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REPARO: Compositional 3D Assets Generation with Differentiable 3D Layout Alignment

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arxiv 2405.18525 v2 pith:ADZGMUWD submitted 2024-05-28 cs.CV

Haonan Han , Rui Yang , Huan Liao , Jiankai Xing , Zunnan Xu , Xiaoming Yu , Junwei Zha , Xiu Li
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Wanhua Li
This is my paper
classification cs.CV
keywords reparoscenedifferentiablegenerationlayoutaddressapproachassets
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Traditional image-to-3D models often struggle with scenes containing multiple objects due to biases and occlusion complexities. To address this challenge, we present REPARO, a novel approach for compositional 3D asset generation from single images. REPARO employs a two-step process: first, it extracts individual objects from the scene and reconstructs their 3D meshes using off-the-shelf image-to-3D models; then, it optimizes the layout of these meshes through differentiable rendering techniques, ensuring coherent scene composition. By integrating optimal transport-based long-range appearance loss term and high-level semantic loss term in the differentiable rendering, REPARO can effectively recover the layout of 3D assets. The proposed method can significantly enhance object independence, detail accuracy, and overall scene coherence. Extensive evaluation of multi-object scenes demonstrates that our REPARO offers a comprehensive approach to address the complexities of multi-object 3D scene generation from single images.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BANG: Dividing 3D Assets via Generative Exploded Dynamics

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    A single color photo becomes a 3D room scene by generating each object with a depth-conditioned diffusion model, then optimizing object poses against estimated depth.

  3. PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers

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    PartCrafter generates several separable 3D part meshes at once from a single image by fine-tuning a pretrained 3D diffusion transformer with part identity tokens and local-global attention.

  4. DanceTogether! Identity-Preserving Multi-Person Interactive Video Generation

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  5. Sel3DCraft: Interactive Visual Prompts for User-Friendly Text-to-3D Generation

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