Prologue adds a small set of learnable tokens trained exclusively with AR cross-entropy loss to decouple generation from reconstruction in autoregressive visual models, yielding lower gFID on ImageNet 256x256.
Selftok: Discrete visual tokens of autoregression, by diffusion, and for reasoning
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HYDRA-X presents the first unified multimodal model using a single ViT for holistic image-video tokenization, with ablations on attention and compression plus a latent-level editing improvement.
Coarse-to-fine 1D token sequences in autoregressive models enable stronger test-time search and even training-free text-to-image generation guided by verifiers, outperforming traditional 2D grid tokenization.
Shell-LCC models the high-quality data manifold as an isotropic shell to derive cost-free reward signals that improve realism and high-frequency details in text-to-video generation.
citing papers explorer
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Autoregressive Visual Generation Needs a Prologue
Prologue adds a small set of learnable tokens trained exclusively with AR cross-entropy loss to decouple generation from reconstruction in autoregressive visual models, yielding lower gFID on ImageNet 256x256.
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HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers
HYDRA-X presents the first unified multimodal model using a single ViT for holistic image-video tokenization, with ablations on attention and compression plus a latent-level editing improvement.
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(1D) Ordered Tokens Enable Efficient Test-Time Search
Coarse-to-fine 1D token sequences in autoregressive models enable stronger test-time search and even training-free text-to-image generation guided by verifiers, outperforming traditional 2D grid tokenization.
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Your Data Manifold is Secretly a Reward Model: Shell-LCC for Text-to-Video Generation
Shell-LCC models the high-quality data manifold as an isotropic shell to derive cost-free reward signals that improve realism and high-frequency details in text-to-video generation.