Have we unified image generation and understanding yet? An empirical
  study of GPT-4o's image generation ability
OpenAI's multimodal GPT-4o has demonstrated remarkable capabilities in image
generation and editing, yet its ability to achieve world knowledge-informed
semantic synthesis--seamlessly integrating domain knowledge, contextual
reasoning, and instruction adherence--remains unproven. In this study, we
systematically evaluate these capabilities across three critical dimensions:
(1) Global Instruction Adherence, (2) Fine-Grained Editing Precision, and (3)
Post-Generation Reasoning. While existing benchmarks highlight GPT-4o's strong
capabilities in image generation and editing, our evaluation reveals GPT-4o's
persistent limitations: the model frequently defaults to literal
interpretations of instructions, inconsistently applies knowledge constraints,
and struggles with conditional reasoning tasks. These findings challenge
prevailing assumptions about GPT-4o's unified understanding and generation
capabilities, exposing significant gaps in its dynamic knowledge integration.
Our study calls for the development of more robust benchmarks and training
strategies that go beyond surface-level alignment, emphasizing context-aware
and reasoning-grounded multimodal generation.
67fdc0c50c63732d9e0b139a
Ning Li
67fdc0c50c63732d9e0b139b
Jingran Zhang
admin_assigned
2025-04-15T08:25:03.508Z
67fdc24ad2c9d1369d390f01
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/51JiReMgHiFHZiJ9OVdaf.png
Jingran Zhang
user
zhangjingran
67fdc0c50c63732d9e0b139c
Justin Cui
admin_assigned
2025-04-15T08:24:57.129Z
65862671e878be571bf9fc52
/avatars/b2a1b939f3112b476e7641e0c5fd2dc7.svg
cuijiaxing
user
cuijiaxing
Have we unified image generation and understanding yet? An empirical
  study of GPT-4o's image generation ability
OpenAI's multimodal GPT-4o has demonstrated remarkable capabilities in image
generation and editing, yet its ability to achieve world knowledge-informed
semantic synthesis--seamlessly integrating domain knowledge, contextual
reasoning, and instruction adherence--remains unproven. In this study, we
systematically evaluate these capabilities across three critical dimensions:
(1) Global Instruction Adherence, (2) Fine-Grained Editing Precision, and (3)
Post-Generation Reasoning. While existing benchmarks highlight GPT-4o's strong
capabilities in image generation and editing, our evaluation reveals GPT-4o's
persistent limitations: the model frequently defaults to literal
interpretations of instructions, inconsistently applies knowledge constraints,
and struggles with conditional reasoning tasks. These findings challenge
prevailing assumptions about GPT-4o's unified understanding and generation
capabilities, exposing significant gaps in its dynamic knowledge integration.
Our study calls for the development of more robust benchmarks and training
strategies that go beyond surface-level alignment, emphasizing context-aware
and reasoning-grounded multimodal generation.
Evaluation of GPT-4o shows limitations in its ability to integrate world knowledge, apply contextual reasoning, and adhere to complex instructions in image generation and editing.
multimodal GPT-4o semantic synthesis global instruction adherence fine-grained editing precision post-generation reasoning knowledge constraints conditional reasoning context-aware generation reasoning-grounded generation