26 citations · 33 across the 17 of their papers we have counts for
9 papers · 1 filter
LayerEdit: Disentangled Multi-Object Editing via Conflict-Aware Multi-Layer Learning
Fengyi Fu, Mengqi Huang, Lei Zhang +1
Text-driven multi-object image editing which aims to precisely modify multiple objects within an image based on text descriptions, has recently attracted considerable interest. Exi…
MoGA: Mixture-of-Groups Attention for End-to-End Long Video Generation
Weinan Jia, Yuning Lu, Mengqi Huang +6
Long video generation with Diffusion Transformers (DiTs) is bottlenecked by the quadratic scaling of full attention with sequence length. Since attention is highly redundant, outpu…
LongAnimation: Long Animation Generation with Dynamic Global-Local Memory
Nan Chen, Mengqi Huang, Yihao Meng +1
Animation colorization is a crucial part of real animation industry production. Long animation colorization has high labor costs. Therefore, automated long animation colorization b…
Pro3D-Editor : A Progressive-Views Perspective for Consistent and Precise 3D Editing
Yang Zheng, Mengqi Huang, Nan Chen +1
Text-guided 3D editing aims to precisely edit semantically relevant local 3D regions, which has significant potential for various practical applications ranging from 3D games to fi…
HDGlyph: A Hierarchical Disentangled Glyph-Based Framework for Long-Tail Text Rendering in Diffusion Models
Shuhan Zhuang, Mengqi Huang, Fengyi Fu +3
Visual text rendering, which aims to accurately integrate specified textual content within generated images, is critical for various applications such as commercial design. Despite…
DualReal: Adaptive Joint Training for Lossless Identity-Motion Fusion in Video Customization
Wenchuan Wang, Mengqi Huang, Yijing Tu +1
Customized text-to-video generation with pre-trained large-scale models has recently garnered significant attention by focusing on identity and motion consistency. Existing works t…
DiT: Dynamic Diffusion Transformer for Accurate Image Generation
Weinan Jia, Mengqi Huang, Nan Chen +2
Diffusion models are widely recognized for their ability to generate high-fidelity images. Despite the excellent performance and scalability of the Diffusion Transformer (DiT) arch…
Less-to-More Generalization: Unlocking More Controllability by In-Context Generation
Shaojin Wu, Mengqi Huang, Wenxu Wu +3
Although subject-driven generation has been extensively explored in image generation due to its wide applications, it still has challenges in data scalability and subject expansibi…
RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models
Yijing Lin, Mengqi Huang, Shuhan Zhuang +1
Unifying diverse image generation tasks within a single framework remains a fundamental challenge in visual generation. While large language models (LLMs) achieve unification throu…