A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule
on the learned gradients, and back-propagate the score of a diffusion model through the …

Diffusion models have emerged as the best approach for generative modeling of 2D images.
Part of their success is due to the possibility of training them on millions if not billions of …

Diffusion models have recently become the de-facto approach for generative modeling in
the 2D domain. However, extending diffusion models to 3D is challenging, due to the …

Diffusion-based image generators can now produce high-quality and diverse samples, but
their success has yet to fully translate to 3D generation: existing diffusion methods can either …

Recent progress in pre-trained diffusion models and 3D generation have spurred interest in
4D content creation. However, achieving high-fidelity 4D generation with spatial-temporal …

Abstract We present Viewset Diffusion, a diffusion-based generator that outputs 3D objects
while only using multi-view 2D data for supervision. We note that there exists a one-to-one …

Diffusion models currently achieve state-of-the-art performance for both conditional and
unconditional image generation. However, so far, image diffusion models do not support …

The increased demand for 3D data in AR/VR robotics and gaming applications gave rise to
powerful generative pipelines capable of synthesizing high-quality 3D objects. Most of these …

Diffusion-based generative models have achieved promising results recently, but raise an
array of open questions in terms of conceptual understanding, theoretical analysis, algorithm …

In this work, we investigate the problem of creating high-fidelity 3D content from only a single
image. This is inherently challenging: it essentially involves estimating the underlying 3D …