1 citations · 1 across the 17 of their papers we have counts for
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UniVOE: A Removal-Driven Framework for Interaction-Aware Universal Video Object Editing
Shuzhou Yang, Lanqing Guo, Lingen Li +4
Video object removal and injection are reciprocal editing operations between object-present and object-absent video states, yet they differ substantially in training difficulty. Re…
Think, Plan, Paint: Layout-Aware Reasoning for Controllable Image Generation in Unified Models
Junhao Liu, Jian-Wei Zhang, Tao Huang +3
Unified Multimodal Large Language Models (MLLMs) offer a promising paradigm for unifying visual understanding and generation, yet they still struggle to follow complex spatial inst…
Rosetta: Composable Native Multimodal Pretraining
Xiangyue Liu, Zijian Zhang, Miles Yang +3
Achieving true artificial general intelligence requires foundation models capable of integrating new modalities without forgetting prior knowledge. However, accommodating continuou…
TMP: Tree-structured Mixed-policy Pruning for Large-scale Image Generation and Editing
Peizhen Zhang, Yang Li, Xunsong Li +10
Modern image generation model rapidly grows their sizes to meet high-fidelity image synthesis. However, they gradually become unaffordable for their enormous parameter consumption…
CrossFlow: One-Step Generation Across Latent and Pixel Spaces
Xiyuan Wang, Xiao Zhang, Yang Li +4
Most diffusion and flow-matching generators define the prior, probability path, and prediction target in the same representation space. Latent diffusion improves efficiency by movi…
HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers
Guozhen Zhang, Xuerui Qiu, Yutao Cui +11
Holistic visual tokenizers are fundamental to unified multimodal models (UMMs) as they map diverse visual inputs into a unified representation space. In this paper, we present HYDR…
AdaMem: Learning What to Remember with Adaptive Memory Policies for Personalized Agents
Xingyu Chen, Rui Wang, Zhaopeng Tu +1
Long-term memory systems allow LLM agents to preserve information beyond a single context window, but most systems focus on storing and retrieving facts after extraction, leaving t…
Baton: Explicit Semantic Blueprints for Joint Video-Audio Generation
Shuyuan Tu, Qi Tian, Zihan Yang +9
Current open-source diffusion models struggle to generate stable and synchronized audio-visual content, particularly in scenarios demanding complex semantic reasoning. The root cau…
Precise: SDE-Consistent Stochastic Sampling for RL Post-Training of Flow-Matching Models
Jade Zou, Tao Huang, Weijie Kong +7
Reinforcement learning (RL) has become an effective way to improve prompt alignment and perceptual quality in diffusion and flow-matching generators. A critical step for applying o…
SOAR: Self-Correction for Optimal Alignment and Refinement in Diffusion Models
You Qin, Linqing Wang, Hao Fei +4
The post-training pipeline for diffusion models currently has two stages: supervised fine-tuning (SFT) on curated data and reinforcement learning (RL) with reward models. A fundame…
OmniWeaving: Towards Unified Video Generation with Free-form Composition and Reasoning
Kaihang Pan, Qi Tian, Jianwei Zhang +12
While proprietary systems such as Seedance-2.0 have achieved remarkable success in omni-capable video generation, the academic research community lags far behind: most of its model…
Symbiotic-MoE: Unlocking the Synergy between Generation and Understanding
Xiangyue Liu, Zijian Zhang, Miles Yang +3
Empowering Large Multimodal Models (LMMs) with image generation often leads to catastrophic forgetting in understanding tasks due to severe gradient conflicts. While existing parad…
UniCom: Unified Multimodal Modeling via Compressed Continuous Semantic Representations
Yaqi Zhao, Wang Lin, Zijian Zhang +5
Current unified multimodal models typically rely on discrete visual tokenizers to bridge the modality gap. However, discretization inevitably discards fine-grained semantic informa…