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<p>Despite the significant potential of 3D object editing to impact various industries, recent research in 3D generation and editing has primarily focused on converting text and images into 3D models, often paying limited attention to the need for <i>fine-grained control</i> over existing 3D objects. This paper introduces a framework that uses a pre-trained regressor to enable continuous and attribute-specific modifications of both the stylistic and geometric attributes of 3D vehicle models. Here, “fine-grained control” refers to the ability to adjust specific geometric or stylistic attributes (such as roof length or perceived luxury) in a continuous and independent manner. Our method aims to preserve the identity of vehicle 3D objects and support multi-attribute editing, allowing for extensive customization while maintaining the model’s structural integrity. The framework leverages DeepSDF to obtain latent representations suitable for continuous attribute editing. Experimental results demonstrate the effectiveness of our approach in achieving detailed, controlled edits on a variety of vehicle 3D models. The code is released at <a href="https://github.com/JiangDong-miao/Vehicle_LatentEdit">https://github.com/JiangDong-miao/Vehicle_LatentEdit</a>.</p>

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Fine-grained 3D vehicle shape manipulation via latent space editing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月8日

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  • JiangDong Miao,
  • Tatsuya Ikeda,
  • Bisser Raytchev,
  • Ryota Mizoguchi,
  • Takenori Hiraoka,
  • Takuji Nakashima,
  • Keigo Shimizu,
  • Toru Higaki,
  • Kazufumi Kaneda

摘要

Despite the significant potential of 3D object editing to impact various industries, recent research in 3D generation and editing has primarily focused on converting text and images into 3D models, often paying limited attention to the need for fine-grained control over existing 3D objects. This paper introduces a framework that uses a pre-trained regressor to enable continuous and attribute-specific modifications of both the stylistic and geometric attributes of 3D vehicle models. Here, “fine-grained control” refers to the ability to adjust specific geometric or stylistic attributes (such as roof length or perceived luxury) in a continuous and independent manner. Our method aims to preserve the identity of vehicle 3D objects and support multi-attribute editing, allowing for extensive customization while maintaining the model’s structural integrity. The framework leverages DeepSDF to obtain latent representations suitable for continuous attribute editing. Experimental results demonstrate the effectiveness of our approach in achieving detailed, controlled edits on a variety of vehicle 3D models. The code is released at https://github.com/JiangDong-miao/Vehicle_LatentEdit.