Search Results for author:
Found 9 papers, 2 papers with code
Track Everything Everywhere Fast and Robustly
We propose a novel test-time optimization approach for efficiently and robustly tracking any pixel at any time in a video.
DynMF: Neural Motion Factorization for Real-time Dynamic View Synthesis with 3D Gaussian Splatting
We argue that the per-point motions of a dynamic scene can be decomposed into a small set of explicit or learned trajectories.
GART: Gaussian Articulated Template Models
We introduce Gaussian Articulated Template Model GART, an explicit, efficient, and expressive representation for non-rigid articulated subject capturing and rendering from monocular videos.
NAP: Neural 3D Articulation Prior
We propose Neural 3D Articulation Prior (NAP), the first 3D deep generative model to synthesize 3D articulated object models.
EFEM: Equivariant Neural Field Expectation Maximization for 3D Object Segmentation Without Scene Supervision
First, we introduce equivariant shape representations to this problem to eliminate the complexity induced by the variation in object configuration.
Equivariant Light Field Convolution and Transformer
We model the ray space, the domain of the light field, as a homogeneous space of $SE(3)$ and introduce the $SE(3)$-equivariant convolution in ray space.
Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces
We present a unified derivation of kernels via the Fourier domain by leveraging the sparsity of Fourier coefficients of the lifted feature fields.
CaDeX: Learning Canonical Deformation Coordinate Space for Dynamic Surface Representation via Neural Homeomorphism
While neural representations for static 3D shapes are widely studied, representations for deformable surfaces are limited to be template-dependent or lack efficiency.
Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images
We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views.
