Update README.md

README.md

@@ -35,10 +35,10 @@ Modify the README.md and follow the format TITLE, AUTHOR, CONFERENCE YEAR | OPTI
 - [LayoutGPT: Compositional Visual Planning and Generation with Large Language Models](https://arxiv.org/abs/2305.15393), arxiv 2023| [github](https://github.com/weixi-feng/LayoutGPT) | [project](https://layoutgpt.github.io/)
 - [GALA3D: Towards Text-to-3D Complex Scene Generation via Layout-guided Generative Gaussian Splatting](https://arxiv.org/abs/2402.07207), Zhou et al., arxiv 2024 | [github](https://github.com/VDIGPKU/GALA3D) | [bibtext](./citations/gala3d.txt)
 - [Learning 3D Scene Priors with 2D Supervision](https://arxiv.org/abs/2211.14157) | CVPR 2023 | [github](https://github.com/yinyunie/ScenePriors) | [peoject](https://yinyunie.github.io/sceneprior-page/)
-- ATISS
-- [DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis](https://arxiv.org/abs/2303.14207) | CVPR 2024 | [github](https://github.com/tangjiapeng/DiffuScene) | [project](https://tangjiapeng.github.io/projects/DiffuScene/)
-- Lay-A-Scene
-- [Infinigen-Indoor
+- [ATISS: Autoregressive Transformers for Indoor Scene Synthesis](https://arxiv.org/pdf/2110.03675) | NIPS 2021 | [github](https://github.com/nv-tlabs/ATISS) | [project](https://research.nvidia.com/labs/toronto-ai/ATISS/)
+- [DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis](https://arxiv.org/abs/2303.14207) | arXiv 2024 | CVPR 2024 | [github](https://github.com/tangjiapeng/DiffuScene) | [project](https://tangjiapeng.github.io/projects/DiffuScene/)
+- [Lay-A-Scene: Personalized 3D Object Arrangement Using Text-to-Image Priors](https://arxiv.org/abs/2406.00687) | [project](https://lay-a-scene.github.io/)
+- [Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generation](https://arxiv.org/abs/2406.11824)  | [github](https://github.com/princeton-vl/infinigen) | [project])(https://infinigen.org/)
 - [InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior](https://arxiv.org/abs/2402.04717) | ICLR 2024 | [github](https://github.com/chenguolin/InstructScene) | [project](https://chenguolin.github.io/projects/InstructScene/)
 - [Compositional 3D Scene Generation using Locally Conditioned Diffusion](https://arxiv.org/abs/2303.12218), arxiv 2023 | [project](https://ryanpo.com/comp3d/) 
 - [CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion](https://arxiv.org/abs/2305.16283) | NIPS 2023 | [github](https://github.com/ymxlzgy/commonscenes) | [project](https://sites.google.com/view/commonscenes)
@@ -53,7 +53,7 @@ Modify the README.md and follow the format TITLE, AUTHOR, CONFERENCE YEAR | OPTI
 - [Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models](https://arxiv.org/abs/1811.12463) | CVPR 2019
 - [Scene Synthesis via Uncertainty-Driven Attribute Synchronization](https://arxiv.org/abs/2108.13499) | ICCV 2021 | [github](https://github.com/brownvc/fast-synth)
 - [ProcTHOR: Large-Scale Embodied AI Using Procedural Generation](https://procthor.allenai.org/), Deitke et al., NeurIPS 2022 |  [github](https://github.com/allenai/procthor) | [bibtex](./citations/procthor.txt)
-
+- [SceneCraft: An LLM Agent for Synthesizing 3D Scenes as Blender Code](https://arxiv.org/abs/2403.01248) | ICML 2024
 - .....