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pals-ttic/sjc

默认分支 main · commit f63c40de · 扫描时间 2026/5/30 18:13:05

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AI 可见性总分
33 /100
亟需修复
品类召回
0 / 2
在所有问题中均未被推荐
规则结果
通过 2 · 警告 0 · 失败 0
客观元数据检查
AI 认识你的名字
2 / 3
直接询问时,AI 是否点名你的仓库
如何阅读这份报告

行动计划告诉你下一步要做什么——按影响力排序、可直接复制粘贴的修改。品类可见性是真正的 GEO 测试:当用户向 AI 提一个不带品牌、本应让 pals-ttic/sjc 浮出水面的问题时,AI 是真的推荐了你,还是推荐了你的竞品?客观检查验证 AI 引擎最先权衡的那些元数据信号。自指检查判断 AI 是否还认识你的名字。

行动计划 — 可复制粘贴的修复

3 条由 gemini-2.5-flash 生成、按优先级排序的修改。修完后请把对应条目标记为完成。

整体方向
  • highreadme#1
    Reposition the README's opening to explicitly state its 3D generation purpose

    原因:

    当前
    # Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation
    
    Haochen Wang*,
    Xiaodan Du*,
    Jiahao Li*,
    Raymond A. Yeh†,
    Greg Shakhnarovich
    (* indicates equal contribution)
    
    TTI-Chicago, †Purdue University
    
    Abstract: *A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule on the learned gradients, and back-propagate the score of a diffusion model through the Jacobian of a differentiable renderer, which we instantiate to be a voxel radiance field. This setup aggregates 2D scores at multiple camera viewpoints into a 3D score, and repurposes a pretrained 2D model for 3D data generation. We identify a technical challenge of distribution mismatch that arises in this application, and propose a novel estimation mechanism to resolve it. We run our algorithm on several off-the-shelf diffusion image generative models, including the recently released Stable Diffusion trained on the large-scale LAION dataset.*
    复制粘贴的修复
    # Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation
    
    This repository provides the official implementation for Score Jacobian Chaining (SJC), a method that repurposes pretrained 2D diffusion models for high-quality 3D object generation.
    
    Haochen Wang*,
    Xiaodan Du*,
    Jiahao Li*,
    Raymond A. Yeh†,
    Greg Shakhnarovich
    (* indicates equal contribution)
    
    TTI-Chicago, †Purdue University
    
    Abstract: *A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule on the learned gradients, and back-propagate the score of a diffusion model through the Jacobian of a differentiable renderer, which we instantiate to be a voxel radiance field. This setup aggregates 2D scores at multiple camera viewpoints into a 3D score, and repurposes a pretrained 2D model for 3D data generation. We identify a technical challenge of distribution mismatch that arises in this application, and propose a novel estimation mechanism to resolve it. We run our algorithm on several off-the-shelf diffusion image generative models, including the recently released Stable Diffusion trained on the large-scale LAION dataset.*
  • mediumtopics#2
    Add more specific topics to align with 3D generation methods

    原因:

    当前
    3d-generation, diffusion-models
    复制粘贴的修复
    3d-generation, diffusion-models, text-to-3d, image-to-3d, nerf, cvpr-2023
  • lowreadme#3
    Clarify the project's license in the README

    原因:

    复制粘贴的修复
    ## License
    This project is licensed under the terms specified in the [LICENSE](LICENSE) file.

本次扫描解析到的品类 GEO 通道:google/gemini-2.5-flash, deepseek/deepseek-v4-flash

品类可见性 — 真正的 GEO 测试

向 google/gemini-2.5-flash 提出的不带品牌问题。AI 推荐了你,还是推荐了别人?

各模型使用同一组问题 — 切换标签对比回答与排名。

召回
0 / 2
0% 的问题里出现了 pals-ttic/sjc
平均排名
越小越好。#1 表示首位推荐。
声量占比
0%
在所有被点名的工具中,你占了多少?
头号对手
DreamFusion
在 2 个问题中被推荐 2 次
竞品排行
  1. DreamFusion · 被推荐 2 次
  2. Fantasia3D · 被推荐 2 次
  3. Imagen · 被推荐 2 次
  4. Stable Diffusion · 被推荐 2 次
  5. MVDream · 被推荐 1 次
  • 品类问题
    How to generate 3D geometry and textures using only pretrained 2D diffusion models?
    你:未被推荐
    AI 推荐顺序:
    1. DreamFusion
    2. MVDream
    3. SyncDreamer
    4. Fantasia3D
    5. Imagen
    6. Stable Diffusion
    7. Zero123
    8. Zero123++
    9. COLMAP
    10. OpenMVG
    11. Shap-E
    12. Point-E
    13. Luma AI's Genie
    14. MiDaS
    15. DPT
    16. Open3D
    17. ControlNet
    18. Meshroom
    19. Metashape
    20. RealityCapture

    AI 推荐了 20 个替代方案,却始终没点名 pals-ttic/sjc。这就是要补上的差距。

    查看 AI 完整回答
  • 品类问题
    Seeking methods for synthesizing 3D objects by adapting existing 2D image generation models.
    你:未被推荐
    AI 推荐顺序:
    1. Stable Diffusion
    2. Imagen
    3. Neural Radiance Field (NeRF)
    4. DreamFusion
    5. Magic3D
    6. Fantasia3D
    7. ProlificDreamer
    8. GET3D
    9. StyleGAN2
    10. StyleGAN3
    11. StyleGAN-NADA
    12. GIRAFFE
    13. pi-GAN
    14. CLIP
    15. CLIP-NeRF
    16. CLIP-Forge
    17. LDM3D
    18. HoloGAN
    19. VoxelGAN

    AI 推荐了 19 个替代方案,却始终没点名 pals-ttic/sjc。这就是要补上的差距。

    查看 AI 完整回答

客观检查

针对 AI 引擎最看重的元数据信号的规则审计。

  • Metadata completeness
    pass

  • README presence
    pass

自指检查

当被直接问到你时,AI 是否还知道你的仓库存在?

  • Compared to common alternatives in this category, what is the core differentiator of pals-ttic/sjc?
    pass
    AI 未点名 pals-ttic/sjc —— 很可能在说另一个项目

    AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?

  • If a team adopts pals-ttic/sjc in production, what risks or prerequisites should they evaluate first?
    pass
    AI 明确点名了 pals-ttic/sjc

    AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?

  • In one sentence, what problem does the repo pals-ttic/sjc solve, and who is the primary audience?
    pass
    AI 明确点名了 pals-ttic/sjc

    AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?

嵌入你的 GEO 徽章

把这个徽章贴进 pals-ttic/sjc 的 README。每次重新扫描都会自动更新,并跳到最新报告——是「我在乎 AI 可发现性」最简单的公开证明。

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订阅 Pro,解锁深度诊断

pals-ttic/sjc — 轻量扫描仍免费;本卡列出 Pro 相对轻量的深度额度。

  • 深度报告每月 10 次
  • 无品牌品类查询5,轻量 2
  • 优先行动项8,轻量 3