UnrealPose: Leveraging Game Engine Kinematics for Large-Scale Synthetic Human Pose Data

• URL: http://arxiv.org/abs/2601.00991v1
• Date: Fri, 02 Jan 2026 21:44:02 GMT
• Title: UnrealPose: Leveraging Game Engine Kinematics for Large-Scale Synthetic Human Pose Data
• Abstract summary: We introduce UnrealPose-Gen, an Unreal Engine 5 pipeline built on Movie Render Queue for high-quality offline rendering.<n>We present UnrealPose-1M, an approximately one million frame corpus comprising eight sequences.<n>We report real-to-synthetic results on four tasks: image-to-3D pose, 2D keypoint detection, 2D-to-3D lifting, and person detection/segmentation.
• Score: 2.7087606206363226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diverse, accurately labeled 3D human pose data is expensive and studio-bound, while in-the-wild datasets lack known ground truth. We introduce UnrealPose-Gen, an Unreal Engine 5 pipeline built on Movie Render Queue for high-quality offline rendering. Our generated frames include: (i) 3D joints in world and camera coordinates, (ii) 2D projections and COCO-style keypoints with occlusion and joint-visibility flags, (iii) person bounding boxes, and (iv) camera intrinsics and extrinsics. We use UnrealPose-Gen to present UnrealPose-1M, an approximately one million frame corpus comprising eight sequences: five scripted "coherent" sequences spanning five scenes, approximately 40 actions, and five subjects; and three randomized sequences across three scenes, approximately 100 actions, and five subjects, all captured from diverse camera trajectories for broad viewpoint coverage. As a fidelity check, we report real-to-synthetic results on four tasks: image-to-3D pose, 2D keypoint detection, 2D-to-3D lifting, and person detection/segmentation. Though time and resources constrain us from an unlimited dataset, we release the UnrealPose-1M dataset, as well as the UnrealPose-Gen pipeline to support third-party generation of human pose data.

Related papers

• Syn4D: A Multiview Synthetic 4D Dataset [92.50132053229817]
  3D reconstruction and tracking of dynamic scenes from monocular video remains an important open challenge in computer vision.<n>To address this limitation, we introduce Syn4D, a multiview synthetic dynamic scenes.<n>Key feature of Syn4D is the ability to unproject any pixel into 3D.
  arXiv  Detail & Related papers  (2026-05-06T17:59:55Z)
• JRDB-Pose3D: A Multi-person 3D Human Pose and Shape Estimation Dataset for Robotics [15.188501869677532]
  JRDB-Pose3D captures multi-human indoor and outdoor environments from a mobile robotic platform.<n> JRDB-Pose3D contains, on average, 5-10 human poses per frame, with some scenes featuring up to 35 individuals simultaneously.
  arXiv  Detail & Related papers  (2026-02-03T03:46:27Z)
• Drag4D: Align Your Motion with Text-Driven 3D Scene Generation [77.79131321983677]
  Drag4D is an interactive framework that integrates object motion control within text-driven 3D scene generation.<n>This framework enables users to define 3D trajectories for the 3D objects generated from a single image, seamlessly integrating them into a high-quality 3D background.
  arXiv  Detail & Related papers  (2025-09-26T05:23:45Z)
• Multi-View 3D Point Tracking [67.21282192436031]
  We introduce the first data-driven multi-view 3D point tracker, designed to track arbitrary points in dynamic scenes using multiple camera views.<n>Our model directly predicts 3D correspondences using a practical number of cameras.<n>We train on 5K synthetic multi-view Kubric sequences and evaluate on two real-world benchmarks.
  arXiv  Detail & Related papers  (2025-08-28T17:58:20Z)
• Towards Scalable Spatial Intelligence via 2D-to-3D Data Lifting [64.64738535860351]
  We present a scalable pipeline that converts single-view images into comprehensive, scale- and appearance-realistic 3D representations.<n>Our method bridges the gap between the vast repository of imagery and the increasing demand for spatial scene understanding.<n>By automatically generating authentic, scale-aware 3D data from images, we significantly reduce data collection costs and open new avenues for advancing spatial intelligence.
  arXiv  Detail & Related papers  (2025-07-24T14:53:26Z)
• Zero-Shot Multi-Object Scene Completion [59.325611678171974]
  We present a 3D scene completion method that recovers the complete geometry of multiple unseen objects in complex scenes from a single RGB-D image. Our method outperforms the current state-of-the-art on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-03-21T17:59:59Z)
• LiCamPose: Combining Multi-View LiDAR and RGB Cameras for Robust Single-frame 3D Human Pose Estimation [31.651300414497822]
  LiCamPose is a pipeline that integrates multi-view RGB and sparse point cloud information to estimate robust 3D human poses via single frame. LiCamPose is evaluated on four datasets, including two public datasets, one synthetic dataset, and one challenging self-collected dataset.
  arXiv  Detail & Related papers  (2023-12-11T14:30:11Z)
• DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity Human-centric Rendering [126.00165445599764]
  We present DNA-Rendering, a large-scale, high-fidelity repository of human performance data for neural actor rendering. Our dataset contains over 1500 human subjects, 5000 motion sequences, and 67.5M frames' data volume. We construct a professional multi-view system to capture data, which contains 60 synchronous cameras with max 4096 x 3000 resolution, 15 fps speed, and stern camera calibration steps.
  arXiv  Detail & Related papers  (2023-07-19T17:58:03Z)
• SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments [0.0]
  We present SLOPER4D, a novel scene-aware dataset collected in large urban environments. We record 12 human subjects' activities over 10 diverse urban scenes from an egocentric view. SLOPER4D consists of 15 sequences of human motions, each of which has a trajectory length of more than 200 meters.
  arXiv  Detail & Related papers  (2023-03-16T05:54:15Z)
• Scene-Aware 3D Multi-Human Motion Capture from a Single Camera [83.06768487435818]
  We consider the problem of estimating the 3D position of multiple humans in a scene as well as their body shape and articulation from a single RGB video recorded with a static camera. We leverage recent advances in computer vision using large-scale pre-trained models for a variety of modalities, including 2D body joints, joint angles, normalized disparity maps, and human segmentation masks. In particular, we estimate the scene depth and unique person scale from normalized disparity predictions using the 2D body joints and joint angles.
  arXiv  Detail & Related papers  (2023-01-12T18:01:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.