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Found 18 papers, 10 papers with code
Long-Context Autoregressive Video Modeling with Next-Frame Prediction
To support exploring efficient long-context video modeling, we first establish a strong autoregressive baseline called Frame AutoRegressive (FAR).
Ranked #3 on
Video Generation
on UCF-101
Edit Transfer: Learning Image Editing via Vision In-Context Relations
We introduce a new setting, Edit Transfer, where a model learns a transformation from just a single source-target example and applies it to a new query image.
ROICtrl: Boosting Instance Control for Visual Generation
Natural language often struggles to accurately associate positional and attribute information with multiple instances, which limits current text-based visual generation models to simpler compositions featuring only a few dominant instances.
EvolveDirector: Approaching Advanced Text-to-Image Generation with Large Vision-Language Models
Our experiments with extensive data indicate that the model trained on generated data of the advanced model can approximate its generation capability.
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
We present a unified transformer, i. e., Show-o, that unifies multimodal understanding and generation.
Ranked #10 on
Text-to-Image Generation
on GenEval
DragAnything: Motion Control for Anything using Entity Representation
We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation.
Towards A Better Metric for Text-to-Video Generation
Experiments on the TVGE dataset demonstrate the superiority of the proposed T2VScore on offering a better metric for text-to-video generation.
MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers
Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control.
MAG-Edit: Localized Image Editing in Complex Scenarios via Mask-Based Attention-Adjusted Guidance
Recent diffusion-based image editing approaches have exhibited impressive editing capabilities in images with simple compositions.
VideoSwap: Customized Video Subject Swapping with Interactive Semantic Point Correspondence
In contrast to previous methods that rely on dense correspondences, we introduce the VideoSwap framework that exploits semantic point correspondences, inspired by our observation that only a small number of semantic points are necessary to align the subject's motion trajectory and modify its shape.
DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and View-Change Human-Centric Video Editing
To overcome this, we propose to introduce the dynamic Neural Radiance Fields (NeRF) as the innovative video representation, where the editing can be performed in the 3D spaces and propagated to the entire video via the deformation field.
MotionDirector: Motion Customization of Text-to-Video Diffusion Models
Given a set of video clips of the same motion concept, the task of Motion Customization is to adapt existing text-to-video diffusion models to generate videos with this motion.
Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation
In this paper, we are the first to propose a hybrid model, dubbed as Show-1, which marries pixel-based and latent-based VDMs for text-to-video generation.
Ranked #2 on
Text-to-Video Generation
on EvalCrafter Text-to-Video (ECTV) Dataset
(using extra training data)
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models
To showcase the power of the proposed approach, we generate datasets with rich dense pixel-wise labels for a wide range of downstream tasks, including semantic segmentation, instance segmentation, and depth estimation.
Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models
Public large-scale text-to-image diffusion models, such as Stable Diffusion, have gained significant attention from the community.
Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
To replicate the success of text-to-image (T2I) generation, recent works employ large-scale video datasets to train a text-to-video (T2V) generator.
Rethinking the Objectives of Vector-Quantized Tokenizers for Image Synthesis
Vector-Quantized (VQ-based) generative models usually consist of two basic components, i. e., VQ tokenizers and generative transformers.
VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder
Equipped with the VQ codebook as a facial detail dictionary and the parallel decoder design, the proposed VQFR can largely enhance the restored quality of facial details while keeping the fidelity to previous methods.
