IP Library Granted Patent US 12,499,601
Granted Patent B2
US 12,499,601 · App. 17/719,934 · Granted Dec 16, 2025

Generation and simultaneous display of multiple digitally garmented avatars

Inventors: Robert Meyer Davidson (Incline Village, NV); Gil Spencer (Incline Village, NV); Dmitriy Vladlenovich Pinskiy (Woodland Hills, CA); Evan Smyth (La Crescenta, CA)
Assignee: SpreeAI Corporation
G06T13/40B29C64/393B33Y50/02G06T15/205G06T19/20G06T2210/16G06T2215/16G06T2219/2021
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,499,601
App. No.
17/719,934
Granted
Dec 16, 2025
Kind
B2
Abstract

Systems and methods for generating and simultaneously displaying multiple photorealistic digital avatars wearing garments. One specific method embodiment comprises the steps of: receiving a selection of one or more garments from a user; producing an electronic avatar for each of the selected garments; retrieving a plurality of body measurements of the user; generating a multi-dimensional space based upon the plurality of body measurements of the user; generating an interpolation measurement space for each electronic avatar for each of the selected garments, with each avatar wearing a selected garment; interpolating between two avatars to generate a realistic customized electronic avatar of the user wearing one of the garments; adding photorealistic imagery of the user's face and hair to each rendered customized animated garmented avatar to create a complete digital garmented avatar for the user for each of the selected garments; transmitting each complete digital garmented avatar to a user device; and simultaneously displaying on the user device each complete digital garmented avatar.

Claims (40)

1 . A computer-implemented method for generating and simultaneously displaying multiple photorealistic digitized avatars wearing garments on a single user computing device, said method comprising:

receiving a selection of multiple garments from a user;

retrieving multiple body measurements of the user;

producing at least one digitized avatar for each garment of the multiple garments;

generating a set of multiple digitized garmented avatars, with each avatar wearing a selected garment and generally conforming to the body measurements of the user by interpolating, body measurement by body measurement, between multiple digitized avatars with similar measurements as the user with respect to each body measurement of the multiple body measurements of the user;

transmitting the set of multiple digitized garmented avatars to the single user computing device associated with the user; and

causing to be simultaneously displayed on the single user computing device the set of multiple digitized garmented avatars.

2 . The method of claim 1 wherein the multiple digitized avatars are selected based on at least one preselected body measurement.

3 . The method of claim 1 wherein the generating step further comprises adding photorealistic imagery of a face and hair of the user to the multiple digitized garmented avatars.

4 . The method of claim 1 wherein the method further comprises generating three-dimensional (3D) representations of the multiple garments, the 3D representations including physical attributes of garment materials to enable the 3D representations to move and lay on the at least one digitized avatar in a realistic manner.

5 . The method of claim 1 wherein the method further comprises applying a cloth simulation to the multiple garments on the multiple digitized garmented avatars to generate realistic draping and movement characteristics.

6 . The method of claim 1 wherein:

there are multiple users; and

the set of multiple digitized garmented avatars comprises at least one avatar for each combination of garment and user.

7 . The method of claim 1 further comprising the step of inserting at least one of the digitized garmented avatars into a background scene.

8 . The method of claim 7 wherein the background scene comprises a still image.

9 . The method of claim 8 wherein the background scene comprises a video.

10 . The method of claim 9 wherein the video comprises at least one of an augmented reality video and a virtual reality video.

11 . The method of claim 1 , wherein the method further comprises:

determining, based on a skin tone of a check of the user, a skin color for the user; and

applying the skin color to the at least one digitized avatar.

12 . The method of claim 1 , wherein the method further comprises:

providing, via a user interface of the single user computing device, augmented reality (AR) tools for the user to customize the multiple digitized garmented avatars in real time.

13 . The method of claim 12 , wherein the AR tools includes at least one of applying makeup, altering the multiple body measurements, or altering a sizing of the multiple garments.

14 . A system comprising a processor configured to perform:

receive a selection of multiple garments from a user;

retrieve multiple body measurements of the user;

produce at least one digitized avatar for each garment of the multiple garments;

generate a set of multiple digitized garmented avatars, with each avatar wearing a selected garment and generally conforming to the body measurements of the user by interpolating, body measurement by body measurement, between multiple digitized avatars with similar measurements as the user with respect to each body measurement of the multiple body measurements of the user;

transmit the set of multiple digitized garmented avatars to a computing device associated with the user; and

cause to be simultaneously displayed on the computing device the set of multiple digitized garmented avatars.

15 . The system of claim 14 , wherein the multiple digitized avatars are selected based on at least one preselected body measurement.

16 . The system of claim 14 , wherein the processor is further configured to add photorealistic imagery of a face and hair of the user to the multiple digitized garmented avatars.

17 . The system of claim 14 , wherein the processor is further configured to generate three-dimensional (3D) representations of the multiple garments, the 3D representations including physical attributes of garment materials to enable the 3D representations to move and lay on the at least one digitized avatar in a realistic manner.

18 . The system of claim 14 , wherein the processor is further configured to apply a cloth simulation to the multiple garments on the multiple digitized garmented avatars to generate realistic draping and movement characteristics.

19 . The system of claim 14 , wherein the processor is further configured to:

determine, based on a skin tone of a check of the user, a skin color for the user; and

apply the skin color to the at least one digitized avatar.

20 . The system of claim 14 , wherein the processor is further configured to:

provide, via a user interface of the computing device, augmented reality (AR) tools for the user to customize the multiple digitized garmented avatars in real time.

Assignments (2)
CHANGE OF NAME Recorded Nov 29, 2023
From: SPREE3D CORPORATION
To: SPREEAI CORPORATION
Reel/Frame 065714/0419 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 25, 2022
From: DAVIDSON, ROBERT MEYER; SPENCER, GIL; PINSKIY, DMITRIY VLADLENOVICH; SMYTH, EVAN
To: SPREE3D CORPORATION
Reel/Frame 060019/0251 →
Continuity (8)
Continuation In Part 17706420 · Mar 28, 2022
Continuation In Part 17357394 · Jun 24, 2021
Continuation In Part 17231325 · Apr 15, 2021
Continuation In Part 17231325 · Apr 15, 2021
Provisional Application 63175001 · Apr 14, 2021
Provisional Application 63132173 · Dec 30, 2020
Provisional Application 63142294 · Jan 27, 2021
Related Publication 20220237846A1 · Jul 28, 2022
References Cited (85)
US 6466215B1 · Matsuda et al. · 2002 [cited by applicant]
US 6546309B1 · Gazzuolo · 2003 [cited by examiner]
US 6614431B1 · Collodi · 2003 [cited by examiner]
US 6731287B1 · Erdem · 2004 [cited by applicant]
US 9412192B2 · Mandel · 2016 [cited by examiner]
US 10255681B2 · Price et al. · 2019 [cited by applicant]
US 10311508B2 · Reed · 2019 [cited by examiner]
US 10628666B2 · Sareen · 2020 [cited by examiner]
US 10706636B2 · Selvarajan · 2020 [cited by examiner]
US 10872475B2 · Aluru · 2020 [cited by examiner]
US 10936853B1 · Sethi et al. · 2021 [cited by applicant]
US 11087392B2 · Singh · 2021 [cited by examiner]
US 11308445B2 · Singh · 2022 [cited by examiner]
US 20070091085A1 · Wang et al. · 2007 [cited by applicant]
US 20070188502A1 · Bishop · 2007 [cited by applicant]
US 20090066700A1 · Harding et al. · 2009 [cited by applicant]
US 20130066750A1 · Siddique et al. · 2013 [cited by applicant]
US 20130100140A1 · Ye et al. · 2013 [cited by applicant]
US 20130314412A1 · Gravois et al. · 2013 [cited by applicant]
US 20150154691A1 · Curry et al. · 2015 [cited by applicant]
US 20150351477A1 · Stahl · 2015 [cited by examiner]
US 20160163084A1 · Corazza et al. · 2016 [cited by applicant]
US 20160209929A1 · Trisnadi · 2016 [cited by examiner]
US 20160247017A1 · Sareen et al. · 2016 [cited by applicant]
US 20160284018A1 · Adeyoola et al. · 2016 [cited by applicant]
US 20170004657A1 · Zagel et al. · 2017 [cited by applicant]
US 20170080346A1 · Abbas · 2017 [cited by applicant]
US 20180047200A1 · O'Hara et al. · 2018 [cited by applicant]
US 20180197347A1 · Tomizuka · 2018 [cited by applicant]
US 20180240280A1 · Chen · 2018 [cited by examiner]
US 20180240281A1 · Vincelette · 2018 [cited by applicant]
US 20190035149A1 · Chen et al. · 2019 [cited by applicant]
US 20190130649A1 · O'Brien et al. · 2019 [cited by applicant]
US 20190156541A1 · Isgar · 2019 [cited by examiner]
US 20190287301A1 · Colbert · 2019 [cited by applicant]
US 20190371032A1 · Scapel et al. · 2019 [cited by applicant]
US 20200066029A1 · Chen et al. · 2020 [cited by applicant]
US 20200126316A1 · Sharma · 2020 [cited by examiner]
US 20200151807A1 · Zhou · 2020 [cited by examiner]
US 20200234508A1 · Shaburov et al. · 2020 [cited by applicant]
US 20200258280A1 · Park et al. · 2020 [cited by applicant]
US 20200294294A1 · Petriv et al. · 2020 [cited by applicant]
US 20200306640A1 · Kolen et al. · 2020 [cited by applicant]
US 20200320769A1 · Chen et al. · 2020 [cited by applicant]
US 20200334867A1 · Chen et al. · 2020 [cited by applicant]
US 20200346420A1 · Friedrich · 2020 [cited by applicant]
US 20200364533A1 · Sareen · 2020 [cited by examiner]
US 20200402307A1 · Tanwer · 2020 [cited by examiner]
US 20210049811A1 · Fedyukov · 2021 [cited by examiner]
US 20210056767A1 · Oh · 2021 [cited by examiner]
US 20210150187A1 · Karras et al. · 2021 [cited by applicant]
US 20210303919A1 · Niu · 2021 [cited by applicant]
CN 110930500A · 2020 [cited by applicant]
WO 2014161429A1 · 2014 [cited by applicant]
WO 2017029488A2 · 2017 [cited by applicant]
WO 2017143392A1 · 2017 [cited by applicant]
WO 2018089039A1 · 2018 [cited by applicant]
WO 2018154331A1 · 2018 [cited by applicant]
WO 2019050808A1 · 2019 [cited by applicant]
WO 2019164266A1 · 2019 [cited by applicant]
WO 2020038254A1 · 2020 [cited by applicant]
WO 2020104990A1 · 2020 [cited by applicant]
WO 2022221398A1 · 2022 [cited by applicant]
Goes, F. et al., “Garment Refitting for Digital Characters”, SIGGRAPH '20 Talks, Aug. 17, 2020, Virtual Event, USA; 2 pages. ACM ISBN 978-1-4503-7971-7/20/08. https://doi.org/10.1145/3388767.3407348. [cited by applicant]
Burkov, “Neural Head Reenactment with Latent Pose Descriptors”, Procedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020, pp. 13766-13795; Oct. 30, 2020. https://arxiv.org/pdf/2004.12… [cited by applicant]
Deng, “Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning”, Computer Vision Foundation Conference, Procedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition … [cited by applicant]
Tripathy, “ICface: Interpretable and Controllable Face Reenactment Using GANs”, Procedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2020, pp. 3385-3394; Jan. 17, 2020. https://arxiv.or… [cited by applicant]
Huang, “Learning Identity-Invariant Motion Representations for Cross-ID Face Reenactment”, Procedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020, pp. 7084-7092, 2020, open access v… [cited by applicant]
Thies, “Face2Face: Real-Time Face Capture and Reenactment of RGB Videos”, Jul. 29, 2020; abstract of this paper published in 2016 by IEEE at https://ieeexplore.ieee.org/document/7780631. [cited by applicant]
Zhao, “Joint face alignment and segmentation via deep multi-task learning”, published in Multimedia Tools and Applications 78, 13131-13148 (2019), published by Springer Nature, 1 New York Plaza, Suite 4600, New York, NY… [cited by applicant]
Li, “FaceShifter: Towards High Fidelity and Occlusion Aware Face Swapping”, Peking University and Microsoft Research, Sep. 15, 2020. [email protected] and [email protected]; pdf version availab… [cited by applicant]
Nirkin, “FSGAN: Subject Agnostic Face Swapping and Reenactment”, Computer Vision Foundation, ICCV 2019, open access version, Aug. 2019, https://arxiv.org/pdf/1908.05932.pdf ; also published in Proceedings of the IEEE In… [cited by applicant]
Naruniec, “High-Resolution Neural Face Swapping for Visual Effects”, Eurographics Symposium on Rendering 2020, vol. 39 (2020), No. 4. https://studios.disneyresearch.com/wp-content/uploads/2020/06/High-Resolution-Neural-… [cited by applicant]
Wawrzonowski et al. “Mobile devices' GPUs in cloth dynamics simulation”, Proceedings of the Federated Conference on Computer Science and Information Systems, Prague, Czech Republic, 2017, pp. 1283-1290. Retrieved on Feb… [cited by applicant]
International Preliminary Report on Patentability (issued by the USPTO/RO as the designated IPEA, after the filing of an Article 34 Amendment) mailed Oct. 11, 2023 for PCT/US2022/022180 with an international filing date… [cited by applicant]
Lewis et al., “Pose Space Deformation: A Unified Approach to Shape Interpolation and Skeleton-Driven Deformation”, SIGGRAPH 2000, New Orleans, Louisiana, USA, pp. 165-172. [cited by applicant]
Neophytou et al., “Shape and Pose Space Deformation for Subject Specific Animation”, Centre for Vision Speech and Signal Processing (CVSSP), University of Surrey, Guildford, United Kingdom; IEEE Conference Publication, … [cited by applicant]
“Pose space deformation,” article in Wikipedia, downloaded Jul. 29, 2022, 2 pages. [cited by applicant]
Kim et al., “Deep Video Portraits”, ACM Trans. Graph, vol. 37, No. 4, Article 163; published online May 29, 2018, pp. 163:1-163:14; Association for Computing Machinery, U.S.A. https://arxiv.org/pdf/1805.11714.pdf. [cited by applicant]
PCT/US2022/024601 , “International Preliminary Report on Patentability”, PCT Application No. PCT/US2022/024601, Aug. 16, 2024, 33 pages. [cited by applicant]
“Extended European Search Report”, EP Application No. 22746447.6, Dec. 16, 2024, 6 pages. [cited by applicant]
“Extended European Search Report”, EP Application No. 22788638.9, Dec. 13, 2024, 7 pages. [cited by applicant]
“EP Search Report”, EP Application No. 22788848.4, Jan. 20, 2025, 6 pages,. [cited by applicant]
“Foreign Office Action”, EP Application No. 22788848.4, Feb. 6, 2025, 1 page. [cited by applicant]
Lewis, et al., “Vogue: Try-On by StyleGAN Interpolation Optimization”, Cornell University Library, Ithaca NY, Jan. 6, 2021, 15 pages. [cited by applicant]