TY - GEN
T1 - CLIPascene
T2 - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
AU - Vinker, Yael
AU - Alaluf, Yuval
AU - Cohen-Or, Daniel
AU - Shamir, Ariel
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we present a method for converting a given scene image into a sketch using different types and multiple levels of abstraction. We distinguish between two types of abstraction. The first considers the fidelity of the sketch, varying its representation from a more precise portrayal of the input to a looser depiction. The second is defined by the visual simplicity of the sketch, moving from a detailed depiction to a sparse sketch. Using an explicit disentanglement into two abstraction axes - and multiple levels for each one - provides users additional control over selecting the desired sketch based on their personal goals and preferences. To form a sketch at a given level of fidelity and simplification, we train two MLP networks. The first network learns the desired placement of strokes, while the second network learns to gradually remove strokes from the sketch without harming its recognizability and semantics. Our approach is able to generate sketches of complex scenes including those with complex backgrounds (e.g. natural and urban settings) and subjects (e.g. animals and people) while depicting gradual abstractions of the input scene in terms of fidelity and simplicity. https://clipascene.github.io/CLIPascene/
AB - In this paper, we present a method for converting a given scene image into a sketch using different types and multiple levels of abstraction. We distinguish between two types of abstraction. The first considers the fidelity of the sketch, varying its representation from a more precise portrayal of the input to a looser depiction. The second is defined by the visual simplicity of the sketch, moving from a detailed depiction to a sparse sketch. Using an explicit disentanglement into two abstraction axes - and multiple levels for each one - provides users additional control over selecting the desired sketch based on their personal goals and preferences. To form a sketch at a given level of fidelity and simplification, we train two MLP networks. The first network learns the desired placement of strokes, while the second network learns to gradually remove strokes from the sketch without harming its recognizability and semantics. Our approach is able to generate sketches of complex scenes including those with complex backgrounds (e.g. natural and urban settings) and subjects (e.g. animals and people) while depicting gradual abstractions of the input scene in terms of fidelity and simplicity. https://clipascene.github.io/CLIPascene/
UR - http://www.scopus.com/inward/record.url?scp=85178924340&partnerID=8YFLogxK
U2 - 10.1109/ICCV51070.2023.00383
DO - 10.1109/ICCV51070.2023.00383
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:85178924340
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 4123
EP - 4133
BT - Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 2 October 2023 through 6 October 2023
ER -