The field of visual computing is rapidly advancing due to the emergence of generative
artificial intelligence (AI), which unlocks unprecedented capabilities for the generation …

Abstract 3D reconstruction of deformable (or non‐rigid) scenes from a set of monocular 2D
image observations is a long‐standing and actively researched area of computer vision and …

We consider the problem of predicting the 3D shape, articulation, viewpoint, texture, and
lighting of an articulated animal like a horse given a single test image as input. We present a …

Inverse rendering of an object under entirely unknown capture conditions is a fundamental
challenge in computer vision and graphics. Neural approaches such as NeRF have …

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 address the problem of inferring the 3D shape and pose of dogs from images. Given the
lack of 3D training data, this problem is challenging, and the best methods lag behind those …

Recent advances in neural reconstruction enable high-quality 3D object reconstruction from
casually captured image collections. Current techniques mostly analyze their progress on …

Accurately estimating the 3D pose and shape is an essential step towards understanding
animal behavior, and can potentially benefit many downstream applications, such as wildlife …

We present Farm3D, a method for learning category-specific 3D reconstructors for
articulated objects, relying solely on “free” virtual supervision from a pre-trained 2D diffusion …

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 …