BANMo: Building Animatable 3D Neural Models from Many Casual Videos

Yang, Guidong; Vo, Minh; Neverova, Natalia; Ramanan, Deva; Vedaldi, Andrea; Joo, Hanbyul

doi:10.48550/arxiv.2112.12761

Cited by 2 publications

(2 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These works reason about the part-level geometry on the point cloud, which lacks the mesh information required for physical simulation. A series of methods on reconstructing deformable object [5,57,58] use articulated bones to represent articulation. These representations loosely constrain the motions of object parts.…”

Section: Related Workmentioning

confidence: 99%

Ditto: Building Digital Twins of Articulated Objects from Interaction

Jiang¹,

Hsu²,

Zhu³

2022

Preprint

View full text Add to dashboard Cite

Digitizing physical objects into the virtual world has the potential to unlock new research and applications in embodied AI and mixed reality. This work focuses on recreating interactive digital twins of real-world articulated objects, which can be directly imported into virtual environments. We introduce Ditto to learn articulation model estimation and 3D geometry reconstruction of an articulated object through interactive perception. Given a pair of visual observations of an articulated object before and after interaction, Ditto reconstructs part-level geometry and estimates the articulation model of the object. We employ implicit neural representations for joint geometry and articulation modeling. Our experiments show that Ditto effectively builds digital twins of articulated objects in a category-agnostic way. We also apply Ditto to real-world objects and deploy the recreated digital twins in physical simulation. Code and additional results are available at https://ut-austin-rpl.github.io/Ditto/

show abstract

Section: Related Workmentioning

confidence: 99%

Ditto: Building Digital Twins of Articulated Objects from Interaction

Jiang¹,

Hsu²,

Zhu³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…We show more visualizations about trajectory motion segmentation and camera localization in Figure 8. Sequences are from 3DPW [44], Youtube-VOS [80], GOT-10K [28] and BANMO [83] dataset. See the attached video for better experience.…”

Section: Additional Visualizationmentioning

confidence: 99%

ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild

Wang¹,

Liu²,

Guo³

et al. 2022

Preprint

View full text Add to dashboard Cite

Estimating the pose of a moving camera from monocular video is a challenging problem, especially due to the presence of moving objects in dynamic environments, where the performance of existing camera pose estimation methods are susceptible to pixels that are not geometrically consistent. To tackle this challenge, we present a robust dense indirect structure-from-motion method for videos that is based on dense correspondence initialized from pairwise optical flow. Our key idea is to optimize long-range video correspondence as dense point trajectories and use it to learn robust estimation of motion segmentation. A novel neural network architecture is proposed for processing irregular point trajectory data. Camera poses are then estimated and optimized with global bundle adjustment over the portion of long-range point trajectories that are classified as static. Experiments on MPI Sintel dataset show that our system produces significantly more accurate camera trajectories compared to existing state-of-the-art methods. In addition, our method is able to retain reasonable accuracy of camera poses on fully static scenes, which consistently outperforms strong state-of-the-art dense correspondence based methods with end-to-end deep learning, demonstrating the potential of dense indirect methods based on optical flow and point trajectories. As the point trajectory representation is general, we further present results and comparisons on in-the-wild monocular videos with complex motion of dynamic objects. Code is available at https://github.com/bytedance/particle-sfm.

show abstract

BANMo: Building Animatable 3D Neural Models from Many Casual Videos

Cited by 2 publications

References 47 publications

Ditto: Building Digital Twins of Articulated Objects from Interaction

Ditto: Building Digital Twins of Articulated Objects from Interaction

ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild

Contact Info

Product

Resources

About