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Vision-as-inverse-graphics, the concept of reconstructing images into editable programs, remains challenging for Vision-Language Models (VLMs), which inherently lack fine-grained spatial grounding in one-shot settings.
Roberts, L.G.: Machine Perception of Three-Dimensional Solids. Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA (1963)
1963
Earlier work this paper cites.
Blanz, V., Vetter, T.: A morphable model for the synthesis of 3d faces. Seminal Graphics Papers: Pushing the Boundaries, Volume 2 (1999), https://api.semanticscholar.org/CorpusID:203705211
1999
Earlier work this paper cites.
Loper, M., Black, M.J.: Opendr: An approximate differentiable renderer. In: European Conference on Computer Vision (2014), https://api.semanticscholar.org/CorpusID:17868098
2014
Earlier work this paper cites.
Kulkarni, T.D., Kohli, P., Tenenbaum, J.B., Mansinghka, V.: Picture: A probabilistic programming language for scene perception. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society (June 2015)
2015
Earlier work this paper cites.
Gulwani, S., Polozov, O., Singh, R.: Program synthesis. Found. Trends Program. Lang. 4
2017
Earlier work this paper cites.
Wu, J., Tenenbaum, J.B., Kohli, P.: Neural scene de-rendering. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Earlier work this paper cites.
Yi, K., Wu, J., Gan, C., Torralba, A., Kohli, P., Tenenbaum, J.: Neural-symbolic vqa: Disentangling reasoning from vision and language understanding. In: Advances in Neural Information Processing Systems (NeurIPS) 31. pp. 1039–1050 (2018)
2018
Earlier work this paper cites.
Liu, S., Li, T., Chen, W., Li, H.: Soft rasterizer: A differentiable renderer for image-based 3d reasoning. 2019 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 7707–7716 (2019), https://api.semanticscholar.org/CorpusID:102484000
2019
Earlier work this paper cites.
Feng, Y., Feng, H., Black, M.J., Bolkart, T.: Learning an animatable detailed 3D face model from in-the-wild images. ACM Transactions on Graphics (ToG), Proc. SIGGRAPH 40
2021
Earlier work this paper cites.
Deitke, M., VanderBilt, E., Herrasti, A., Weihs, L., Salvador, J., Ehsani, K., Han, W., Kolve, E., Farhadi, A., Kembhavi, A., Mottaghi, R.: ProcTHOR: Large-Scale Embodied AI Using Procedural Generation. In: NeurIPS (2022), outstanding Paper Award
2022
Earlier work this paper cites.
Yao, S., Chen, H., Yang, J., Narasimhan, K.: Webshop: Towards scalable real-world web interaction with grounded language agents. Advances in Neural Information Processing Systems 35
2022
Earlier work this paper cites.
Dwedari, M.M., Niessner, M., Chen, Z.: Generating context-aware natural answers for questions in 3d scenes. In: Proceedings of the British Machine Vision Conference (BMVC). BMVA Press (2023)
2023
Earlier work this paper cites.
Feng, W., Zhu, W., Fu, T.j., Jampani, V., Akula, A., He, X., Basu, S., Wang, X.E., Wang, W.Y.: Layoutgpt: compositional visual planning and generation with large language models. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. NIPS ’23 (2023)
2023
Earlier work this paper cites.
Gupta, T., Kembhavi, A.: Visual programming: Compositional visual reasoning without training. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 14953–14962 (2023)
2023
Earlier work this paper cites.
Hong, Y., Zhen, H., Chen, P., Zheng, S., Du, Y., Chen, Z., Gan, C.: 3dllm: Injecting the 3d world into large language models. In: Advances in Neural Information Processing Systems (NeurIPS). vol. 36, pp. 20482–20494. Curran Associates, Inc. (2023)
2023
Earlier work this paper cites.
Hu, Y., Stretcu, O., Lu, C.T., Viswanathan, K., Hata, K., Luo, E., Krishna, R., Fuxman, A.: Visual program distillation: Distilling tools and programmatic reasoning into vision-language models (2023)
2023
Earlier work this paper cites.
Kodnongbua, M., Jones, B., Ahmad, M.B.S., Kim, V., Schulz, A.: Reparamcad: Zero-shot cad re-parameterization for interactive manipulation. In: ACM SIGGRAPH Asia 2023 Conference Papers. ACM, New York, NY, USA (2023). https://doi.org/10.1145/3610548.3618219
2023
Earlier work this paper cites.
Raistrick, A., Lipson, L., Ma, Z., Mei, L., Wang, M., Zuo, Y., Kayan, K., Wen, H., Han, B., Wang, Y., Newell, A., Law, H., Goyal, A., Yang, K., Deng, J.: Infinite photorealistic worlds using procedural generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12630–12641 (2023)
2023
Earlier work this paper cites.
Ritchie, D., Guerrero, P., Jones, R.K., Mitra, N.J., Schulz, A., Willis, K.D.D., Wu, J.: Neurosymbolic models for computer graphics. Computer Graphics Forum 42
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
Surís, D., Menon, S., Vondrick, C.: Vipergpt: Visual inference via python execution for reasoning. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 11888–11898 (2023)
2023
Earlier work this paper cites.
Xue, L., Gao, M., Xing, C., Martín-Martín, R., Wu, J., Xiong, C., Xu, R., Niebles, J.C., Savarese, S.: Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1179–1189. IEEE Computer Society (2023)
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2024
Cited alongside, same era.
Bandyopadhyay, S., Maheshwari, H., Natarajan, A., Saxena, A.: Enhancing presentation slide generation by LLMs with a multi-staged end-to-end approach. In: Mahamood, S., Minh, N.L., Ippolito, D. (eds.) Proceedings of the 17th International Natural Language Generation Conference. pp. 222–229. Association for Computational Linguistics, Tokyo, Japan (Sep 2024). https://doi.org/10.18653/v1/2024.inlg-main.18
2024
Cited alongside, same era.
Chen, B., Xu, Z., Kirmani, S., Ichter, B., Sadigh, D., Guibas, L., Xia, F.: Spatialvlm: Endowing vision-language models with spatial reasoning capabilities. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 14455–14465 (June 2024)
2024
Cited alongside, same era.
Gu, Y., Huang, I., Je, J., Yang, G., Guibas, L.: Blendergym: Benchmarking foundational model systems for graphics editing. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 18574–18583 (2025)
2025
Later among the works it cites.
He, J., Treude, C., Lo, D.: Llm-based multi-agent systems for software engineering: Literature review, vision, and the road ahead. ACM Trans. Softw. Eng. Methodol. 34
2025
Later among the works it cites.
Hong, K., Troynikov, A., Huber, J.: Context rot: How increasing input tokens impacts llm performance. Tech. rep., Chroma (July 2025), https://research.trychroma.com/context-rot
2025
Later among the works it cites.
2025
Later among the works it cites.
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Cheng, A.C., Yin, H., Fu, Y., Guo, Q., Yang, R., Kautz, J., Wang, X., Liu, S.: Spatialrgpt: Grounded spatial reasoning in vision-language models. In: NeurIPS (2024)
2024
Cited alongside, same era.
Ge, J., Subramanian, S., Shi, B., Herzig, R., Darrell, T.: Recursive visual programming. In: European Conference on Computer Vision. pp. 1–18. Springer (2024)
2024
Cited alongside, same era.
Hu, Z., Iscen, A., Jain, A., Kipf, T., Yue, Y., Ross, D.A., Schmid, C., Fathi, A.: Scenecraft: An llm agent for synthesizing 3d scenes as blender code. In: Forty-first International Conference on Machine Learning (2024)
2024
Cited alongside, same era.
Huang, I., Yang, G., Guibas, L.: Blenderalchemy: Editing 3d graphics with vision-language models. In: European Conference on Computer Vision. pp. 297–314. Springer (2024)
2024
Cited alongside, same era.
Koh, J.Y., Lo, R., Jang, L., Duvvur, V., Lim, M.C., Huang, P.Y., Neubig, G., Zhou, S., Salakhutdinov, R., Fried, D.: Visualwebarena: Evaluating multimodal agents on realistic visual web tasks. In: ICLR 2024 Workshop on Large Language Model (LLM) Agents (2024), https://openreview.net/forum?id=RPKxrKTJbj
2024
Cited alongside, same era.
Kulits, P., Feng, H., Liu, W., Abrevaya, V.F., Black, M.J.: Re-thinking inverse graphics with large language models. Transactions on Machine Learning Research (2024)
2024
Cited alongside, same era.
Liu, N.F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., Liang, P.: Lost in the middle: How language models use long contexts. Transactions of the Association for Computational Linguistics 12
2024
Cited alongside, same era.
Meshy: Meshy: Fast 3d generative ai. https://www.meshy.ai/ (2024)
2024
Cited alongside, same era.
Öcal, B.M., Tatarchenko, M., Karaoğlu, S., Gevers, T.: Sceneteller: Language-to-3d scene generation. In: Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LXXXV. pp. 362–378 (2024). https://doi.org/10.1007/978-3-031-73013-9_21
2024
Cited alongside, same era.
OpenAI: Gpt-5 (Aug 2025), https://openai.com/index/introducing-gpt-5/ , released August 7, 2025
2025
Later among the works it cites.
Qin, Y., Xu, Z., Liu, Y.: Apply hierarchical-chain-of-generation to complex attributes text-to-3d generation. 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 18521–18530 (2025), https://api.semanticscholar.org/CorpusID:278481349
2025
Later among the works it cites.
2025
Later among the works it cites.
Sun, C., Han, J., Deng, W., Wang, X., Qin, Z., Gould, S.: 3d-gpt: Procedural 3d modeling with large language models. In: 2025 International Conference on 3D Vision (3DV). pp. 1253–1263. IEEE (2025)
2025
Later among the works it cites.
Sun, F.Y., Liu, W., Gu, S., Lim, D., Bhat, G., Tombari, F., Li, M., Haber, N., Wu, J.: Layoutvlm: Differentiable optimization of 3d layout via vision-language models. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 29469–29478 (2025)
2025
Later among the works it cites.
2025
Later among the works it cites.
2025
Later among the works it cites.
2025
Later among the works it cites.
2025
Later among the works it cites.
2025
Later among the works it cites.
Wang, F., Zhao, Z., Liu, Y., Zhang, D., Gao, J., Sun, H., Li, X.: Svgen: Interpretable vector graphics generation with large language models. In: Proceedings of the 33rd ACM International Conference on Multimedia. pp. 9608–9617. MM ’25, Association for Computing Machinery, New York, NY, USA (2025). https://doi.org/10.1145/3746027.3755011
2025
Later among the works it cites.
Wang, Z.Z., Mao, J., Fried, D., Neubig, G.: Agent workflow memory. In: Forty-second International Conference on Machine Learning (2025), https://openreview.net/forum?id=NTAhi2JEEE
2025
Later among the works it cites.
Xu, W., Liang, Z., Mei, K., Gao, H., Tan, J., Zhang, Y.: A-mem: Agentic memory for llm agents. In: Advances in Neural Information Processing Systems (2025)
2025
Later among the works it cites.
2025
Later among the works it cites.
2025
Later among the works it cites.
2025
Later among the works it cites.
2025
Later among the works it cites.
Zhou, M., Wang, Y., Hou, J., Zhang, S., Li, Y., Luo, C., Peng, J., Zhang, Z.: Scenex: procedural controllable large-scale scene generation. In: Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence and Thirty-Seventh Conference on Innovative Applications of Artificial Intelligence and Fifteenth Symposium on Educational Advances in Artificial Intelligence. AAAI’25/IAAI’25/EAAI’25, AAAI Press (2025). https://doi.org/10.1609/aaai.v39i10.33174
2025
Later among the works it cites.
Anthropic: The complete guide to building skills for claude. Tech. rep., Anthropic (jan 2026), https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf , accessed: 2026-03-05
2026
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