# Gaussian Model

GAGAvatar is a Gaussian model-based technology for 3D avatar reconstruction and animation generation. It quickly generates 3D avatars from a single image and supports real-time facial expression animation. The main advantages of this technology include high-fidelity 3D model generation, fast rendering speed, and the ability to generalize to unseen identities. GAGAvatar captures identity and facial details through an innovative dual-improvement method and utilizes global image features and 3D deformable models to control expressions, providing a new benchmark for research and applications in digital avatars.

Long-LRM is a model designed for 3D Gaussian reconstruction, capable of recreating large scenes from a series of input images. The model can process 32 images at a resolution of 960x540 in just 1.3 seconds, operating on a single A100 80G GPU. It integrates the latest Mamba2 modules with traditional transformer modules to enhance efficiency without compromising quality through effective token merging and Gaussian trimming. Unlike traditional feedforward models that can only reconstruct small portions of a scene, Long-LRM can regenerate the entire scene in one go. On large-scale scene datasets like DL3DV-140 and Tanks and Temples, Long-LRM's performance is comparable to optimization-based methods while achieving two orders of magnitude greater efficiency.

Human101 is a framework for quickly reconstructing human figures from a single view. It can train a 3D Gaussian model within 100 seconds and render images at a resolution of 1024 and above 60FPS without pre-saving the Gaussian properties of each frame. The Human101 pipeline is as follows: first, extract 2D human pose from the single-view video. Then, use the pose to drive a 3D simulator to generate corresponding 3D skeletal animation. Finally, construct a time-varying 3D Gaussian model based on the animation for real-time rendering.