computer-vision-9

Image to 3D

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Collaborative Neural Rendering using Anime Character Sheets

Zuzeng Lin, Ailin Huang, Zhewei Huang, Chen Hu, Shuchang Zhou•Mon Jul 11 2022

This paper presents the Collaborative Neural Rendering (CoNR) method, which creates new images for specified poses from a few reference images (AKA Character Sheets), and collects a character sheet dataset containing over 700,000 hand-drawn and synthesized images of diverse poses to facilitate research in this area.

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Let's Transfer Transformations of Shared Semantic Representations

James Hays, Nam S. Vo, Lu Jiang•Fri Mar 01 2019

This work shows how one can learn transformations with no training examples by learning them on another domain and then transfer to the target domain, and demonstrates this on an image retrieval task where search query is an image, plus an additional transformation specification.

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SyncDreamer: Generating Multiview-consistent Images from a Single-view Image

Lingjie Liu, Yuan Liu, Chu-Hsing Lin, Zijiao Zeng, Xiaoxiao Long, Taku Komura, Wenping Wang•Wed Sep 06 2023

Experiments show that SyncDreamer generates images with high consistency across different views, thus making it well-suited for various 3D generation tasks such as novel-view-synthesis, text-to-3D, and image-to -3D.

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Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting

Lingzhe Zhao, Peidong Liu, Zhiqi Li, Yiming Chen•Thu Mar 14 2024

Multi-view ControlNet (MVControl) is introduced, a novel neural network architecture designed to enhance existing pre-trained multi-view diffusion models by integrating additional input conditions, such as edge, depth, normal, and scribble maps, to address controllable text-to-3D generation.

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InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models

Jiale Xu, Weihao Cheng, Yiming Gao, Xintao Wang, Shenghua Gao, Ying Shan•Tue Apr 09 2024

InstantMesh is presented, a feed-forward framework for instant 3D mesh generation from a single image, featuring state-of-the-art generation quality and significant training scalability, and is able to create diverse 3D assets within 10 seconds.

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Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking

Arjun Jain, Abhishek Sharma, Saurabh Sharma, Pavan Teja Varigonda, Prashast Bindal•Mon Apr 01 2019

A Deep Conditional Variational Autoencoder based model that synthesizes diverse anatomically plausible 3D-pose samples conditioned on the estimated 2D- pose is proposed, and it is shown that CVAE-based 3d-pose sample set is consistent with the 2D to 3D lifting and helps tackling the inherent ambiguity in2D-to-3D lifting.

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Local Aggressive Adversarial Attacks on 3D Point Cloud

Ruonan Li, Yiming Sun, F. Chen, Mingjie Wang•Tue May 18 2021

A flow of aggressive optimization strategies are developed to reinforce the unperceptive generation of adversarial examples toward misleading victim models, and a local aggressive adversarial attacks (L3A) is proposed to solve above issues.

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ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation

N. Navab, D. Stricker, Yongzhi Su, J. Rambach, Benjamin Busam, Federico Tombari, Mahdi Saleh, Torben Fetzer•Wed Mar 16 2022

This work presents a discrete descriptor, which can represent the object surface densely by incorporating a hierarchical binary grouping, and proposes a coarse to fine training strategy, which enables fine-grained correspondence prediction of the 6DoF pose.

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NeuralLift-360: Lifting an in-the-Wild 2D Photo to A 3D Object with 360° Views

Zhiwen Fan, Dejia Xu, Peihao Wang, Yi Wang, Zhangyang Wang•Mon Nov 28 2022

This work proposes a novel framework, dubbed NeuralLift-360, that utilizes a depth-aware neural radiance representation (NeRF) and learns to craft the scene guided by denoising diffusion models and can be guided with rough depth estimation in the wild.

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