Search Results for author:
Found 8 papers, 6 papers with code
MC$^2$: Multi-concept Guidance for Customized Multi-concept Generation
Customized text-to-image generation aims to synthesize instantiations of user-specified concepts and has achieved unprecedented progress in handling individual concept.
VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion Models
Different from conventional T2V sampling (i. e., temporal and spatial modeling), VideoElevator explicitly decomposes each sampling step into temporal motion refining and spatial quality elevating.
Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models
In this work, we introduce a novel Referring Diffusional segmentor (Ref-Diff) for this task, which leverages the fine-grained multi-modal information from generative models.
VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization
In this paper, we propose a VQGAN-based framework (i. e., VQ-Font) to enhance glyph fidelity through token prior refinement and structure-aware enhancement.
ControlVideo: Training-free Controllable Text-to-Video Generation
Text-driven diffusion models have unlocked unprecedented abilities in image generation, whereas their video counterpart still lags behind due to the excessive training cost of temporal modeling.
Associating Spatially-Consistent Grouping with Text-supervised Semantic Segmentation
In this work, we investigate performing semantic segmentation solely through the training on image-sentence pairs.
ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation
In addition to the unprecedented ability in imaginary creation, large text-to-image models are expected to take customized concepts in image generation.
Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks
In this paper, we present a novel one-shot generative domain adaption method, i. e., DiFa, for diverse generation and faithful adaptation.
