DEsignBench: Exploring and Benchmarking DALL-E 3 for Imagining Visual
  Design
We introduce DEsignBench, a text-to-image (T2I) generation benchmark tailored
for visual design scenarios. Recent T2I models like DALL-E 3 and others, have
demonstrated remarkable capabilities in generating photorealistic images that
align closely with textual inputs. While the allure of creating visually
captivating images is undeniable, our emphasis extends beyond mere aesthetic
pleasure. We aim to investigate the potential of using these powerful models in
authentic design contexts. In pursuit of this goal, we develop DEsignBench,
which incorporates test samples designed to assess T2I models on both "design
technical capability" and "design application scenario." Each of these two
dimensions is supported by a diverse set of specific design categories. We
explore DALL-E 3 together with other leading T2I models on DEsignBench,
resulting in a comprehensive visual gallery for side-by-side comparisons. For
DEsignBench benchmarking, we perform human evaluations on generated images in
DEsignBench gallery, against the criteria of image-text alignment, visual
aesthetic, and design creativity. Our evaluation also considers other
specialized design capabilities, including text rendering, layout composition,
color harmony, 3D design, and medium style. In addition to human evaluations,
we introduce the first automatic image generation evaluator powered by GPT-4V.
This evaluator provides ratings that align well with human judgments, while
being easily replicable and cost-efficient. A high-resolution version is
available at
https://github.com/design-bench/design-bench.github.io/raw/main/designbench.pdf?download=
65373e288cc9d7c02405850c
Kevin Lin
claimed_verified
2023-10-24T07:29:15.288Z
6298fd95b58e71e2ac9f3ad8
/avatars/7d34644d537bc5c17cf1e4ce4095355c.svg
Kevin Lin
user
kevinlin311tw
65373e288cc9d7c02405850d
Zhengyuan Yang
admin_assigned
2023-10-24T08:24:55.698Z
630713411801ecc7d2592a7c
/avatars/fb36f69f03421c3a2a7f72ba0858fa60.svg
Zhengyuan Yang
user
zyang39
65373e288cc9d7c02405850e
Linjie Li
admin_assigned
2023-10-24T08:37:42.242Z
63db16fff03c3d71ef397206
/avatars/bfb7e0d730b7d03302799d5d2828d97d.svg
Linjie Li
user
linjieli222
65373e288cc9d7c02405850f
Jianfeng Wang
admin_assigned
2023-10-24T08:38:03.293Z
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/avatars/0dbe862cde7e4bef25bedf860584d8c1.svg
Jianfeng Wang
user
amsword8
65373e288cc9d7c024058510
Lijuan Wang
admin_assigned
2023-10-24T08:38:16.143Z
6413521d4e5305c14f22e110
/avatars/a6f8d0573e678f79bc3c0b7897b818ce.svg
Lijuan Wang
user
Lijuan
DEsignBench: Exploring and Benchmarking DALL-E 3 for Imagining Visual
  Design
We introduce DEsignBench, a text-to-image (T2I) generation benchmark tailored
for visual design scenarios. Recent T2I models like DALL-E 3 and others, have
demonstrated remarkable capabilities in generating photorealistic images that
align closely with textual inputs. While the allure of creating visually
captivating images is undeniable, our emphasis extends beyond mere aesthetic
pleasure. We aim to investigate the potential of using these powerful models in
authentic design contexts. In pursuit of this goal, we develop DEsignBench,
which incorporates test samples designed to assess T2I models on both "design
technical capability" and "design application scenario." Each of these two
dimensions is supported by a diverse set of specific design categories. We
explore DALL-E 3 together with other leading T2I models on DEsignBench,
resulting in a comprehensive visual gallery for side-by-side comparisons. For
DEsignBench benchmarking, we perform human evaluations on generated images in
DEsignBench gallery, against the criteria of image-text alignment, visual
aesthetic, and design creativity. Our evaluation also considers other
specialized design capabilities, including text rendering, layout composition,
color harmony, 3D design, and medium style. In addition to human evaluations,
we introduce the first automatic image generation evaluator powered by GPT-4V.
This evaluator provides ratings that align well with human judgments, while
being easily replicable and cost-efficient. A high-resolution version is
available at
https://github.com/design-bench/design-bench.github.io/raw/main/designbench.pdf?download=
DEsignBench evaluates text-to-image models in visual design contexts with human and automatic assessments across various design criteria.
text-to-image T2I DEsignBench DALL-E 3 image-text alignment visual aesthetic design creativity text rendering layout composition color harmony 3D design medium style GPT-4V