Holodeck: Language Guided Generation of 3D Embodied AI Environments
On the map: World models → Video-generation world models and simulators
Abstract (arXiv)
3D simulated environments play a critical role in Embodied AI, but their creation requires expertise and extensive manual effort, restricting their diversity and scope. To mitigate this limitation, we present Holodeck, a system that generates 3D environments to match a user-supplied prompt fully automatedly. Holodeck can generate diverse scenes, e.g., arcades, spas, and museums, adjust the designs for styles, and can capture the semantics of complex queries such as "apartment for a researcher with a cat" and "office of a professor who is a fan of Star Wars". Holodeck leverages a large language model (i.e., GPT-4) for common sense knowledge about what the scene might look like and uses a large collection of 3D assets from Objaverse to populate the scene with diverse objects. To address the challenge of positioning objects correctly, we prompt GPT-4 to generate spatial relational constraints between objects and then optimize the layout to satisfy those constraints. Our large-scale human evaluation shows that annotators prefer Holodeck over manually designed procedural baselines in residential scenes and that Holodeck can produce high-quality outputs for diverse scene types. We also demonstrate an exciting application of Holodeck in Embodied AI, training agents to navigate in novel scenes like music rooms and daycares without human-constructed data, which is a significant step forward in developing general-purpose embodied agents.
Key facts. Cited by 257 papers in the map (data through 7 Oct 2026). Placed in Video-generation world models and simulators, one of 11 directions in World models. Builds on 43 of its 58 references that are in the map. First posted on arXiv 14 Dec 2023.
Builds on
43 of 58 references.
Closest ancestors · 4
Same direction, newest first
- 2022DreamFusion: Text-to-3D using 2D Diffusion29 Sep
- 2021Learning Transferable Visual Models From Natural Language Supervision26 Feb
- 2020ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation9 Jul
- 2017AI2-THOR: An Interactive 3D Environment for Visual AI14 Dec
Other references · 39
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- 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
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- 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,388
- 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,522
- 2022Magic3D: High-Resolution Text-to-3D Content Creationcited by 1,285
- 2019Habitat: A Platform for Embodied AI Researchcited by 1,163
- 2021Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AIcited by 579
- 2016Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modelingcited by 558
- 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
- 2022Latent-NeRF for Shape-Guided Generation of 3D Shapes and Texturescited by 506
27 more
- 2021Habitat 2.0: Training Home Assistants to Rearrange their Habitatcited by 501
- 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 487
- 2018Gibson Env: Real-World Perception for Embodied Agentscited by 477
- 2018VirtualHome: Simulating Household Activities via Programscited by 411
- 20203D-FRONT: 3D Furnished Rooms with layOuts and semaNTicscited by 375
- 2022ProcTHOR: Large-Scale Embodied AI Using Procedural Generationcited by 369
- 2023LayoutGPT: Compositional Visual Planning and Generation with Large Language Modelscited by 337
- 2023Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Modelscited by 245
- 2020ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objectscited by 240
- 2023RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulationcited by 206
- 2021ATISS: Autoregressive Transformers for Indoor Scene Synthesiscited by 195
- 2021Simple but Effective: CLIP Embeddings for Embodied AIcited by 191
- 2020RoboTHOR: An Open Simulation-to-Real Embodied AI Platformcited by 190
- 2023NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusioncited by 179
- 2018Escaping Plato's Cave: 3D Shape From Adversarial Renderingcited by 170
- 2023Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigationcited by 162
- 2023SceneScape: Text-Driven Consistent Scene Generationcited by 160
- 2020SceneFormer: Indoor Scene Generation with Transformerscited by 154
- 2023Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fieldscited by 122
- 2023LEGO-Net: Learning Regular Rearrangements of Objects in Roomscited by 75
- 2022HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoisingcited by 72
- 2022Phone2Proc: Bringing Robust Robots Into Our Chaotic Worldcited by 24
- 2017SceneSeer: 3D Scene Design with Natural Languagecited by 20
- 2023Towards Language-guided Interactive 3D Generation: LLMs as Layout Interpreter with Generative Feedbackcited by 17
- 2020Graph2Plan: Learning Floorplan Generation from Layout Graphscited by 15
- 2023RoomDesigner: Encoding Anchor-latents for Style-consistent and Shape-compatible Indoor Scene Generationcited by 12
- 2023Aladdin: Zero-Shot Hallucination of Stylized 3D Assets from Abstract Scene Descriptionscited by 11
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Citing papers per year: 2024: 40; 2025: 87; 2026: 130.
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