11 papers
FA-LAM: Focus-Aware Large Avatar Model for One-Shot 4D Animatable Gaussian Head
Yingdong Hu, Yisheng He, Yiming Jiang +3
We propose FA-LAM, a Focus-Aware Large Avatar Model for one-shot animatable Gaussian head creation, while simultaneously enabling static 3D and dynamic 4D full-head recovery. The c…
Large Depth Completion Model from Sparse Observations
Zhu Yu, Zhengyi Zhao, Runmin Zhang +7
This work presents the Large Depth Completion Model (LDCM), a simple, effective, and robust framework for single-view metric depth estimation with sparse observations. Without rely…
MeshLAM: Feed-Forward One-Shot Animatable Textured Mesh Avatar Reconstruction
Yisheng He, Steven Hoi
We introduce MeshLAM, a feed-forward framework for one-shot animatable mesh head reconstruction that generates high-fidelity, animatable 3D head avatars from a single image. Unlike…
ViSA: 3D-Aware Video Shading for Real-Time Upper-Body Avatar Creation
Fan Yang, Heyuan Li, Peihao Li +9
Generating high-fidelity upper-body 3D avatars from one-shot input image remains a significant challenge. Current 3D avatar generation methods, which rely on large reconstruction m…
PanoLAM: Large Avatar Model for Gaussian Full-Head Synthesis from One-shot Unposed Image
Peng Li, Yisheng He, Yingdong Hu +7
We present a feed-forward framework for Gaussian full-head synthesis from a single unposed image. Unlike previous work that relies on time-consuming GAN inversion and test-time opt…
Forge4D: Feed-Forward 4D Human Reconstruction and Interpolation from Uncalibrated Sparse-view Videos
Yingdong Hu, Yisheng He, Jinnan Chen +6
Instant reconstruction of dynamic 3D humans from uncalibrated sparse-view videos is critical for numerous downstream applications. Existing methods, however, are either limited by…