UAV4D: Dynamic Neural Rendering of Human-Centric UAV Imagery using Gaussian Splatting

• URL: http://arxiv.org/abs/2506.05011v1
• Date: Thu, 05 Jun 2025 13:21:09 GMT
• Title: UAV4D: Dynamic Neural Rendering of Human-Centric UAV Imagery using Gaussian Splatting
• Authors: Jaehoon Choi, Dongki Jung, Christopher Maxey, Yonghan Lee, Sungmin Eum, Dinesh Manocha, Heesung Kwon,
• Abstract summary: We introduce UAV4D, a framework for enabling photorealistic rendering for dynamic real-world scenes captured by UAVs.<n>We use a combination of a 3D foundation model and a human mesh reconstruction model to reconstruct both the scene background and humans.<n>Our results demonstrate the benefits of our approach over existing methods in novel view synthesis, achieving a 1.5 dB PSNR improvement and superior visual sharpness.
• Score: 54.883935964137706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite significant advancements in dynamic neural rendering, existing methods fail to address the unique challenges posed by UAV-captured scenarios, particularly those involving monocular camera setups, top-down perspective, and multiple small, moving humans, which are not adequately represented in existing datasets. In this work, we introduce UAV4D, a framework for enabling photorealistic rendering for dynamic real-world scenes captured by UAVs. Specifically, we address the challenge of reconstructing dynamic scenes with multiple moving pedestrians from monocular video data without the need for additional sensors. We use a combination of a 3D foundation model and a human mesh reconstruction model to reconstruct both the scene background and humans. We propose a novel approach to resolve the scene scale ambiguity and place both humans and the scene in world coordinates by identifying human-scene contact points. Additionally, we exploit the SMPL model and background mesh to initialize Gaussian splats, enabling holistic scene rendering. We evaluated our method on three complex UAV-captured datasets: VisDrone, Manipal-UAV, and Okutama-Action, each with distinct characteristics and 10~50 humans. Our results demonstrate the benefits of our approach over existing methods in novel view synthesis, achieving a 1.5 dB PSNR improvement and superior visual sharpness.

Related papers

• UAVTwin: Neural Digital Twins for UAVs using Gaussian Splatting [57.63613048492219]
  We present UAVTwin, a method for creating digital twins from real-world environments and facilitating data augmentation for training downstream models embedded in unmanned aerial vehicles (UAVs)<n>This is achieved by integrating 3D Gaussian Splatting (3DGS) for reconstructing backgrounds along with controllable synthetic human models that display diverse appearances and actions in multiple poses.
  arXiv  Detail & Related papers  (2025-04-02T22:17:30Z)
• WonderHuman: Hallucinating Unseen Parts in Dynamic 3D Human Reconstruction [51.22641018932625]
  We present WonderHuman to reconstruct dynamic human avatars from a monocular video for high-fidelity novel view synthesis.<n>Our method achieves SOTA performance in producing photorealistic renderings from the given monocular video.
  arXiv  Detail & Related papers  (2025-02-03T04:43:41Z)
• DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features [65.8738034806085]
  DistillNeRF is a self-supervised learning framework for understanding 3D environments in autonomous driving scenes. Our method is a generalizable feedforward model that predicts a rich neural scene representation from sparse, single-frame multi-view camera inputs.
  arXiv  Detail & Related papers  (2024-06-17T21:15:13Z)
• Deformable 3D Gaussian Splatting for Animatable Human Avatars [50.61374254699761]
  We propose a fully explicit approach to construct a digital avatar from as little as a single monocular sequence. ParDy-Human constitutes an explicit model for realistic dynamic human avatars which requires significantly fewer training views and images. Our avatars learning is free of additional annotations such as Splat masks and can be trained with variable backgrounds while inferring full-resolution images efficiently even on consumer hardware.
  arXiv  Detail & Related papers  (2023-12-22T20:56:46Z)
• Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition [40.46674919612935]
  We present Vid2Avatar, a method to learn human avatars from monocular in-the-wild videos. Our method does not require any groundtruth supervision or priors extracted from large datasets of clothed human scans. It solves the tasks of scene decomposition and surface reconstruction directly in 3D by modeling both the human and the background in the scene jointly.
  arXiv  Detail & Related papers  (2023-02-22T18:59:17Z)
• Neural Groundplans: Persistent Neural Scene Representations from a Single Image [90.04272671464238]
  We present a method to map 2D image observations of a scene to a persistent 3D scene representation. We propose conditional neural groundplans as persistent and memory-efficient scene representations.
  arXiv  Detail & Related papers  (2022-07-22T17:41:24Z)
• DronePose: Photorealistic UAV-Assistant Dataset Synthesis for 3D Pose Estimation via a Smooth Silhouette Loss [27.58747838557417]
  3D localisation of the UAV assistant is an important task that can facilitate the exchange of spatial information between the user and the UAV. We design a data synthesis pipeline to create a realistic multimodal dataset that includes both the exocentric user view, and the egocentric UAV view. We then exploit the joint availability of photorealistic and synthesized inputs to train a single-shot monocular pose estimation model.
  arXiv  Detail & Related papers  (2020-08-20T07:54:56Z)

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.