  For visual content generation, discrepancies between user intentions and the
generated content have been a longstanding problem. This discrepancy arises
from two main factors. First, user intentions are inherently complex, with
subtle details not fully captured by input prompts. The absence of such details
makes it challenging for generative models to accurately reflect the intended
meaning, leading to a mismatch between the desired and generated output.
Second, generative models trained on visual-label pairs lack the comprehensive
knowledge to accurately represent all aspects of the input data in their
generated outputs. To address these challenges, we propose a knowledge-enhanced
iterative refinement framework for visual content generation. We begin by
analyzing and identifying the key challenges faced by existing generative
models. Then, we introduce various knowledge sources, including human insights,
pre-trained models, logic rules, and world knowledge, which can be leveraged to
address these challenges. Furthermore, we propose a novel visual generation
framework that incorporates a knowledge-based feedback module to iteratively
refine the generation process. This module gradually improves the alignment
between the generated content and user intentions. We demonstrate the efficacy
of the proposed framework through preliminary results, highlighting the
potential of knowledge-enhanced generative models for intention-aligned content
generation.
