Source Themes

We propose a novel egocentric whole-body motion capture method that solves the fisheye distortion with FisheyeViT and ensures the temporal coherence with the diffusion-based uncertainty-aware motion refinement.

We present UnrealEgo, i.e., a new large-scale naturalistic dataset for egocentric 3D human pose estimation.

We present a new method for egocentric global 3D body pose estimation using a single head-mounted fisheye camera.

To address the shortage of in-the-wild data, we collect a large-scale in-the-wild egocentric dataset called Egocentric Poses in the Wild (EgoPW).

In this paper, we propose an end-to-end learning strategy to generate textures of human bodies under the supervision of person re-identification.

We leverage classification techniques with space transformations and kernel tricks as established in the realm of machine learning, and present a counterexample-guided method named NIL for synthesizing polynomial interpolants, thereby yielding a unified framework tackling the interpolation problem for the general quantifier-free theory of nonlinear arithmetic, possibly involving transcendental functions.

We define a novel network algorithm programming language (NAPL) that enhances the SDN framework with a rapid programming flow from topology-based network models to C++ implementations, thus bridging the gap between the limited capability of existing SDN APIs and the reality of practical network management.