Search Results for author:
Found 11 papers, 5 papers with code
Explorative Inbetweening of Time and Space
We introduce bounded generation as a generalized task to control video generation to synthesize arbitrary camera and subject motion based only on a given start and end frame.
HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data
In this paper, we propose HOIDiffusion for generating realistic and diverse 3D hand-object interaction data.
Restoration by Generation with Constrained Priors
We propose a method to adapt a pretrained diffusion model for image restoration by simply adding noise to the input image to be restored and then denoise.
Dolfin: Diffusion Layout Transformers without Autoencoder
In this paper, we introduce a novel generative model, Diffusion Layout Transformers without Autoencoder (Dolfin), which significantly improves the modeling capability with reduced complexity compared to existing methods.
Patched Denoising Diffusion Models For High-Resolution Image Synthesis
Feature collage systematically crops and combines partial features of the neighboring patches to predict the features of a shifted image patch, allowing the seamless generation of the entire image due to the overlap in the patch feature space.
DiffusionRig: Learning Personalized Priors for Facial Appearance Editing
On a high level, DiffusionRig learns to map simplistic renderings of 3D face models to realistic photos of a given person.
MasQCLIP for Open-Vocabulary Universal Image Segmentation
We present a new method for open-vocabulary universal image segmentation, which is capable of performing instance, semantic, and panoptic segmentation under a unified framework.
Point Cloud Recognition with Position-to-Structure Attention Transformers
In this paper, we present Position-to-Structure Attention Transformers (PS-Former), a Transformer-based algorithm for 3D point cloud recognition.
Open-Vocabulary Universal Image Segmentation with MaskCLIP
In this paper, we tackle an emerging computer vision task, open-vocabulary universal image segmentation, that aims to perform semantic/instance/panoptic segmentation (background semantic labeling + foreground instance segmentation) for arbitrary categories of text-based descriptions in inference time.
Guided Variational Autoencoder for Disentanglement Learning
We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning.
