Denoising diffusion probabilistic models (DDPMs) employ a sequence of white Gaussian
noise samples to generate an image. In analogy with GANs those noise maps could be …

Reconstructing the 3D articulated shape of an animal from a single in-the-wild image is a
challenging task. We propose LEPARD, a learning-based framework that discovers …

Generative models have been very popular in the recent years for their image generation
capabilities. GAN-based models are highly regarded for their disentangled latent space …

Explicit 3D shape abstraction from a single 2D image is a long-standing problem in
computer vision and graphics. By leveraging a set of primitives to represent the target shape …

Abstract We introduce Posterior Distillation Sampling (PDS) a novel optimization method for
parametric image editing based on diffusion models. Existing optimization-based methods …

Recently, the real topology has been attracting widespread interest in two dimensions (2D).
Here, based on first‐principles calculations and theoretical analysis, we reveal the …

Generating instructional images of human daily actions from an egocentric viewpoint serves
as a key step towards efficient skill transfer. In this paper, we introduce a novel problem …

Can a text-to-image diffusion model be used as a training objective for adapting a GAN
generator to another domain? In this paper, we show that the classifier-free guidance can be …

Text-driven diffusion models have significantly advanced the image editing performance by
using text prompts as inputs. One crucial step in text-driven image editing is to invert the …

Diffusion models have opened the path to a wide range of text-based image editing
frameworks. However, these typically build on the multi-step nature of the diffusion …