Editable Multi-View Differential Rendering and Single-View Neural Human Rendering

PhD Qualifying Examination


Title: "Editable Multi-View Differential Rendering and Single-View Neural Human
Rendering"

by

Mr. Xiangjun GAO


Abstract:

3D reconstruction and rendering seek to recover photorealistic 3D 
representations from 2D imagery, providing a fundamental basis for a wide 
range of applications, including virtual reality, gaming, and digital content 
creation. Despite the success of traditional multi-view geometry pipelines, 
recent trends have shifted towards neural 3D representations and single-view 
reconstruction driven by generative models. However, existing methods still 
face significant limitations: explicit representations like 3D Gaussian 
Splatting lack flexible manipulation capabilities, while generative 
reconstruction often suffers from severe texture inconsistencies and 
hallucinations.

In this thesis, we explore the evolution of 3D vision techniques, from 
editable multi-view reconstruction to single-view reconstruction with 
generative models. More specifically, we address challenges in two aspects: 
1) manipulable photo-realistic rendering for explicit representations, and 2) 
texture-consistent novel view synthesis for single-view human rendering. We 
specifically introduce two novel methods we have proposed, Mani-GS and 
ConTex-Human. Mani-GS achieves competitive performance against 
state-of-the-art by enabling physical manipulation through a 
triangle-shape-aware 3DGS-Mesh binding method. ConTex-Human achieves 
state-of-the-art performance by ensuring texture consistency through 
depth-guided back view synthesis and visibility-aware patch consistency 
regularization.


Date:                   Monday, 15 December 2025

Time:                   3:00pm - 5:00pm

Venue:                  Room 2128A
                        Lift 19

Committee Members:      Prof. Long Quan (Supervisor, Chairperson)
                        Dr. Dan Xu
                        Dr. Long Chen