title: Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and
    Texture Generation
summary: 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:
  https://github.com/Tencent/Tencent-XR-3DGen.
paper_authors._id: 67be3ebc1c80786468704721
paper_authors.name: Jiayu Yang
paper_authors._id: 67be3ebc1c80786468704722
paper_authors.name: Taizhang Shang
paper_authors._id: 67be3ebc1c80786468704723
paper_authors.name: Weixuan Sun
paper_authors._id: 67be3ebc1c80786468704724
paper_authors.name: Xibin Song
paper_authors._id: 67be3ebc1c80786468704725
paper_authors.name: Ziang Cheng
paper_authors._id: 67be3ebc1c80786468704726
paper_authors.name: Senbo Wang
paper_authors._id: 67be3ebc1c80786468704727
paper_authors.name: Shenzhou Chen
paper_authors._id: 67be3ebc1c80786468704728
paper_authors.name: Weizhe Liu
paper_authors._id: 67be3ebc1c80786468704729
paper_authors.name: Hongdong Li
paper_authors._id: 67be3ebc1c8078646870472a
paper_authors.name: Pan Ji
paper_title: Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and
    Texture Generation
paper_summary: 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:
  https://github.com/Tencent/Tencent-XR-3DGen.
paper_ai_summary: The framework generates high-quality 3D shapes and textures from various inputs using VAE, diffusion networks, and a multi-stage texture generation process with consistency enforcement.
paper_ai_keywords: Variational Autoencoder VAE diffusion network Artist-Created Mesh AM RGB-to-PBR consistency scheduler