# AToken: A Unified Tokenizer for Vision

arXiv: https://arxiv.org/abs/2509.14476 · HTML: https://mlsanity.com/paper/2509.14476/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 139 of its 153 references that are in the map.
- First posted on arXiv 17 Sep 2025.

## Builds on (most cited first, 30 of 139)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)

## Built on by (most cited first, 30 of 30)

- [AlignTok: Aligning Visual Foundation Encoders to Tokenizers for Diffusion Models](https://mlsanity.com/paper/2509.25162/) (2025)
- [From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2509.25373/) (2025)
- [Both Semantics and Reconstruction Matter: Making Representation Encoders Ready for Text-to-Image Generation and Editing](https://mlsanity.com/paper/2512.17909/) (2025)
- [Adapting Self-Supervised Representations as a Latent Space for Efficient Generation](https://mlsanity.com/paper/2510.14630/) (2025)
- [Toward Diffusible High-Dimensional Latent Spaces: A Frequency Perspective](https://mlsanity.com/paper/2511.22249/) (2025)
- [Autoregressive Image Generation with Masked Bit Modeling](https://mlsanity.com/paper/2602.09024/) (2026)
- [EVATok: Adaptive Length Video Tokenization for Efficient Visual Autoregressive Generation](https://mlsanity.com/paper/2603.12267/) (2026)
- [Spherical Leech Quantization for Visual Tokenization and Generation](https://mlsanity.com/paper/2512.14697/) (2025)
- [VideoFlexTok: Flexible-Length Coarse-to-Fine Video Tokenization](https://mlsanity.com/paper/2604.12887/) (2026)
- [iFSQ: Improving FSQ for Image Generation with 1 Line of Code](https://mlsanity.com/paper/2601.17124/) (2026)
- [Unified Multimodal Autoregressive Modeling with Shared Context-Visual Tokenizer is Key to Unification](https://mlsanity.com/paper/2606.18249/) (2026)
- [VTok: A Unified Video Tokenizer with Decoupled Spatial-Temporal Latents](https://mlsanity.com/paper/2602.04202/) (2026)
- [Adaptive 1D Video Diffusion Autoencoder](https://mlsanity.com/paper/2602.04220/) (2026)
- [MUSE: Resolving Manifold Misalignment in Visual Tokenization via Topological Orthogonality](https://mlsanity.com/paper/2605.05646/) (2026)
- [V-RAE: Rethinking Video Latent Spaces for Generation](https://mlsanity.com/paper/2608.13556/) (2026)
- [Compression Tells Intelligence: Visual Coding, Visual Token Technology, and the Unification](https://mlsanity.com/paper/2601.20742/) (2026)
- [HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers](https://mlsanity.com/paper/2606.13289/) (2026)
- [AIA: Rethinking Architecture Decoupling Strategy In Unified Multimodal Model](https://mlsanity.com/paper/2511.22663/) (2025)
- [VLIC: Vision-Language Models As Perceptual Judges for Human-Aligned Image Compression](https://mlsanity.com/paper/2512.15701/) (2025)
- [VibeToken: Scaling 1D Image Tokenizers and Autoregressive Models for Dynamic Resolution Generations](https://mlsanity.com/paper/2604.24885/) (2026)
- [ViTok-v2: Scaling Native Resolution Auto-Encoders to 5 Billion Parameters](https://mlsanity.com/paper/2605.05331/) (2026)
- [ARM: An AutoRegressive Large Multimodal Model with Unified Discrete Representations](https://mlsanity.com/paper/2606.11188/) (2026)
- [FlashDecoder: Real-Time Latent-to-Pixel Streaming Decoder with Transformers](https://mlsanity.com/paper/2607.14898/) (2026)
- [MergeTok: Unified Continuous and Discrete Visual Tokenization via Token Merging](https://mlsanity.com/paper/2605.30904/) (2026)
- [Test-Time Registers as Global Priors for Tokenized Image Generation](https://mlsanity.com/paper/2607.16824/) (2026)
- [Expressivity In Multimodal Contrastive Learning](https://mlsanity.com/paper/2608.17203/) (2026)
- [Studying Image Tokenizers as Visual Languages in Unified Multimodal Models](https://mlsanity.com/paper/2609.09143/) (2026)
- [A Latent Distribution Perspective on Evaluating and Improving Latent Generative Models](https://mlsanity.com/paper/2609.24088/) (2026)
- [NesTok: Nested Self-Aligned 1D Tokenizer for Autoregressive Image Generation](https://mlsanity.com/paper/2609.36756/) (2026)
- [DC-SAE: Deep Compression Semantic Autoencoder for Faster Diffusion Convergence](https://mlsanity.com/paper/2609.39222/) (2026)

## Abstract (arXiv)

We present AToken, the first unified visual tokenizer that achieves both high-fidelity reconstruction and semantic understanding across images, videos, and 3D assets. Unlike existing tokenizers that specialize in either reconstruction or understanding for single modalities, AToken encodes these diverse visual inputs into a shared 4D latent space, unifying both tasks and modalities in a single framework. Specifically, we introduce a pure transformer architecture with 4D rotary position embeddings to process visual inputs of arbitrary resolutions and temporal durations. To ensure stable training, we introduce an adversarial-free training objective that combines perceptual and Gram matrix losses, achieving state-of-the-art reconstruction quality. By employing a progressive training curriculum, AToken gradually expands from single images, videos, and 3D, and supports both continuous and discrete latent tokens. AToken achieves 0.21 rFID with 82.2% ImageNet accuracy for images, 3.01 rFVD with 40.2% MSRVTT retrieval for videos, and 28.28 PSNR with 90.9% classification accuracy for 3D.. In downstream applications, AToken enables both visual generation tasks (e.g., image generation with continuous and discrete tokens, text-to-video generation, image-to-3D synthesis) and understanding tasks (e.g., multimodal LLMs), achieving competitive performance across all benchmarks. These results shed light on the next-generation multimodal AI systems built upon unified visual tokenization.