DeformAvatar: Point-Based Human Avatar Re-targeting and Rendering

In this paper, we present the DeformAvatar, a novel architecture for human avatar re-targetting and rendering based on point clouds. Given the multiple views of a person, we first build a point-model-paired human representation containing a raw point cloud and an optimal parametric model. Then, we repurpose several advanced neural point-based rendering and Gaussian Splatting techniques for 3D avatar modeling. Finally, to enhance photorealistic re-targeting of body shapes and poses, we propose a dual point-adaptive (DPA) regularization based on traditional linear blend skinning. Extensive experiments demonstrate that our DeformAvatar framework can synthesize highly realistic novel views in new shape and pose parameters. We also find that 3DGS-based avatar modeling is superior to others in 3D avatar re-targeting.