collaborators

5 papers

cs.GR2026

Reality Check: How Avatar and Face Representation Affect the Perceptual Evaluation of Synthesized Gestures

Haoyang Du, Yinghan Xu, John Dingliana +3

The capacity to create realistic virtual humans has progressed significantly, and such characters can be found in many applications across entertainment, education and health. As a…

cs.GR2026

SplatBus: A Gaussian Splatting Viewer Framework via GPU Interprocess Communication

Yinghan Xu, Théo Morales, John Dingliana

Radiance field-based rendering methods have attracted significant interest from the computer vision and computer graphics communities. They enable high-fidelity rendering with comp…

cs.CV2026

LayerGS: Decomposition and Inpainting of Layered 3D Human Avatars via 2D Gaussian Splatting

Yinghan Xu, John Dingliana

We propose a novel framework for decomposing arbitrarily posed humans into animatable multi-layered 3D human avatars, separating the body and garments. Conventional single-layer re…

cs.CV2025

CrowdSplat: Exploring Gaussian Splatting For Crowd Rendering

Xiaohan Sun, Yinghan Xu, John Dingliana +1

We present CrowdSplat, a novel approach that leverages 3D Gaussian Splatting for real-time, high-quality crowd rendering. Our method utilizes 3D Gaussian functions to represent ani…

cs.CV2025

Evaluating CrowdSplat: Perceived Level of Detail for Gaussian Crowds

Xiaohan Sun, Yinghan Xu, John Dingliana +1

Efficient and realistic crowd rendering is an important element of many real-time graphics applications such as Virtual Reality (VR) and games. To this end, Levels of Detail (LOD)…