205 citations · 376 across the 26 of their papers we have counts for
22 papers · 1 filter
FourierHandFlow: Neural 4D Hand Representation Using Fourier Query Flow
Jihyun Lee, Junbong Jang, Donghwan Kim +2
Recent 4D shape representations model continuous temporal evolution of implicit shapes by (1) learning query flows without leveraging shape and articulation priors or (2) decoding…
SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions
Yuseung Lee, Kunho Kim, Hyunjin Kim +1
The remarkable capabilities of pretrained image diffusion models have been utilized not only for generating fixed-size images but also for creating panoramas. However, naive stitch…
Im2Hands: Learning Attentive Implicit Representation of Interacting Two-Hand Shapes
Jihyun Lee, Minhyuk Sung, Honggyu Choi +1
We present Implicit Two Hands (Im2Hands), the first neural implicit representation of two interacting hands. Unlike existing methods on two-hand reconstruction that rely on a param…
SALAD: Part-Level Latent Diffusion for 3D Shape Generation and Manipulation
Juil Koo, Seungwoo Yoo, Minh Hieu Nguyen +1
We present a cascaded diffusion model based on a part-level implicit 3D representation. Our model achieves state-of-the-art generation quality and also enables part-level shape edi…
LADIS: Language Disentanglement for 3D Shape Editing
Ian Huang, Panos Achlioptas, Tianyi Zhang +3
Natural language interaction is a promising direction for democratizing 3D shape design. However, existing methods for text-driven 3D shape editing face challenges in producing dec…
Seg&Struct: The Interplay Between Part Segmentation and Structure Inference for 3D Shape Parsing
Jeonghyun Kim, Kaichun Mo, Minhyuk Sung +1
We propose Seg&Struct, a supervised learning framework leveraging the interplay between part segmentation and structure inference and demonstrating their synergy in an integrated f…
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian
Jihyun Lee, Minhyuk Sung, Hyunjin Kim +1
We propose a framework that can deform an object in a 2D image as it exists in 3D space. Most existing methods for 3D-aware image manipulation are limited to (1) only changing the…
3D-GIF: 3D-Controllable Object Generation via Implicit Factorized Representations
Minsoo Lee, Chaeyeon Chung, Hojun Cho +4
While NeRF-based 3D-aware image generation methods enable viewpoint control, limitations still remain to be adopted to various 3D applications. Due to their view-dependent and ligh…
Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders
Mikaela Angelina Uy, Yen-yu Chang, Minhyuk Sung +4
We propose Point2Cyl, a supervised network transforming a raw 3D point cloud to a set of extrusion cylinders. Reverse engineering from a raw geometry to a CAD model is an essential…
PartGlot: Learning Shape Part Segmentation from Language Reference Games
Juil Koo, Ian Huang, Panos Achlioptas +2
We introduce PartGlot, a neural framework and associated architectures for learning semantic part segmentation of 3D shape geometry, based solely on part referential language. We e…
CTRL-C: Camera calibration TRansformer with Line-Classification
Jinwoo Lee, Hyunsung Go, Hyunjoon Lee +3
Single image camera calibration is the task of estimating the camera parameters from a single input image, such as the vanishing points, focal length, and horizon line. In this wor…
CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Clouds
Eric-Tuan Lê, Minhyuk Sung, Duygu Ceylan +3
Representing human-made objects as a collection of base primitives has a long history in computer vision and reverse engineering. In the case of high-resolution point cloud scans,…
DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates
Minghua Liu, Minhyuk Sung, Radomir Mech +1
We propose DeepMetaHandles, a 3D conditional generative model based on mesh deformation. Given a collection of 3D meshes of a category and their deformation handles (control points…
Joint Learning of 3D Shape Retrieval and Deformation
Mikaela Angelina Uy, Vladimir G. Kim, Minhyuk Sung +3
We propose a novel technique for producing high-quality 3D models that match a given target object image or scan. Our method is based on retrieving an existing shape from a databas…
MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization
Jiahui Huang, He Wang, Tolga Birdal +4
We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for multiple input 3D point clouds. The two non-trivial chal…
Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images
Jiahui Lei, Srinath Sridhar, Paul Guerrero +3
We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views. Previous work on learning shape…
Neural Geometric Parser for Single Image Camera Calibration
Jinwoo Lee, Minhyuk Sung, Hyunjoon Lee +1
We propose a neural geometric parser learning single image camera calibration for man-made scenes. Unlike previous neural approaches that rely only on semantic cues obtained from n…
Deformation-Aware 3D Model Embedding and Retrieval
Mikaela Angelina Uy, Jingwei Huang, Minhyuk Sung +2
We introduce a new problem of retrieving 3D models that are deformable to a given query shape and present a novel deep deformation-aware embedding to solve this retrieval task. 3D…
Learning 3D Part Assembly from a Single Image
Yichen Li, Kaichun Mo, Lin Shao +2
Autonomous assembly is a crucial capability for robots in many applications. For this task, several problems such as obstacle avoidance, motion planning, and actuator control have…
GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud
Li Yi, Wang Zhao, He Wang +2
We introduce a novel 3D object proposal approach named Generative Shape Proposal Network (GSPN) for instance segmentation in point cloud data. Instead of treating object proposal a…
Supervised Fitting of Geometric Primitives to 3D Point Clouds
Lingxiao Li, Minhyuk Sung, Anastasia Dubrovina +2
Fitting geometric primitives to 3D point cloud data bridges a gap between low-level digitized 3D data and high-level structural information on the underlying 3D shapes. As such, it…
Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions
Minhyuk Sung, Hao Su, Ronald Yu +1
Various 3D semantic attributes such as segmentation masks, geometric features, keypoints, and materials can be encoded as per-point probe functions on 3D geometries. Given a collec…