Discovering 3D Parts from Image Collections

Abstract

Figure 1: Discovering 3D parts form single-view image collections. Our method (LPD) enables self-supervised 3D part discovery while learning to reconstruct object shapes from single-view images. Compared to other methods using different part constraints, LPD discovers more faithful and consistent parts, which improve the reconstruction quality and allow part reasoning/manipulation.

Primitive-Based 3D Human-Object Interaction Modelling and Programming

“…Considering these problems, primitive-based models are proposed (Yao et al 2021;Kluger et al 2021;Yavartanoo et al 2021;Paschalidou et al 2021;Han et al 2021;He et al 2021;Yao et al 2022Yao et al , 2023b. Most use rendered 3D models as input (Yao et al 2021;Yavartanoo et al 2021;Paschalidou et al 2021;Han et al 2021), but few use realis-tic photos (Kluger et al 2021;He et al 2021;Yao et al 2022Yao et al , 2023b, among which (Yao et al 2022(Yao et al , 2023b are most similar to ours. However, they only focus on one animal in one image case, and the input images need to be carefully curated or pre-processed.…”