Inexpensive and accessible sensors are accelerating data acquisition in animal ecology.
These technologies hold great potential for large-scale ecological understanding, but are …

Highlights•Deep neural networks are shattering performance benchmarks in computer
vision for various tasks.•Using modern deep learning approaches (DNNs) in the lab is a …

Abstract We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an
object given just a single RGB image. To perform novel view synthesis in this …

Abstract We introduce Gaussian Articulated Template Model (GART) an explicit efficient and
expressive representation for non-rigid articulated subject capturing and rendering from …

Prior work for articulated 3D shape reconstruction often relies on specialized multi-view and
depth sensors or pre-built deformable 3D models. Such methods do not scale to diverse sets …

Quantitative behavioral measurements are important for answering questions across
scientific disciplines—from neuroscience to ecology. State-of-the-art deep-learning methods …

Extracting behavioral measurements non-invasively from video is stymied by the fact that it is
a hard computational problem. Recent advances in deep learning have tremendously …

Differently from 2D image datasets such as COCO, largescale human datasets with 3D
ground-truth annotations are very difficult to obtain in the wild. In this paper, we address this …

Many image-based perception tasks can be formulated as detecting, associating and
tracking semantic keypoints, eg, human body pose estimation and tracking. In this work, we …

We present a learning framework that learns to recover the 3D shape, pose and texture from
a single image, trained on an image collection without any ground truth 3D shape, multi …