REVIEW 22 cited by
Image and Video Tokenization with Binary Spherical Quantization
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Image and Video Tokenization with Binary Spherical Quantization
abstract
We propose a new transformer-based image and video tokenizer with Binary Spherical Quantization (BSQ). BSQ projects the high-dimensional visual embedding to a lower-dimensional hypersphere and then applies binary quantization. BSQ is (1) parameter-efficient without an explicit codebook, (2) scalable to arbitrary token dimensions, and (3) compact: compressing visual data by up to 100$\times$ with minimal distortion. Our tokenizer uses a transformer encoder and decoder with simple block-wise causal masking to support variable-length videos as input. The resulting BSQ-ViT achieves state-of-the-art visual reconstruction quality on image and video reconstruction benchmarks with 2.4$\times$ throughput compared to the best prior methods. Furthermore, by learning an autoregressive prior for adaptive arithmetic coding, BSQ-ViT achieves comparable results on video compression with state-of-the-art video compression standards. BSQ-ViT also enables masked language models to achieve competitive image synthesis quality to GAN- and diffusion-based methods.
Forward citations
Cited by 22 Pith papers
-
DigitCode: Symbolic Tokenization of Hand Motion by Anatomical Units
DigitCode tokenizes hand motion by anatomical units, showing the token span (bone/finger/hand) matters more than the quantizer family, and reduces symbolic reconstruction error by about three quarters.
-
FlexiSLM: A Dynamic and Controllable Frame Rate Spoken Language Model
FlexiSLM is the first spoken language model supporting dynamic and controllable frame rates on speech input and output, outperforming fixed-rate 7B models at high quality and enabling faster inference at lower rates l...
-
ChannelTok: Efficient Flexible-Length Vision Tokenization
ChannelTok introduces channel-wise tokenization with stochastic tail-dropping to achieve rFID 2.92 on ImageNet at 8.6x faster decoding and 2.1x smaller size than prior flexible tokenizers.
-
ScaleMoGen: Autoregressive Next-Scale Prediction for Human Motion Generation
ScaleMoGen introduces a scale-wise autoregressive framework that quantizes motions into hierarchical discrete tokens and predicts next-scale maps to achieve SOTA FID 0.030 on HumanML3D and text-guided editing.
-
ScaleMoGen: Autoregressive Next-Scale Prediction for Human Motion Generation
ScaleMoGen applies next-scale autoregressive prediction to human motion generation with multi-scale skeletal-temporal bitwise token maps, reporting SOTA FID on HumanML3D.
-
Generative Refinement Networks for Visual Synthesis
Hierarchical Binary Quantization plus global refinement AR yields 0.56 rFID reconstruction and 1.81 gFID class-conditional generation on ImageNet, with competitive T2I/T2V at 2B scale.
-
Modality-Aware and Anatomical Vector-Quantized Autoencoding for Multimodal Brain MRI
NeuroQuant is a modality-aware 3D VQ-VAE that uses dual-stream encoding, a shared anatomical codebook, and FiLM to achieve superior multi-modal brain MRI reconstruction.
-
ViBES: A Conversational Agent with Behaviorally-Intelligent 3D Virtual Body
ViBES introduces a speech-language-behavior model using modality-specific transformer experts that jointly generates dialogue and 3D body actions, showing gains over separate co-speech and text-to-motion baselines on ...
-
PacTure: Efficient PBR Texture Generation on Packed Views with Visual Autoregressive Models
PacTure uses view packing and next-scale autoregressive prediction to generate consistent multi-view PBR textures faster than prior sequential or cross-attention methods.
-
Concept Removal for Frontier Image Generative Models
A transcoder-based in-place replacement of the bottleneck layer enables selective concept removal in modern diffusion and autoregressive image models without degrading output quality.
-
VPG: Visual Prefix Guidance for Autoregressive Image and Video Generation
VPG is a training-free inference-time guidance technique that improves autoregressive image and video generation by contrasting model outputs under generated versus corrupted prefixes to strengthen next-step support f...
-
CPC-VAR:Continual Personalized and Compositional Generation in Visual Autoregressive Models
CPC-VAR adds Gradient-based Concept Neuron Selection for continual single-concept learning and a context-aware multi-branch composition strategy to reduce forgetting and entanglement in VAR-based personalized image ge...
-
What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion
Prior-Aligned AutoEncoders shape latent manifolds with spatial coherence, local continuity, and global semantics to improve latent diffusion, achieving SOTA gFID 1.03 on ImageNet 256x256 with up to 13x faster convergence.
-
Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning
Polaris learns hierarchical concepts via coupled orbital polar embeddings on hyperspheres that separate meaning from structure using tangent projections, exponential maps, and asymmetric objectives, yielding up to 19-...
-
Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning
Polaris separates semantic meaning from hierarchical structure in embeddings via angular geometry and radius on a hypersphere, yielding up to 19-point gains in taxonomy expansion retrieval over baselines.
-
Generative Refinement Networks for Visual Synthesis
GRN uses hierarchical binary quantization and entropy-guided refinement to set new ImageNet records of 0.56 rFID for reconstruction and 1.81 gFID for class-conditional generation while releasing code and models.
-
ELT: Elastic Looped Transformers for Visual Generation
Weight-shared looped transformers trained with intra-loop self-distillation match MaskGIT-class FID/FVD at roughly 4x fewer parameters and support any-time inference across loop counts.
-
ELT: Elastic Looped Transformers for Visual Generation
Elastic Looped Transformers share weights across recurrent blocks and apply intra-loop self-distillation to deliver 4x parameter reduction while matching competitive FID and FVD scores on ImageNet and UCF-101.
-
WindFM: An Open-Source Foundation Model for Zero-Shot Wind Power Forecasting
A wind-specific foundation model, WindFM, uses hierarchical tokenization and autoregressive pre-training on the NREL WIND Toolkit to achieve state-of-the-art zero-shot wind power forecasts.
-
Wireless TokenCom: RL-Based Tokenizer Agreement for Multi-User Wireless Token Communications
Joint tokenizer/codebook selection, subchannel assignment, and beamforming for multi-user video TokenCom is posed as an MDP and solved by DQN for discrete choices and DDPG for beamforming, with simulated gains over H.265.
-
LGQ: Learnable Geometric Quantization for Image Tokenization
LGQ reports better ImageNet reconstruction FID than FSQ/SimVQ using soft-to-hard learnable-codebook quantization, but its abstract's generation and utilization claims are contradicted by the body.
-
Cosmos World Foundation Model Platform for Physical AI
The Cosmos platform supplies open-source pre-trained world models and supporting tools for building fine-tunable digital world simulations to train Physical AI.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.