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Found 14 papers, 9 papers with code
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.
Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions
This work presents Moonshot, a new video generation model that conditions simultaneously on multimodal inputs of image and text.
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.
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)
Dataset Condensation via Generative Model
Dataset condensation aims to condense a large dataset with a lot of training samples into a small set.
Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks
Despite the rapid advancement of unsupervised learning in visual representation, it requires training on large-scale datasets that demand costly data collection, and pose additional challenges due to concerns regarding data privacy.
Too Large; Data Reduction for Vision-Language Pre-Training
Specifically, TL;DR can compress the mainstream VLP datasets at a high ratio, e. g., reduce well-cleaned CC3M dataset from 2. 82M to 0. 67M ($\sim$24\%) and noisy YFCC15M from 15M to 2. 5M ($\sim$16. 7\%).
Making Vision Transformers Efficient from A Token Sparsification View
In this work, we propose a novel Semantic Token ViT (STViT), for efficient global and local vision transformers, which can also be revised to serve as backbone for downstream tasks.
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.
DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes
In this paper, we present DeVRF, a novel representation to accelerate learning dynamic radiance fields.
Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition
Learning spatial-temporal relation among multiple actors is crucial for group activity recognition.
MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning
With such multi-dimension and multi-scale factorization, our MorphMLP block can achieve a great accuracy-computation balance.
Ranked #38 on
Action Recognition
on Something-Something V2
(using extra training data)
