QuadTok introduces a quadtree-based visual tokenizer that replaces traditional 2D grids or 1D token sequences with a hierarchical structure, allowing dynamic allocation of tokens to visually complex regions while using coarser resolution for homogeneous areas. The tokenizer, trained on ImageNet, reduces token usage by about 10% compared to a fixed 256-token grid and retains comparable reconstruction fidelity. Leveraging the tree’s natural causality, a 947 M GPT-style model conditioned on a quadtree topology achieves a 2.08 gFID on ImageNet 256×256 and supports zero‑shot spatially controlled image generation.
arXiv:2609.36756v1 Announce Type: cross Abstract: One-dimensional (1D) variable-length visual tokenizers enable adaptive compression by varying the number of tokens, allowing downstream autoregressiv...
VTBench is a new benchmark that evaluates visual tokenizers (VTs) used in autoregressive image generation. It assesses VTs on image reconstruction, detail preservation, and text preservation across diverse scenarios, revealing that continuous VAEs outperform discrete VTs in maintaining spatial structure and semantic detail. The study also explores GPT‑4o’s potential autoregressive behavior and releases the benchmark publicly to encourage development of robust, open‑source VTs.
The paper introduces MTAR, a training framework for autoregressive image generation that enhances performance through multi-token prediction, token-level contrastive regularization, and semantic dropping. These components address sparse supervision, improve representation discriminability, and accelerate training without affecting inference. On ImageNet, MTAR outperforms LlamaGen with lower FID and faster training, achieving comparable results in only a third of the iterations.
One-dimensional (1D) variable-length visual tokenizers enable adaptive compression by varying the number of tokens, allowing downstream autoregressive (AR) models to flexibly trade off generation qual...
arXiv:2605. 26089v2 Announce Type: replace-cross Abstract: We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens.
arXiv:2606. 05552v1 Announce Type: new Abstract: Despite progress in image tokenization, standard methods encode redundant information by mixing all granularities within each token, thus redundancy persists between tokens.
arXiv:2610.09242v1 Announce Type: new Abstract: Representation Autoencoders (RAEs) generate images from pre-trained visual fea- tures, but their dense token grids make generative modeling expensive....
VideoTok4D introduces a 4D‑aware video tokenizer that transforms videos into compact tokens representing a dynamic 3D world. It employs spatiotemporal disentanglement to separate static and dynamic content, a track‑aware dynamic attention mechanism for motion consistency across views, and a diffusion prior (Co4DGen) for efficient 4D scene generation. Experiments show state‑of‑the‑art results with up to four orders of magnitude less storage than dense 4D representations, and shorter diffusion sequences for faster generation.
BudgetPix introduces a compute‑adaptive tokenization framework for pixel‑space image diffusion, dynamically allocating computational resources based on visual complexity and spatial layout. It comprises an adaptive encoder using an entropy‑guided quadtree and multi‑scale patch embedder, a scale‑aware decoder, and a flexible training schedule that allows pixel‑space denoisers to operate across variable token counts. The method integrates with existing diffusion architectures, achieving comparable fidelity to larger models while using only a fraction of the compute budget, and demonstrates improved quality‑efficiency tradeoffs in both text‑to‑image and class‑conditional generation.
The paper introduces SGMA, a structure‑guided masked autoencoding framework designed for ultra‑high‑resolution scientific images. SGMA combines a content‑adaptive quadtree tokenizer that reduces gigapixel images to a fixed‑length sequence with a structure‑conditioned masking process that focuses reconstruction on spatially informative regions. The method, enhanced by Damped Accumulation to stabilize multi‑scale signals, achieves superior performance over standard MAE baselines on electron microscopy, whole‑slide optical microscopy, and X‑ray CT datasets, delivering significant accuracy gains and up to a 24.8× inference speedup.
arXiv:2610.13181v1 Announce Type: new Abstract: Efficient visual tokenization requires balancing representation compactness with the preservation of fine-grained image content. Conventional grid-base...
arXiv:2607. 25527v1 Announce Type: cross Abstract: Unifying visual understanding and generation in one model holds immense promise, but remains challenging and expensive due to heavy compute and data demands and conflicts between the visual features needed for these two capabilities.