We present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D
meshes generation from a single unposed image in the wild using both2D and 3D priors. In …

Abstract We present Scalable Interpolant Transformers (SiT), a family of generative models
built on the backbone of Diffusion Transformers (DiT). The interpolant framework, which …

Aided by text-to-image and text-to-video diffusion models, existing 4D content creation
pipelines utilize score distillation sampling to optimize the entire dynamic 3D scene …

Learning 3D models of all animals in nature requires massively scaling up existing
solutions. With this ultimate goal in mind we develop 3D-Fauna an approach that learns a …

Estimating 3D articulated shapes like animal bodies from monocular images is inherently
challenging due to the ambiguities of camera viewpoint, pose, texture, lighting, etc. We …

We introduce DragAPart, a method that, given an image and a set of drags as input,
generates a new image of the same object that responds to the action of the drags …

Advances in 3D generation have facilitated sequential 3D model generation (aka 4D
generation), yet its application for animatable objects with large motion remains scarce. Our …

We present a method to build animatable dog avatars from monocular videos. This is
challenging as animals display a range of (unpredictable) non-rigid movements and have a …

Data-driven three-dimensional parametric shape models of the human body have gained
enormous popularity both for the analysis of visual data and for the generation of synthetic …

We introduce a new method for learning a generative model of articulated 3D animal
motions from raw, unlabeled online videos. Unlike existing approaches for 3D motion …