InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation

2026-05-13

AI Summary

This work addresses the severe degradation in text legibility and facial details caused by coarse-grained downsampling and quantization in standard tokenizers for discrete autoregressive image generation. To mitigate this, the authors propose a content-aware, region-sensitive reconstruction loss that explicitly optimizes the fidelity of text and face regions during tokenizer training. This approach introduces, for the first time in discrete visual tokenizers, semantic-region-specific supervision signals. Combined with a compact 16k codebook and a 16× downsampling ratio, it enables fine-grained and adaptive reconstruction. Experiments demonstrate that the proposed tokenizer significantly outperforms existing methods in reconstructing text and facial regions, and successfully transfers to the InsightAR generative model, enhancing textual clarity and facial realism while preserving strong general reconstruction performance.

Abstract

Text and faces are among the most perceptually salient and practically important patterns in visual generation, yet they remain challenging for autoregressive generators built on discrete tokenization. A central bottleneck is the tokenizer: aggressive downsampling and quantization often discard the fine-grained structures needed to preserve readable glyphs and distinctive facial features. We attribute this gap to standard discrete-tokenizer objectives being weakly aligned with text legibility and facial fidelity, as these objectives typically optimize generic reconstruction while compressing diverse content uniformly. To address this, we propose InsightTok, a simple yet effective discrete visual tokenization framework that enhances text and face fidelity through localized, content-aware perceptual losses. With a compact 16k codebook and a 16x downsampling rate, InsightTok significantly outperforms prior tokenizers in text and face reconstruction without compromising general reconstruction quality. These gains consistently transfer to autoregressive image generation in InsightAR, producing images with clearer text and more faithful facial details. Overall, our results highlight the potential of specialized supervision in tokenizer training for advancing discrete image generation.

Problem

• text fidelity
• face fidelity
• discrete tokenization
• autoregressive image generation
• visual tokenization

Innovation

• discrete tokenization
• text fidelity
• face fidelity
• perceptual loss
• autoregressive image generation