: 'Henderson, P. M., Tsiminaki, V., & Lampert, C. (2020). Leveraging 2D data
to learn textured 3D mesh generation. In Proceedings of the IEEE/CVF Conference
on Computer Vision and Pattern Recognition (pp. 7498–7507). Virtual: IEEE.
https://doi.org/10.1109/CVPR42600.2020.00752'
chicago: Henderson, Paul M, Vagia Tsiminaki, and Christoph Lampert. “Leveraging
2D Data to Learn Textured 3D Mesh Generation.” In Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition, 7498–7507. IEEE, 2020.
https://doi.org/10.1109/CVPR42600.2020.00752.
ieee: P. M. Henderson, V. Tsiminaki, and C. Lampert, “Leveraging 2D data to learn
textured 3D mesh generation,” in Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition, Virtual, 2020, pp. 7498–7507.
ista: 'Henderson PM, Tsiminaki V, Lampert C. 2020. Leveraging 2D data to learn textured
3D mesh generation. Proceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition,
7498–7507.'
mla: Henderson, Paul M., et al. “Leveraging 2D Data to Learn Textured 3D Mesh Generation.”
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,
IEEE, 2020, pp. 7498–507, doi:10.1109/CVPR42600.2020.00752.
short: P.M. Henderson, V. Tsiminaki, C. Lampert, in:, Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition, IEEE, 2020, pp. 7498–7507.
conference:
end_date: 2020-06-19
location: Virtual
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2020-06-14
date_created: 2020-07-31T16:53:49Z
date_published: 2020-07-01T00:00:00Z
date_updated: 2023-10-17T07:37:11Z
day: '01'
ddc:
- '004'
department:
- _id: ChLa
doi: 10.1109/CVPR42600.2020.00752
external_id:
arxiv:
- '2004.04180'
file:
- access_level: open_access
content_type: application/pdf
creator: phenders
date_created: 2020-07-31T16:57:12Z
date_updated: 2020-07-31T16:57:12Z
file_id: '8187'
file_name: paper.pdf
file_size: 10262773
relation: main_file
success: 1
file_date_updated: 2020-07-31T16:57:12Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openaccess.thecvf.com/content_CVPR_2020/papers/Henderson_Leveraging_2D_Data_to_Learn_Textured_3D_Mesh_Generation_CVPR_2020_paper.pdf
month: '07'
oa: 1
oa_version: Submitted Version
page: 7498-7507
publication: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
Recognition
publication_identifier:
eisbn:
- '9781728171685'
eissn:
- 2575-7075
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Leveraging 2D data to learn textured 3D mesh generation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '8188'
abstract:
- lang: eng
text: "A natural approach to generative modeling of videos is to represent them
as a composition of moving objects. Recent works model a set of 2D sprites over
a slowly-varying background, but without considering the underlying 3D scene that\r\ngives
rise to them. We instead propose to model a video as the view seen while moving
through a scene with multiple 3D objects and a 3D background. Our model is trained
from monocular videos without any supervision, yet learns to\r\ngenerate coherent
3D scenes containing several moving objects. We conduct detailed experiments on
two datasets, going beyond the visual complexity supported by state-of-the-art
generative approaches. We evaluate our method on\r\ndepth-prediction and 3D object
detection---tasks which cannot be addressed by those earlier works---and show
it out-performs them even on 2D instance segmentation and tracking."
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "This research was supported by the Scientific Service Units (SSU)
of IST Austria through resources\r\nprovided by Scientific Computing (SciComp).
PH is employed part-time by Blackford Analysis, but\r\nthey did not support this
project in any way."
article_processing_charge: No
arxiv: 1
author:
- first_name: Paul M
full_name: Henderson, Paul M
id: 13C09E74-18D9-11E9-8878-32CFE5697425
last_name: Henderson
orcid: 0000-0002-5198-7445
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Henderson PM, Lampert C. Unsupervised object-centric video generation and
decomposition in 3D. In: 34th Conference on Neural Information Processing Systems.
Vol 33. Curran Associates; 2020:3106–3117.'
apa: 'Henderson, P. M., & Lampert, C. (2020). Unsupervised object-centric video
generation and decomposition in 3D. In 34th Conference on Neural Information
Processing Systems (Vol. 33, pp. 3106–3117). Vancouver, Canada: Curran Associates.'
chicago: Henderson, Paul M, and Christoph Lampert. “Unsupervised Object-Centric
Video Generation and Decomposition in 3D.” In 34th Conference on Neural Information
Processing Systems, 33:3106–3117. Curran Associates, 2020.
ieee: P. M. Henderson and C. Lampert, “Unsupervised object-centric video generation
and decomposition in 3D,” in 34th Conference on Neural Information Processing
Systems, Vancouver, Canada, 2020, vol. 33, pp. 3106–3117.
ista: 'Henderson PM, Lampert C. 2020. Unsupervised object-centric video generation
and decomposition in 3D. 34th Conference on Neural Information Processing Systems.
NeurIPS: Neural Information Processing Systems vol. 33, 3106–3117.'
mla: Henderson, Paul M., and Christoph Lampert. “Unsupervised Object-Centric Video
Generation and Decomposition in 3D.” 34th Conference on Neural Information
Processing Systems, vol. 33, Curran Associates, 2020, pp. 3106–3117.
short: P.M. Henderson, C. Lampert, in:, 34th Conference on Neural Information Processing
Systems, Curran Associates, 2020, pp. 3106–3117.
conference:
end_date: 2020-12-12
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2020-12-06
date_created: 2020-07-31T16:59:19Z
date_published: 2020-07-07T00:00:00Z
date_updated: 2023-04-25T09:49:58Z
day: '07'
department:
- _id: ChLa
external_id:
arxiv:
- '2007.06705'
intvolume: ' 33'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2007.06705
month: '07'
oa: 1
oa_version: Preprint
page: 3106–3117
publication: 34th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713829546'
publication_status: published
publisher: Curran Associates
quality_controlled: '1'
status: public
title: Unsupervised object-centric video generation and decomposition in 3D
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 33
year: '2020'
...