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Holistic++ Scene Understanding

This repo contains code for ICCV 2019 paper
Holistic++ Scene Understanding: Single-view 3D Holistic Scene Parsing and Human Pose Estimation with Human-Object Interaction and Physical Commensense
Yixin Chen*, Siyuan Huang*, Tao Yuan, Siyuan Qi, Yixin Zhu, Song-Chun Zhu
The IEEE International Conference on Computer Vision (ICCV), 2019
(* indicates equal contribution.)

In this paper, we propose a new 3D holistic++ scene understanding problem, which jointly tackles two tasks from a single-view image: (i) holistic scene parsing and reconstruction—3D estimations of object bounding boxes, camera pose, and room layout, and (ii) 3D human pose estimation. The intuition behind is to leverage the coupled nature of these two tasks to improve the granularity and performance of scene understanding. We propose to exploit two critical and essential connections between these two tasks: (i) human-object interaction (HOI) to model the fine-grained relations between agents and objects in the scene, and (ii) physical commonsense to model the physical plausibility of the reconstructed scene. The optimal configuration of the 3D scene, represented by a parse graph, is inferred using Markov chain Monte Carlo (MCMC), which efficiently traverses through the non-differentiable joint solution space. Experimental results demonstrate that the proposed algorithm significantly improves the performance of the two tasks on three datasets, showing an improved generalization ability.

Please refer to our paper or project for more details.



Support Python 3+.

pip install -r requirements.txt


  1. Download the preprocessed data which contains the image and initialization from here , extract it with


We will release more images from different dataset with initialization soon.


Joint inference of holistic scene understanding and human pose by image name.

    python --image_name frame1


If you find the paper and/or the code helpful, please cite us.

title={Holistic++ Scene Understanding: Single-view 3D Holistic Scene Parsing and Human Pose Estimation with Human-Object Interaction and Physical Commonsense}, 
author = {Chen, Yixin and Huang, Siyuan and Yuan, Tao and Qi, Siyuan and Zhu, Yixin and Zhu, Song-Chun}, 
booktitle={The IEEE International Conference on Computer Vision (ICCV), 


Our code is released under the MIT license.

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