# Putting Humans in a Scene: Learning Affordance in 3D Indoor Environments

arXiv: https://arxiv.org/abs/1903.05690 · HTML: https://mlsanity.com/paper/1903.05690/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2019.

## Built on by (most cited first, 30 of 72)

- [OpenScene: 3D Scene Understanding with Open Vocabularies](https://mlsanity.com/paper/2211.15654/) (2022)
- [BEHAVE: Dataset and Method for Tracking Human Object Interactions](https://mlsanity.com/paper/2204.06950/) (2022)
- [Diffusion-based Generation, Optimization, and Planning in 3D Scenes](https://mlsanity.com/paper/2301.06015/) (2023)
- [Hand-Object Contact Consistency Reasoning for Human Grasps Generation](https://mlsanity.com/paper/2104.03304/) (2021)
- [Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors](https://mlsanity.com/paper/2103.17265/) (2021)
- [Scaling Up Dynamic Human-Scene Interaction Modeling](https://mlsanity.com/paper/2403.08629/) (2024)
- [COUCH: Towards Controllable Human-Chair Interactions](https://mlsanity.com/paper/2205.00541/) (2022)
- [Affordance Diffusion: Synthesizing Hand-Object Interactions](https://mlsanity.com/paper/2303.12538/) (2023)
- [Synthesizing Diverse Human Motions in 3D Indoor Scenes](https://mlsanity.com/paper/2305.12411/) (2023)
- [InterCap: Joint Markerless 3D Tracking of Humans and Objects in Interaction](https://mlsanity.com/paper/2209.12354/) (2022)
- [SAGA: Stochastic Whole-Body Grasping with Contact](https://mlsanity.com/paper/2112.10103/) (2021)
- [Compositional Human-Scene Interaction Synthesis with Semantic Control](https://mlsanity.com/paper/2207.12824/) (2022)
- [Move as You Say, Interact as You Can: Language-guided Human Motion Generation with Scene Affordance](https://mlsanity.com/paper/2403.18036/) (2024)
- [GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving](https://mlsanity.com/paper/2101.06543/) (2021)
- [Diffusion-Guided Reconstruction of Everyday Hand-Object Interaction Clips](https://mlsanity.com/paper/2309.05663/) (2023)
- [Scene-aware Generative Network for Human Motion Synthesis](https://mlsanity.com/paper/2105.14804/) (2021)
- [AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions](https://mlsanity.com/paper/2112.00246/) (2021)
- [Human-Aware Object Placement for Visual Environment Reconstruction](https://mlsanity.com/paper/2203.03609/) (2022)
- [Autonomous Character-Scene Interaction Synthesis from Text Instruction](https://mlsanity.com/paper/2410.03187/) (2024)
- [Putting People in Their Place: Affordance-Aware Human Insertion into Scenes](https://mlsanity.com/paper/2304.14406/) (2023)
- [ReGenNet: Towards Human Action-Reaction Synthesis](https://mlsanity.com/paper/2403.11882/) (2024)
- [Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations](https://mlsanity.com/paper/2303.13129/) (2023)
- [Open-Vocabulary Affordance Detection in 3D Point Clouds](https://mlsanity.com/paper/2303.02401/) (2023)
- [One-Shot Object Affordance Detection in the Wild](https://mlsanity.com/paper/2108.03658/) (2021)
- [GenZI: Zero-Shot 3D Human-Scene Interaction Generation](https://mlsanity.com/paper/2311.17737/) (2023)
- [Generating Continual Human Motion in Diverse 3D Scenes](https://mlsanity.com/paper/2304.02061/) (2023)
- [Embodied Scene-aware Human Pose Estimation](https://mlsanity.com/paper/2206.09106/) (2022)
- [EgoChoir: Capturing 3D Human-Object Interaction Regions from Egocentric Views](https://mlsanity.com/paper/2405.13659/) (2024)
- [Learning Visual Affordance Grounding from Demonstration Videos](https://mlsanity.com/paper/2108.05675/) (2021)
- [A Survey on Human Interaction Motion Generation](https://mlsanity.com/paper/2503.12763/) (2025)

## Abstract (arXiv)

Affordance modeling plays an important role in visual understanding. In this paper, we aim to predict affordances of 3D indoor scenes, specifically what human poses are afforded by a given indoor environment, such as sitting on a chair or standing on the floor. In order to predict valid affordances and learn possible 3D human poses in indoor scenes, we need to understand the semantic and geometric structure of a scene as well as its potential interactions with a human. To learn such a model, a large-scale dataset of 3D indoor affordances is required. In this work, we build a fully automatic 3D pose synthesizer that fuses semantic knowledge from a large number of 2D poses extracted from TV shows as well as 3D geometric knowledge from voxel representations of indoor scenes. With the data created by the synthesizer, we introduce a 3D pose generative model to predict semantically plausible and physically feasible human poses within a given scene (provided as a single RGB, RGB-D, or depth image). We demonstrate that our human affordance prediction method consistently outperforms existing state-of-the-art methods.