Search Results for author:
Found 14 papers, 11 papers with code
Follow-Your-Click: Open-domain Regional Image Animation via Short Prompts
Despite recent advances in image-to-video generation, better controllability and local animation are less explored.
Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Aligners
Thus, instead of training the giant models from scratch, we propose to bridge the existing strong models with a shared latent representation space.
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.
MagicStick: Controllable Video Editing via Control Handle Transformations
Yet succinct, our method is the first method to show the ability of video property editing from the pre-trained text-to-image model.
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)
FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling
With the availability of large-scale video datasets and the advances of diffusion models, text-driven video generation has achieved substantial progress.
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.
Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videos
Generating text-editable and pose-controllable character videos have an imperious demand in creating various digital human.
Latent Video Diffusion Models for High-Fidelity Long Video Generation
Diffusion models have shown remarkable results recently but require significant computational resources.
Ranked #2 on
Video Generation
on Taichi
Interpreting Class Conditional GANs with Channel Awareness
To describe such a phenomenon, we propose channel awareness, which quantitatively characterizes how a single channel contributes to the final synthesis.
Unsupervised Portrait Shadow Removal via Generative Priors
Qualitative and quantitative experiments on a real-world portrait shadow dataset demonstrate that our approach achieves comparable performance with supervised shadow removal methods.
