6 papers
LINA: Linear Autoregressive Image Generative Models with Continuous Tokens
Jiahao Wang, Ting Pan, Haoge Deng +4
Autoregressive models with continuous tokens form a promising paradigm for visual generation, especially for text-to-image (T2I) synthesis, but they suffer from high computational…
Uniform Discrete Diffusion with Metric Path for Video Generation
Haoge Deng, Ting Pan, Fan Zhang +8
Continuous-space video generation has advanced rapidly, while discrete approaches lag behind due to error accumulation and long-context inconsistency. In this work, we revisit disc…
CI-VID: A Coherent Interleaved Text-Video Dataset
Yiming Ju, Jijin Hu, Zhengxiong Luo +7
Text-to-video (T2V) generation has recently attracted considerable attention, resulting in the development of numerous high-quality datasets that have propelled progress in this ar…
End-to-End Vision Tokenizer Tuning
Wenxuan Wang, Fan Zhang, Yufeng Cui +5
Existing vision tokenization isolates the optimization of vision tokenizers from downstream training, implicitly assuming the visual tokens can generalize well across various tasks…
Autoregressive Video Generation without Vector Quantization
Haoge Deng, Ting Pan, Haiwen Diao +6
This paper presents a novel approach that enables autoregressive video generation with high efficiency. We propose to reformulate the video generation problem as a non-quantized au…
You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale
Baorui Ma, Huachen Gao, Haoge Deng +4
Recent 3D generation models typically rely on limited-scale 3D `gold-labels' or 2D diffusion priors for 3D content creation. However, their performance is upper-bounded by constrai…