182 research outputs found
A Two-part Transformer Network for Controllable Motion Synthesis
Although part-based motion synthesis networks have been investigated to
reduce the complexity of modeling heterogeneous human motions, their
computational cost remains prohibitive in interactive applications. To this
end, we propose a novel two-part transformer network that aims to achieve
high-quality, controllable motion synthesis results in real-time. Our network
separates the skeleton into the upper and lower body parts, reducing the
expensive cross-part fusion operations, and models the motions of each part
separately through two streams of auto-regressive modules formed by multi-head
attention layers. However, such a design might not sufficiently capture the
correlations between the parts. We thus intentionally let the two parts share
the features of the root joint and design a consistency loss to penalize the
difference in the estimated root features and motions by these two
auto-regressive modules, significantly improving the quality of synthesized
motions. After training on our motion dataset, our network can synthesize a
wide range of heterogeneous motions, like cartwheels and twists. Experimental
and user study results demonstrate that our network is superior to
state-of-the-art human motion synthesis networks in the quality of generated
motions.Comment: 16 pages, 26 figure
Cutting and Fracturing Models without Remeshing
Abstract. A finite element simulation framework for cutting and fracturing model without remeshing is presented. The main idea of proposed method is adding a discontinuous function for the standard approximation to account for the crack. A feasible technique is adopted for dealing with multiple cracks and intersecting cracks. Several involved problems including extended freedoms of finite element nodes as well as mass matrix calculation are discussed. The presented approach is easy to simulate object deformation while changing topology. Moreover, previous methods developed in standard finite element framework, such as the stiffness warping method, can be extended and utilized
Representing Volumetric Videos as Dynamic MLP Maps
This paper introduces a novel representation of volumetric videos for
real-time view synthesis of dynamic scenes. Recent advances in neural scene
representations demonstrate their remarkable capability to model and render
complex static scenes, but extending them to represent dynamic scenes is not
straightforward due to their slow rendering speed or high storage cost. To
solve this problem, our key idea is to represent the radiance field of each
frame as a set of shallow MLP networks whose parameters are stored in 2D grids,
called MLP maps, and dynamically predicted by a 2D CNN decoder shared by all
frames. Representing 3D scenes with shallow MLPs significantly improves the
rendering speed, while dynamically predicting MLP parameters with a shared 2D
CNN instead of explicitly storing them leads to low storage cost. Experiments
show that the proposed approach achieves state-of-the-art rendering quality on
the NHR and ZJU-MoCap datasets, while being efficient for real-time rendering
with a speed of 41.7 fps for images on an RTX 3090 GPU. The
code is available at https://zju3dv.github.io/mlp_maps/.Comment: Accepted to CVPR 2023. The first two authors contributed equally to
this paper. Project page: https://zju3dv.github.io/mlp_maps
CP-SLAM: Collaborative Neural Point-based SLAM System
This paper presents a collaborative implicit neural simultaneous localization
and mapping (SLAM) system with RGB-D image sequences, which consists of
complete front-end and back-end modules including odometry, loop detection,
sub-map fusion, and global refinement. In order to enable all these modules in
a unified framework, we propose a novel neural point based 3D scene
representation in which each point maintains a learnable neural feature for
scene encoding and is associated with a certain keyframe. Moreover, a
distributed-to-centralized learning strategy is proposed for the collaborative
implicit SLAM to improve consistency and cooperation. A novel global
optimization framework is also proposed to improve the system accuracy like
traditional bundle adjustment. Experiments on various datasets demonstrate the
superiority of the proposed method in both camera tracking and mapping.Comment: Accepted at NeurIPS 202
- …