6 citations · 7 across the 8 of their papers we have counts for
9 papers
UDM-GRPO: Stable and Efficient Group Relative Policy Optimization for Uniform Discrete Diffusion Models
Jiaqi Wang, Haoge Deng, Ting Pan +5
Uniform Discrete Diffusion Model (UDM) has recently emerged as a promising paradigm for discrete generative modeling; however, its integration with reinforcement learning remains l…
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…
Emu3.5: Native Multimodal Models are World Learners
Yufeng Cui, Honghao Chen, Haoge Deng +20
We introduce Emu3.5, a large-scale multimodal world model that natively predicts the next state across vision and language. Emu3.5 is pre-trained end-to-end with a unified next-tok…
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…
OmniGen2: Towards Instruction-Aligned Multimodal Generation
Chenyuan Wu, Pengfei Zheng, Ruiran Yan +19
In this work, we introduce OmniGen2, a versatile and open-source generative model designed to provide a unified solution for diverse generation tasks, including text-to-image, imag…
EVEv2: Improved Baselines for Encoder-Free Vision-Language Models
Haiwen Diao, Xiaotong Li, Yufeng Cui +6
Existing encoder-free vision-language models (VLMs) are rapidly narrowing the performance gap with their encoder-based counterparts, highlighting the promising potential for unifie…
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…
GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and Consistent 3D Generation
Baorui Ma, Haoge Deng, Junsheng Zhou +3
Text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models has shown great promise but still suffers from inconsistent 3D geometric structures (Janus…