1 citations · 2 across the 17 of their papers we have counts for
18 papers
Chameleon: Style-Content Disentangled Framework for Cross-Domain Object Compositing
Sukhun Ko, Soo Ye Kim, Jeahun Sung +1
Image compositing aims to seamlessly insert a foreground object into a background image, and recent advances in diffusion models have significantly enhanced the quality, especially…
MotionGrounder: Grounded Multi-Object Motion Transfer via Diffusion Transformer
Samuel Teodoro, Yun Chen, Agus Gunawan +3
Motion transfer enables controllable video generation by transferring temporal dynamics from a reference video to synthesize a new video conditioned on a target caption. However, e…
LightMover: Generative Light Movement with Color and Intensity Controls
Gengze Zhou, Tianyu Wang, Soo Ye Kim +7
We present LightMover, a framework for controllable light manipulation in single images that leverages video diffusion priors to produce physically plausible illumination changes w…
Tri-Prompting: Video Diffusion with Unified Control over Scene, Subject, and Motion
Zhenghong Zhou, Xiaohang Zhan, Zhiqin Chen +8
Recent video diffusion models achieve strong visual quality and temporal coherence, but still lack coordinated control over scene layout, customized subject identity, and camera/su…
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…
Generative Video Propagation
Shaoteng Liu, Tianyu Wang, Jui-Hsien Wang +8
Large-scale video generation models have the inherent ability to realistically model natural scenes. In this paper, we demonstrate that through a careful design of a generative vid…
UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics
Xi Chen, Zhifei Zhang, He Zhang +10
We introduce UniReal, a unified framework designed to address various image generation and editing tasks. Existing solutions often vary by tasks, yet share fundamental principles:…
MetaShadow: Object-Centered Shadow Detection, Removal, and Synthesis
Tianyu Wang, Jianming Zhang, Haitian Zheng +7
Shadows are often under-considered or even ignored in image editing applications, limiting the realism of the edited results. In this paper, we introduce MetaShadow, a three-in-one…
Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic Alignment
Yizhi Song, Liu He, Zhifei Zhang +8
Personalized image generation has emerged from the recent advancements in generative models. However, these generated personalized images often suffer from localized artifacts such…
Generative Image Layer Decomposition with Visual Effects
Jinrui Yang, Qing Liu, Yijun Li +7
Recent advancements in large generative models, particularly diffusion-based methods, have significantly enhanced the capabilities of image editing. However, achieving precise cont…
Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation and Reconstruction
Yuanhao Cai, He Zhang, Kai Zhang +12
Existing feedforward image-to-3D methods mainly rely on 2D multi-view diffusion models that cannot guarantee 3D consistency. These methods easily collapse when changing the prompt…
Thinking Outside the BBox: Unconstrained Generative Object Compositing
Gemma Canet Tarrés, Zhe Lin, Zhifei Zhang +6
Compositing an object into an image involves multiple non-trivial sub-tasks such as object placement and scaling, color/lighting harmonization, viewpoint/geometry adjustment, and s…
IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representation
Yizhi Song, Zhifei Zhang, Zhe Lin +7
Generative object compositing emerges as a promising new avenue for compositional image editing. However, the requirement of object identity preservation poses a significant challe…