1 citations · 2 across the 17 of their papers we have counts for
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Both Semantics and Reconstruction Matter: Making Representation Encoders Ready for Text-to-Image Generation and Editing
Shilong Zhang, He Zhang, Zhifei Zhang +11
Modern Latent Diffusion Models (LDMs) typically operate in low-level Variational Autoencoder (VAE) latent spaces that are primarily optimized for pixel-level reconstruction. To uni…
EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning
Xuan Ju, Tianyu Wang, Yuqian Zhou +11
Recent advances in foundation models highlight a clear trend toward unification and scaling, showing emergent capabilities across diverse domains. While image generation and editin…
OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions
Yuanhao Cai, He Zhang, Xi Chen +11
Existing feedforward subject-driven video customization methods mainly study single-subject scenarios due to the difficulty of constructing multi-subject training data pairs. Anoth…
Frame Guidance: Training-Free Guidance for Frame-Level Control in Video Diffusion Models
Sangwon Jang, Taekyung Ki, Jaehyeong Jo +4
Advancements in diffusion models have significantly improved video quality, directing attention to fine-grained controllability. However, many existing methods depend on fine-tunin…
ObjectMover: Generative Object Movement with Video Prior
Xin Yu, Tianyu Wang, Soo Ye Kim +5
Simple as it seems, moving an object to another location within an image is, in fact, a challenging image-editing task that requires re-harmonizing the lighting, adjusting the pose…
Multitwine: Multi-Object Compositing with Text and Layout Control
Gemma Canet Tarrés, Zhe Lin, Zhifei Zhang +4
We introduce the first generative model capable of simultaneous multi-object compositing, guided by both text and layout. Our model allows for the addition of multiple objects with…