VoT: Vision-of-Thought for Unified Multimodal Representation Alignment
Current text-to-image systems typically employ a "text encoder plus diffusion decoder" paradigm, in which text semantics directly modulate continuous latent noise. Despite their success, these methods lack an explicit, interpretable intermediate representation that effectively bridges high-level linguistic semantics and low-level visual signals. In this paper, we propose Vision-of-Thought (VoT), a framework that introduces a discrete visual-thinking layer between vision-language models (VLMs) and diffusion transformers (DiTs). Instead of treating VLMs merely as text encoders, we use them as multimodal planners that generate discrete VoT tokens representing high-level visual plans, such as...
| Authors | Jingxiang Sun, Chao Liao, Zhengxiong Luo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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