Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and Texture Generation

• URL: http://arxiv.org/abs/2502.14247v1
• Date: Thu, 20 Feb 2025 04:22:30 GMT
• Title: Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and Texture Generation
• Authors: Jiayu Yang, Taizhang Shang, Weixuan Sun, Xibin Song, Ziang Chen, Senbo Wang, Shenzhou Chen, Weizhe Liu, Hongdong Li, Pan Ji,
• Abstract summary: This report presents a comprehensive framework for generating high-quality 3D shapes and textures from diverse input prompts. The framework consists of 3D shape generation and texture generation. This report details the system architecture, experimental results, and potential future directions to improve and expand the framework.
• Score: 58.77520205498394
• License:
• Abstract: This report presents a comprehensive framework for generating high-quality 3D shapes and textures from diverse input prompts, including single images, multi-view images, and text descriptions. The framework consists of 3D shape generation and texture generation. (1). The 3D shape generation pipeline employs a Variational Autoencoder (VAE) to encode implicit 3D geometries into a latent space and a diffusion network to generate latents conditioned on input prompts, with modifications to enhance model capacity. An alternative Artist-Created Mesh (AM) generation approach is also explored, yielding promising results for simpler geometries. (2). Texture generation involves a multi-stage process starting with frontal images generation followed by multi-view images generation, RGB-to-PBR texture conversion, and high-resolution multi-view texture refinement. A consistency scheduler is plugged into every stage, to enforce pixel-wise consistency among multi-view textures during inference, ensuring seamless integration. The pipeline demonstrates effective handling of diverse input formats, leveraging advanced neural architectures and novel methodologies to produce high-quality 3D content. This report details the system architecture, experimental results, and potential future directions to improve and expand the framework. The source code and pretrained weights are released at: \url{https://github.com/Tencent/Tencent-XR-3DGen}.

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