Reconstructing the 3D articulated shape of an animal from a single in-the-wild image is a challenging task.

To tackle this issue with a common benchmark, we introduce the Drunkard's

Thus, they do not effectively capture implicit correspondences between articulated shapes or regularize jittery temporal deformations.

Environment Yellowstone's supervolcano is creating a 19-mile bulge But scientists aren't that worried. Breakthroughs, discoveries, and DIY tips sent six days a week. In Yellowstone National Park, a bulge the size of 279 football fields has risen by an inch since last July. With no signs of slowing down, the bump that's roughly 19 miles across may cause some worry that the iconic locale's hibernating supervolcano is readying for an apocalyptic reawakening. Although impressed by the situation, the Yellowstone Volcano Observatory's scientist-in-charge doesn't sound particularly worried.

Analyzing inter-individual variability of physiological functions is particularly appealing in medical and biological contexts to describe or quantify health conditions. Such analysis can be done by comparing individuals to a reference one with time series as biomedical data.This paper introduces an unsupervised representation learning (URL) algorithm for time series tailored to inter-individual studies. The idea is to represent time series as deformations of a reference time series. The deformations are diffeomorphisms parameterized and learned by our method called TS-LDDMM. Once the deformations and the reference time series are learned, the vector representations of individual time series are given by the parametrization of their corresponding deformation. At the crossroads between URL for time series and shape analysis, the proposed algorithm handles irregularly sampled multivariate time series of variable lengths and provides shape-based representations of temporal data.In this work, we establish a representation theorem for the graph of a time series and derive its consequences on the LDDMM framework. We showcase the advantages of our representation compared to existing methods using synthetic data and real-world examples motivated by biomedical applications.

Recently, Gaussian splatting has received more and more attention in the field of static scene rendering. Due to the low computational overhead and inherent flexibility of explicit representations, plane-based explicit methods are popular ways to predict deformations for Gaussian-based dynamic scene rendering models. However, plane-based methods rely on the inappropriate low-rank assumption and excessively decompose the space-time 4D encoding, resulting in overmuch feature overlap and unsatisfactory rendering quality. To tackle these problems, we propose Grid4D, a dynamic scene rendering model based on Gaussian splatting and employing a novel explicit encoding method for the 4D input through the hash encoding.