We propose STAG4D, a novel framework for high-quality 4D generation, integrating pre-trained diffusion models with dynamic 3D Gaussian splatting. Our method outperforms prior 4D generation works in rendering quality, spatial-temporal consistency, and generation robustness, setting a new state-of-the-art for 4D generation from diverse inputs, including text, image, and video.
arXiv 2024    Project Page    Code

We introduce a method for animating human images, using the SMPL 3D human parametric model within a latent diffusion framework to improve shape alignment and motion guidance. By incorporating various maps and skeleton-based guidance, we enrich the model with detailed 3D shape and pose attributes, fusing them via a multi-layer motion fusion module with self-attention mechanisms.
arXiv 2024    Project Page    Code

We present a novel differentiable point-based rendering framework for material and lighting decomposition from multi-view images, enabling editing, ray-tracing, and real-time relighting of the 3D point cloud. Our framework showcases the potential to revolutionize the mesh-based graphics pipeline with a relightable, traceable, and editable rendering pipeline solely based on point cloud.
arXiv 2024    Project Page   Code

Author's picture

Hao Zhu

CITE Lab - 3DV Group, Nanjing University
E-mail: zhuhaoese@nju.edu.cn


Associate Researcher


Nanjing, China