We present Stable Video 4D 2.0 (SV4D 2.0), a multi-view video diffusion
model for dynamic 3D asset generation. Compared to its predecessor SV4D,
SV4D 2.0 is more robust to occlusions and large motion, generalizes better
to real-world videos, and produces higher-quality outputs in terms of
detail sharpness and spatio-temporal consistency.
Diffusion models are the main driver of progress in image and video
synthesis, but suffer from slow inference speed. Distillation methods, like
the recently introduced adversarial diffusion distillation (ADD) aim to
shift the model from many-shot to single-step inference, albeit at the cost
of expensive and difficult optimization due to its reliance on a fixed
pretrained DINOv2 discriminator.
We present Stable Virtual Camera, a generalist diffusion model that creates
novel views of a scene, given any number of input views and target cameras.
We study the problem of single-image 3D object reconstruction. Recent works
have diverged into two directions: regression-based modeling and generative
modeling. In this paper, we present SPAR3D, a novel two-stage approach
aiming to take the best of both directions.
We present SF3D, a novel method for rapid and high-quality textured object
mesh reconstruction from a single image in just 0.5 seconds.
We present Stable Video 4D (SV4D), a latent video diffusion model for
multi-frame and multi-view consistent dynamic 3D content generation.
Here we describe the architecture and training process of a new
open-weights text-to-audio model trained with Creative Commons data. Our
evaluation shows that the model’s performance is competitive with the
state-of-the-art across various metrics.
This workshop paper highlights the limitations of generative AI tools in
translating digital creations into the physical world and proposes new
augmentations to generative AI tools for creating physically viable 3D
models.
We present Stable Video 3D (SV3D) — a latent video diffusion model for
high-resolution, image-to-multi-view generation of orbital videos around a
3D object.
In this work, we improve existing noise sampling techniques for training
rectified flow models by biasing them towards perceptually relevant scales.