Though the advancement of pre-trained large language models unfolds, the exploration of
building a unified model for language and other multimodal data, such as motion, remains …

We study a challenging task, conditional human motion generation, which produces
plausible human motion sequences according to various conditional inputs, such as action …

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 …

Building animatable 3D models is challenging due to the need for 3D scans, laborious
registration, and manual rigging. Recently, differentiable rendering provides a pathway to …

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 …

Understandings of the three-dimensional social behaviors of freely moving large-size
mammals are valuable for both agriculture and life science, yet challenging due to …

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 …

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 …

Creating high-quality articulated 3D models of animals is challenging either via manual
creation or using 3D scanning tools. Therefore, techniques to reconstruct articulated 3D …