3 citations · 4 across the 9 of their papers we have counts for
10 papers
AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation
Yuchao Gu, Guian Fang, Yuxin Jiang +4
Few-step video generation has been significantly advanced by consistency distillation. However, the performance of consistency-distilled models often degrades as more sampling step…
Mitty: Diffusion-based Human-to-Robot Video Generation
Yiren Song, Cheng Liu, Weijia Mao +1
Learning directly from human demonstration videos is a key milestone toward scalable and generalizable robot learning. Yet existing methods rely on intermediate representations suc…
The Image as Its Own Reward: Reinforcement Learning with Adversarial Reward for Image Generation
Weijia Mao, Hao Chen, Zhenheng Yang +1
A reliable reward function is essential for reinforcement learning (RL) in image generation. Most current RL approaches depend on pre-trained preference models that output scalar r…
UniRL: Self-Improving Unified Multimodal Models via Supervised and Reinforcement Learning
Weijia Mao, Zhenheng Yang, Mike Zheng Shou
Unified multimodal large language models such as Show-o and Janus have achieved strong performance across both generation and understanding tasks. However, these models typically r…
DoraCycle: Domain-Oriented Adaptation of Unified Generative Model in Multimodal Cycles
Rui Zhao, Weijia Mao, Mike Zheng Shou
Adapting generative models to specific domains presents an effective solution for satisfying specialized requirements. However, adapting to some complex domains remains challenging…
Long-Context Autoregressive Video Modeling with Next-Frame Prediction
Yuchao Gu, Weijia Mao, Mike Zheng Shou
Long-context video modeling is essential for enabling generative models to function as world simulators, as they must maintain temporal coherence over extended time spans. However,…
UniMoD: Efficient Unified Multimodal Transformers with Mixture-of-Depths
Weijia Mao, Zhenheng Yang, Mike Zheng Shou
Unified multimodal transformers, which handle both generation and understanding tasks within a shared parameter space, have received increasing attention in recent research. Althou…
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
Jinheng Xie, Weijia Mao, Zechen Bai +7
We present a unified transformer, i.e., Show-o, that unifies multimodal understanding and generation. Unlike fully autoregressive models, Show-o unifies autoregressive and (discret…
ShowRoom3D: Text to High-Quality 3D Room Generation Using 3D Priors
Weijia Mao, Yan-Pei Cao, Jia-Wei Liu +2
We introduce ShowRoom3D, a three-stage approach for generating high-quality 3D room-scale scenes from texts. Previous methods using 2D diffusion priors to optimize neural radiance…
DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and View-Change Human-Centric Video Editing
Jia-Wei Liu, Yan-Pei Cao, Jay Zhangjie Wu +6
Despite recent progress in diffusion-based video editing, existing methods are limited to short-length videos due to the contradiction between long-range consistency and frame-wise…