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Found 14 papers, 12 papers with code
Noise Calibration: Plug-and-play Content-Preserving Video Enhancement using Pre-trained Video Diffusion Models
In order to improve the quality of synthesized videos, currently, one predominant method involves retraining an expert diffusion model and then implementing a noising-denoising process for refinement.
Make a Cheap Scaling: A Self-Cascade Diffusion Model for Higher-Resolution Adaptation
Diffusion models have proven to be highly effective in image and video generation; however, they still face composition challenges when generating images of varying sizes due to single-scale training data.
VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models
Based on this stronger coupling, we shift the distribution to higher quality without motion degradation by finetuning spatial modules with high-quality images, resulting in a generic high-quality video model.
Ranked #1 on
Text-to-Video Generation
on EvalCrafter Text-to-Video (ECTV) Dataset
(using extra training data)
StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style Adapter
To address these challenges, we introduce StyleCrafter, a generic method that enhances pre-trained T2V models with a style control adapter, enabling video generation in any style by providing a reference image.
VideoCrafter1: Open Diffusion Models for High-Quality Video Generation
The I2V model is designed to produce videos that strictly adhere to the content of the provided reference image, preserving its content, structure, and style.
Ranked #3 on
Text-to-Video Generation
on EvalCrafter Text-to-Video (ECTV) Dataset
(using extra training data)
DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors
Animating a still image offers an engaging visual experience.
EvalCrafter: Benchmarking and Evaluating Large Video Generation Models
For video generation, various open-sourced models and public-available services have been developed to generate high-quality videos.
ScaleCrafter: Tuning-free Higher-Resolution Visual Generation with Diffusion Models
Our work also suggests that a pre-trained diffusion model trained on low-resolution images can be directly used for high-resolution visual generation without further tuning, which may provide insights for future research on ultra-high-resolution image and video synthesis.
Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation
For the first module, we leverage an off-the-shelf video retrieval system and extract video depths as motion structure.
Make-Your-Video: Customized Video Generation Using Textual and Structural Guidance
Our method, dubbed Make-Your-Video, involves joint-conditional video generation using a Latent Diffusion Model that is pre-trained for still image synthesis and then promoted for video generation with the introduction of temporal modules.
TaleCrafter: Interactive Story Visualization with Multiple Characters
Accurate Story visualization requires several necessary elements, such as identity consistency across frames, the alignment between plain text and visual content, and a reasonable layout of objects in images.
BSNet: Lane Detection via Draw B-spline Curves Nearby
The locality of lane representation is the ability to modify lanes locally which can simplify parameter optimization.
Delving Deep Into Many-to-Many Attention for Few-Shot Video Object Segmentation
The key is to model the relationship between the query videos and the support images for propagating the object information.
Reciprocal Transformations for Unsupervised Video Object Segmentation
Additionally, to exclude the information of the moving background objects from motion features, our transformation module enables to reciprocally transform the appearance features to enhance the motion features, so as to focus on the moving objects with salient appearance while removing the co-moving outliers.
Ranked #12 on
Unsupervised Video Object Segmentation
on DAVIS 2016 val
