Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Aigerman, Noam"'
3D Gaussian Splatting (GS) is one of the most promising novel 3D representations that has received great interest in computer graphics and computer vision. While various systems have introduced editing capabilities for 3D GS, such as those guided by
Externí odkaz:
http://arxiv.org/abs/2411.12168
In recent years, many deep learning approaches have incorporated layers that solve optimization problems (e.g., linear, quadratic, and semidefinite programs). Integrating these optimization problems as differentiable layers requires computing the der
Externí odkaz:
http://arxiv.org/abs/2410.06324
Autor:
Chen, Qimin, Chen, Zhiqin, Kim, Vladimir G., Aigerman, Noam, Zhang, Hao, Chaudhuri, Siddhartha
We present a 3D modeling method which enables end-users to refine or detailize 3D shapes using machine learning, expanding the capabilities of AI-assisted 3D content creation. Given a coarse voxel shape (e.g., one produced with a simple box extrusion
Externí odkaz:
http://arxiv.org/abs/2409.06129
We propose MeshUp, a technique that deforms a 3D mesh towards multiple target concepts, and intuitively controls the region where each concept is expressed. Conveniently, the concepts can be defined as either text queries, e.g., "a dog" and "a turtle
Externí odkaz:
http://arxiv.org/abs/2408.14899
Autor:
Muralikrishnan, Sanjeev, Dutt, Niladri Shekhar, Chaudhuri, Siddhartha, Aigerman, Noam, Kim, Vladimir, Fisher, Matthew, Mitra, Niloy J.
We introduce Temporal Residual Jacobians as a novel representation to enable data-driven motion transfer. Our approach does not assume access to any rigging or intermediate shape keyframes, produces geometrically and temporally consistent motions, an
Externí odkaz:
http://arxiv.org/abs/2407.14958
This work proposes a novel representation of injective deformations of 3D space, which overcomes existing limitations of injective methods: inaccuracy, lack of robustness, and incompatibility with general learning and optimization frameworks. The cor
Externí odkaz:
http://arxiv.org/abs/2406.12121
The recent developments in neural fields have brought phenomenal capabilities to the field of shape generation, but they lack crucial properties, such as incremental control - a fundamental requirement for artistic work. Triangular meshes, on the oth
Externí odkaz:
http://arxiv.org/abs/2403.02460
Autor:
Maesumi, Arman, Guerrero, Paul, Kim, Vladimir G., Fisher, Matthew, Chaudhuri, Siddhartha, Aigerman, Noam, Ritchie, Daniel
Exploring variations of 3D shapes is a time-consuming process in traditional 3D modeling tools. Deep generative models of 3D shapes often feature continuous latent spaces that can, in principle, be used to explore potential variations starting from a
Externí odkaz:
http://arxiv.org/abs/2310.07814
Autor:
Aigerman, Noam, Groueix, Thibault
This paper proposes a fully-automatic, text-guided generative method for producing perfectly-repeating, periodic, tile-able 2D imagery, such as the one seen on floors, mosaics, ceramics, and the work of M.C. Escher. In contrast to square texture imag
Externí odkaz:
http://arxiv.org/abs/2309.14564
We present an automated technique for computing a map between two genus-zero shapes, which matches semantically corresponding regions to one another. Lack of annotated data prohibits direct inference of 3D semantic priors; instead, current State-of-t
Externí odkaz:
http://arxiv.org/abs/2309.04836