Semantic One-Dimensional Tokenizer for Image Reconstruction and Generation
Visual generative models based on latent space have achieved great success, underscoring the significance of visual tokenization. Mapping images to latents boosts efficiency and enables multimodal alignment for scaling up in downstream tasks. Existing visual tokenizers primarily map images into fixed 2D spatial grids and focus on pixel-level restoration, which hinders the capture of representations with compact global semantics. To address these issues, we propose \textbf{SemTok}, a semantic one-dimensional tokenizer that compresses 2D images into 1D discrete tokens with high-level semantics. SemTok sets a new state-of-the-art in image reconstruction, achieving superior fidelity with a re...
| Authors | Yunpeng Qu, Kaidong Zhang, Yukang Ding et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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