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Edify 3D: Scalable High-Quality 3D Asset Generation

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arxiv 2411.07135 v1 pith:LXJJ4TXY submitted 2024-11-11 cs.CV cs.AIcs.GR

NVIDIA: Maciej Bala , Yin Cui , Yifan Ding , Yunhao Ge , Zekun Hao , Jon Hasselgren , Jacob Huffman , Jingyi Jin
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J.P. Lewis Zhaoshuo Li Chen-Hsuan Lin Yen-Chen Lin Tsung-Yi Lin Ming-Yu Liu Alice Luo Qianli Ma Jacob Munkberg Stella Shi Fangyin Wei Donglai Xiang Jiashu Xu Xiaohui Zeng Qinsheng Zhang
This is my paper · ORCID
classification cs.CVcs.AIcs.GR
keywords high-qualityassetedifygenerationmaterialsmethodobjectshape
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We introduce Edify 3D, an advanced solution designed for high-quality 3D asset generation. Our method first synthesizes RGB and surface normal images of the described object at multiple viewpoints using a diffusion model. The multi-view observations are then used to reconstruct the shape, texture, and PBR materials of the object. Our method can generate high-quality 3D assets with detailed geometry, clean shape topologies, high-resolution textures, and materials within 2 minutes of runtime.

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Cited by 4 Pith papers

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

  1. G-MAD: A Game-Based Data Generation Framework for Multi-View RGB-T Aerial Object Detection

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Using Arma3, G-MAD automatically generates multi-view RGB-T aerial images with engine-native bounding box annotations, and the AMOD benchmark demonstrates gains in multi-view training and synthetic-to-real transfer.

  2. CustomX: Unified Character, Action, and Scene Customization in Video World Models

    cs.CV 2025-12 conditional novelty 6.0 of 10

    AniX generates controllable videos of a user-supplied character performing typed actions inside a user-supplied 3D scene by fine-tuning a pre-trained video generator on small locomotion datasets.

  3. 3D-Generalist: Self-Improving Vision-Language-Action Models for Crafting 3D Worlds

    cs.GR 2025-07 conditional novelty 6.0 of 10

    A self-improving vision-language-model policy iteratively crafts 3D environments from text, and renderings of those environments serve as effective synthetic pretraining data for vision models.

  4. AI-powered Contextual 3D Environment Generation: A Systematic Review

    cs.GR 2025-06 conditional novelty 4.0 of 10

    A PRISMA-based systematic review of 136 papers finds diffusion models dominate high-quality AI 3D scene generation, with computational cost, data quality, and evaluation metrics as key limitations.

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