Noam Aigerman

Shahar Kovalsky

2024-10-08

ArXiv (prépublication)

DECOLLAGE: 3D Detailization by Controllable, Localized, and Learned Geometry Enhancement

Qimin Chen

Zhiqin Chen

Vladimir Kim

Hao (Richard) Zhang

Hao Zhang 0002

Siddhartha Chaudhuri

2024-10-03

Lecture Notes in Computer Science (publié)

MeshUp: Multi-Target Mesh Deformation via Blended Score Distillation

Hyunwoo Kim

Itai Lang

Thibault Groueix

Vladimir Kim

Rana Hanocka

We propose MeshUp, a technique that deforms a 3D mesh towards multiple target concepts, and intuitively controls the region where each conce… (voir plus)pt is expressed. Conveniently, the concepts can be defined as either text queries, e.g.,"a dog"and"a turtle,"or inspirational images, and the local regions can be selected as any number of vertices on the mesh. We can effectively control the influence of the concepts and mix them together using a novel score distillation approach, referred to as the Blended Score Distillation (BSD). BSD operates on each attention layer of the denoising U-Net of a diffusion model as it extracts and injects the per-objective activations into a unified denoising pipeline from which the deformation gradients are calculated. To localize the expression of these activations, we create a probabilistic Region of Interest (ROI) map on the surface of the mesh, and turn it into 3D-consistent masks that we use to control the expression of these activations. We demonstrate the effectiveness of BSD empirically and show that it can deform various meshes towards multiple objectives. Our project page is at https://threedle.github.io/MeshUp.

2024-08-27

ArXiv (prépublication)

Temporal Residual Jacobians For Rig-free Motion Transfer

Sanjeev Muralikrishnan

Niladri Shekhar Dutt

Siddhartha Chaudhuri

Vladimir Kim

Matthew Fisher

Niloy J. Mitra

We introduce Temporal Residual Jacobians as a novel representation to enable data-driven motion transfer. Our approach does not assume acces… (voir plus)s to any rigging or intermediate shape keyframes, produces geometrically and temporally consistent motions, and can be used to transfer long motion sequences. Central to our approach are two coupled neural networks that individually predict local geometric and temporal changes that are subsequently integrated, spatially and temporally, to produce the final animated meshes. The two networks are jointly trained, complement each other in producing spatial and temporal signals, and are supervised directly with 3D positional information. During inference, in the absence of keyframes, our method essentially solves a motion extrapolation problem. We test our setup on diverse meshes (synthetic and scanned shapes) to demonstrate its superiority in generating realistic and natural-looking animations on unseen body shapes against SoTA alternatives. Supplemental video and code are available at https://temporaljacobians.github.io/ .

2024-07-20

ArXiv (prépublication)

TutteNet: Injective 3D Deformations by Composition of 2D Mesh Deformations

Bo Sun

Thibault Groueix

Chen Song

Qixing Huang

2024-06-16

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)

MagicClay: Sculpting Meshes With Generative Neural Fields

Amir Barda

Vladimir Kim

Amit H. Bermano

Thibault Groueix

The recent developments in neural fields have brought phenomenal capabilities to the field of shape generation, but they lack crucial proper… (voir plus)ties, such as incremental control - a fundamental requirement for artistic work. Triangular meshes, on the other hand, are the representation of choice for most geometry related tasks, offering efficiency and intuitive control, but do not lend themselves to neural optimization. To support downstream tasks, previous art typically proposes a two-step approach, where first a shape is generated using neural fields, and then a mesh is extracted for further processing. Instead, in this paper we introduce a hybrid approach that maintains both a mesh and a Signed Distance Field (SDF) representations consistently. Using this representation, we introduce MagicClay - an artist friendly tool for sculpting regions of a mesh according to textual prompts while keeping other regions untouched. Our framework carefully and efficiently balances consistency between the representations and regularizations in every step of the shape optimization; Relying on the mesh representation, we show how to render the SDF at higher resolutions and faster. In addition, we employ recent work in differentiable mesh reconstruction to adaptively allocate triangles in the mesh where required, as indicated by the SDF. Using an implemented prototype, we demonstrate superior generated geometry compared to the state-of-the-art, and novel consistent control, allowing sequential prompt-based edits to the same mesh for the first time.

2024-03-04

ArXiv (prépublication)

Neural Semantic Surface Maps

Luca Morreale

Vladimir Kim

Niloy J. Mitra

2024-01-01

Comput. Graph. Forum (publié)

Explorable Mesh Deformation Subspaces from Unstructured 3D Generative Models

Arman Maesumi

Paul Guerrero

Vladimir Kim

Matthew Fisher

Siddhartha Chaudhuri

Daniel Ritchie

2023-12-11

SIGGRAPH Asia 2023 Conference Papers (publié)

DA Wand: Distortion-Aware Selection Using Neural Mesh Parameterization

Richard Liu

Vladimir Kim

Rana Hanocka

We present a neural technique for learning to select a local sub-region around a point which can be used for mesh parameterization. The moti… (voir plus)vation for our framework is driven by interactive workflows used for decaling, texturing, or painting on surfaces. Our key idea is to incorporate segmentation probabilities as weights of a classical parameterization method, implemented as a novel differentiable parameterization layer within a neural network framework. We train a segmentation network to select 3D regions that are parameterized into 2D and penalized by the resulting distortion, giving rise to segmentations which are distortion-aware. Following training, a user can use our system to interactively select a point on the mesh and obtain a large, meaningful region around the selection which induces a low-distortion parameterization. Our code11https://github.com/threedle/DA-Wand and project22https://threedle.github.io/DA-Wand/ are publicly available.

2023-06-17

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)

Isometric Energies for Recovering Injectivity in Constrained Mapping

Xingyi Du

Danny M. Kaufman

Qingnan Zhou

Shahar Kovalsky

Yajie Yan

Tao Ju

2022-11-29

ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (publié)