EgoGaussian: Dynamic Scene Understanding from Egocentric Video with 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2406.19811v2
• Date: Wed, 02 Oct 2024 04:00:01 GMT
• Title: EgoGaussian: Dynamic Scene Understanding from Egocentric Video with 3D Gaussian Splatting
• Authors: Daiwei Zhang, Gengyan Li, Jiajie Li, Mickaël Bressieux, Otmar Hilliges, Marc Pollefeys, Luc Van Gool, Xi Wang,
• Abstract summary: EgoGaussian is a method capable of simultaneously reconstructing 3D scenes and dynamically tracking 3D object motion from RGB egocentric input alone. We show significant improvements in terms of both dynamic object and background reconstruction quality compared to the state-of-the-art.
• Score: 95.44545809256473
• License:
• Abstract: Human activities are inherently complex, often involving numerous object interactions. To better understand these activities, it is crucial to model their interactions with the environment captured through dynamic changes. The recent availability of affordable head-mounted cameras and egocentric data offers a more accessible and efficient means to understand human-object interactions in 3D environments. However, most existing methods for human activity modeling neglect the dynamic interactions with objects, resulting in only static representations. The few existing solutions often require inputs from multiple sources, including multi-camera setups, depth-sensing cameras, or kinesthetic sensors. To this end, we introduce EgoGaussian, the first method capable of simultaneously reconstructing 3D scenes and dynamically tracking 3D object motion from RGB egocentric input alone. We leverage the uniquely discrete nature of Gaussian Splatting and segment dynamic interactions from the background, with both having explicit representations. Our approach employs a clip-level online learning pipeline that leverages the dynamic nature of human activities, allowing us to reconstruct the temporal evolution of the scene in chronological order and track rigid object motion. EgoGaussian shows significant improvements in terms of both dynamic object and background reconstruction quality compared to the state-of-the-art. We also qualitatively demonstrate the high quality of the reconstructed models.

Related papers

• MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion [118.74385965694694]
  We present Motion DUSt3R (MonST3R), a novel geometry-first approach that directly estimates per-timestep geometry from dynamic scenes. By simply estimating a pointmap for each timestep, we can effectively adapt DUST3R's representation, previously only used for static scenes, to dynamic scenes. We show that by posing the problem as a fine-tuning task, identifying several suitable datasets, and strategically training the model on this limited data, we can surprisingly enable the model to handle dynamics.
  arXiv  Detail & Related papers  (2024-10-04T18:00:07Z)
• Shape of Motion: 4D Reconstruction from a Single Video [51.04575075620677]
  We introduce a method capable of reconstructing generic dynamic scenes, featuring explicit, full-sequence-long 3D motion. We exploit the low-dimensional structure of 3D motion by representing scene motion with a compact set of SE3 motion bases. Our method achieves state-of-the-art performance for both long-range 3D/2D motion estimation and novel view synthesis on dynamic scenes.
  arXiv  Detail & Related papers  (2024-07-18T17:59:08Z)
• Scaling Up Dynamic Human-Scene Interaction Modeling [58.032368564071895]
  TRUMANS is the most comprehensive motion-captured HSI dataset currently available. It intricately captures whole-body human motions and part-level object dynamics. We devise a diffusion-based autoregressive model that efficiently generates HSI sequences of any length.
  arXiv  Detail & Related papers  (2024-03-13T15:45:04Z)
• ParaHome: Parameterizing Everyday Home Activities Towards 3D Generative Modeling of Human-Object Interactions [11.32229757116179]
  We introduce the ParaHome system, designed to capture dynamic 3D movements of humans and objects within a common home environment. By leveraging the ParaHome system, we collect a novel large-scale dataset of human-object interaction.
  arXiv  Detail & Related papers  (2024-01-18T18:59:58Z)
• Estimating 3D Motion and Forces of Human-Object Interactions from Internet Videos [49.52070710518688]
  We introduce a method to reconstruct the 3D motion of a person interacting with an object from a single RGB video. Our method estimates the 3D poses of the person together with the object pose, the contact positions and the contact forces on the human body.
  arXiv  Detail & Related papers  (2021-11-02T13:40:18Z)
• NeuralDiff: Segmenting 3D objects that move in egocentric videos [92.95176458079047]
  We study the problem of decomposing the observed 3D scene into a static background and a dynamic foreground. This task is reminiscent of the classic background subtraction problem, but is significantly harder because all parts of the scene, static and dynamic, generate a large apparent motion. In particular, we consider egocentric videos and further separate the dynamic component into objects and the actor that observes and moves them.
  arXiv  Detail & Related papers  (2021-10-19T12:51:35Z)
• Kinematics-Guided Reinforcement Learning for Object-Aware 3D Ego-Pose Estimation [25.03715978502528]
  We propose a method for incorporating object interaction and human body dynamics into the task of 3D ego-pose estimation. We use a kinematics model of the human body to represent the entire range of human motion, and a dynamics model of the body to interact with objects inside a physics simulator. This is the first work to estimate a physically valid 3D full-body interaction sequence with objects from egocentric videos.
  arXiv  Detail & Related papers  (2020-11-10T00:06:43Z)
• Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction [24.72947291987545]
  Key challenge for an agent learning to interact with the world is to reason about physical properties of objects. We propose a novel approach for modeling the dynamics of a robot's interactions directly from unlabeled 3D point clouds and images.
  arXiv  Detail & Related papers  (2020-08-02T11:04:49Z)

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.