3D-GIF: 3D-Controllable Object Generation via Implicit Factorized Representations

• URL: http://arxiv.org/abs/2203.06457v1
• Date: Sat, 12 Mar 2022 15:23:17 GMT
• Title: 3D-GIF: 3D-Controllable Object Generation via Implicit Factorized Representations
• Authors: Minsoo Lee, Chaeyeon Chung, Hojun Cho, Minjung Kim, Sanghun Jung, Jaegul Choo, and Minhyuk Sung
• Abstract summary: We propose the factorized representations which are view-independent and light-disentangled, and training schemes with randomly sampled light conditions. We demonstrate the superiority of our method by visualizing factorized representations, re-lighted images, and albedo-textured meshes. This is the first work that extracts albedo-textured meshes with unposed 2D images without any additional labels or assumptions.
• Score: 31.095503715696722
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While NeRF-based 3D-aware image generation methods enable viewpoint control, limitations still remain to be adopted to various 3D applications. Due to their view-dependent and light-entangled volume representation, the 3D geometry presents unrealistic quality and the color should be re-rendered for every desired viewpoint. To broaden the 3D applicability from 3D-aware image generation to 3D-controllable object generation, we propose the factorized representations which are view-independent and light-disentangled, and training schemes with randomly sampled light conditions. We demonstrate the superiority of our method by visualizing factorized representations, re-lighted images, and albedo-textured meshes. In addition, we show that our approach improves the quality of the generated geometry via visualization and quantitative comparison. To the best of our knowledge, this is the first work that extracts albedo-textured meshes with unposed 2D images without any additional labels or assumptions.

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