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Found 10 papers, 7 papers with code
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.
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)
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.
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.
CVPR 2023 Text Guided Video Editing Competition
In this paper we present a retrospective on the competition and describe the winning method.
Symbolic Replay: Scene Graph as Prompt for Continual Learning on VQA Task
However, CL on VQA involves not only the expansion of label sets (new Answer sets).
Label-Efficient Online Continual Object Detection in Streaming Video
Remarkably, with only 25% annotated video frames, our method still outperforms the base CL learners, which are trained with 100% annotations on all video frames.
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.
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.
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.
