SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generation

Peng Zheng, Tao Liu, Zili Yi, Rui Ma

• We introduce SemanticHuman-HD, the first method to achieve semantic disentangled human image synthesis. Notably, SemanticHuman-HD is also the first method to achieve 3D-aware image synthesis at 10242 resolution, benefiting from our proposed 3D-aware super-resolution module.

3D-SSGAN: Lifting 2D Semantics for 3D-Aware Compositional Portrait Synthesis

Ruiqi Liu, Peng Zheng, Ye Wang, Rui Ma

• We propose 3D-SSGAN, a novel framework for 3D-aware compositional portrait image synthesis. First, a simple yet effective depth-guided 2D-to-3D lifting module maps the generated 2D part features and semantics to 3D. Then, a volume renderer with a novel 3D-aware semantic mask renderer is utilized to produce the composed face features and corresponding masks.

TaylorGrid: Towards Fast and High-Quality Implicit Field Learning via Direct Taylor-based Grid Optimization

Renyi Mao, Qingshan Xu, Peng Zheng, Ye Wang, Tieru Wu, Rui Ma

• In this paper, we aim for both fast and high-quality implicit field learning, and propose TaylorGrid, a novel implicit field representation which can be efficiently computed via direct Taylor expansion optimization on 2D or 3D grids.

D-Aug: Enhancing Data Augmentation for Dynamic LiDAR Scenes

Jiaxing Zhao, Peng Zheng, Rui Ma

• We propose D-Aug, a LiDAR data augmentation method tailored for augmenting dynamic scenes. D-Aug extracts objects and inserts them into dynamic scenes, considering the continuity of these objects across consecutive frames.