no code implementations • 4 Feb 2025 • Nissim Maruani, Wang Yifan, Matthew Fisher, Pierre Alliez, Mathieu Desbrun
This paper proposes ShapeShifter, a new 3D generative model that learns to synthesize shape variations based on a single reference model.
no code implementations • 21 Dec 2024 • Sanghyun Son, Matheus Gadelha, Yang Zhou, Matthew Fisher, Zexiang Xu, Yi-Ling Qiao, Ming C. Lin, Yi Zhou
Recent probabilistic methods for 3D triangular meshes capture diverse shapes by differentiable mesh connectivity, but face high computational costs with increased shape details.
no code implementations • 17 Dec 2024 • Aditya Ganeshan, Thibault Groueix, Paul Guerrero, Radomír Měch, Matthew Fisher, Daniel Ritchie
But editing pattern images is tricky: desired edits are often programmatic: structure-aware edits that alter the underlying program which generates the pattern.
no code implementations • 20 Jul 2024 • Sanjeev Muralikrishnan, Niladri Shekhar Dutt, Siddhartha Chaudhuri, Noam Aigerman, Vladimir Kim, Matthew Fisher, Niloy J. Mitra
We introduce Temporal Residual Jacobians as a novel representation to enable data-driven motion transfer.
no code implementations • 15 Jul 2024 • Chenxi Liu, Siqi Wang, Matthew Fisher, Deepali Aneja, Alec Jacobson
Effective representation of 2D images is fundamental in digital image processing, where traditional methods like raster and vector graphics struggle with sharpness and textural complexity respectively.
no code implementations • CVPR 2024 • Vikas Thamizharasan, Difan Liu, Matthew Fisher, Nanxuan Zhao, Evangelos Kalogerakis, Michal Lukac
The success of denoising diffusion models in representing rich data distributions over 2D raster images has prompted research on extending them to other data representations, such as vector graphics.
no code implementations • CVPR 2024 • Cusuh Ham, Matthew Fisher, James Hays, Nicholas Kolkin, Yuchen Liu, Richard Zhang, Tobias Hinz
We present personalized residuals and localized attention-guided sampling for efficient concept-driven generation using text-to-image diffusion models.
no code implementations • 25 Apr 2024 • Arman Maesumi, Dylan Hu, Krishi Saripalli, Vladimir G. Kim, Matthew Fisher, Sören Pirk, Daniel Ritchie
Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation.
1 code implementation • CVPR 2024 • Ta-Ying Cheng, Matheus Gadelha, Thibault Groueix, Matthew Fisher, Radomir Mech, Andrew Markham, Niki Trigoni
We do this by engineering special sets of input tokens that can be transformed in a continuous manner -- we call them Continuous 3D Words.
no code implementations • 19 Dec 2023 • James Hong, Lu Yuan, Michaël Gharbi, Matthew Fisher, Kayvon Fatahalian
How to frame (or crop) a photo often depends on the image subject and its context; e. g., a human portrait.
no code implementations • CVPR 2024 • Vikas Thamizharasan, Difan Liu, Shantanu Agarwal, Matthew Fisher, Michael Gharbi, Oliver Wang, Alec Jacobson, Evangelos Kalogerakis
We present VecFusion, a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions.
1 code implementation • 24 Nov 2023 • Manuel Ladron De Guevara, Matthew Fisher, Aaron Hertzmann
We introduce a novel image-to-painting method that facilitates the creation of large-scale, high-fidelity paintings with human-like quality and stylistic variation.
no code implementations • 11 Oct 2023 • Arman Maesumi, Paul Guerrero, Vladimir G. Kim, Matthew Fisher, Siddhartha Chaudhuri, Noam Aigerman, Daniel Ritchie
Deep generative models of 3D shapes often feature continuous latent spaces that can, in principle, be used to explore potential variations starting from a set of input shapes.
1 code implementation • CVPR 2023 • Ying-Tian Liu, Zhifei Zhang, Yuan-Chen Guo, Matthew Fisher, Zhaowen Wang, Song-Hai Zhang
Automatic generation of fonts can be an important aid to typeface design.
no code implementations • CVPR 2023 • Xianghao Xu, Paul Guerrero, Matthew Fisher, Siddhartha Chaudhuri, Daniel Ritchie
We instead propose to decompose shapes using a library of 3D parts provided by the user, giving full control over the choice of parts.
1 code implementation • CVPR 2023 • Titas Anciukevičius, Zexiang Xu, Matthew Fisher, Paul Henderson, Hakan Bilen, Niloy J. Mitra, Paul Guerrero
In this paper, we present RenderDiffusion, the first diffusion model for 3D generation and inference, trained using only monocular 2D supervision.
2 code implementations • 20 Jul 2022 • James Hong, Haotian Zhang, Michaël Gharbi, Matthew Fisher, Kayvon Fatahalian
We introduce the task of spotting temporally precise, fine-grained events in video (detecting the precise moment in time events occur).
Ranked #6 on
Action Spotting
on SoccerNet-v2
1 code implementation • CVPR 2022 • Zhan Xu, Matthew Fisher, Yang Zhou, Deepali Aneja, Rushikesh Dudhat, Li Yi, Evangelos Kalogerakis
Rigged puppets are one of the most prevalent representations to create 2D character animations.
1 code implementation • 24 May 2022 • Difan Liu, Sandesh Shetty, Tobias Hinz, Matthew Fisher, Richard Zhang, Taesung Park, Evangelos Kalogerakis
We present ASSET, a neural architecture for automatically modifying an input high-resolution image according to a user's edits on its semantic segmentation map.
1 code implementation • 21 Apr 2022 • Tom Monnier, Matthew Fisher, Alexei A. Efros, Mathieu Aubry
Approaches for single-view reconstruction typically rely on viewpoint annotations, silhouettes, the absence of background, multiple views of the same instance, a template shape, or symmetry.
3D Object Reconstruction From A Single Image
3D Reconstruction
+2
1 code implementation • ICCV 2021 • Difan Liu, Matthew Fisher, Aaron Hertzmann, Evangelos Kalogerakis
We show that, in contrast to previous image-based methods, the use of a geometric representation of 3D shape and 2D strokes allows the model to transfer important aspects of shape and texture style while preserving contours.
1 code implementation • ICCV 2021 • James Hong, Matthew Fisher, Michaël Gharbi, Kayvon Fatahalian
This leads to poor accuracy when downstream tasks, such as action recognition, depend on pose.
1 code implementation • 30 Aug 2021 • Ye Yuan, Wuyang Chen, Zhaowen Wang, Matthew Fisher, Zhifei Zhang, Zhangyang Wang, Hailin Jin
The novel graph constructor maps a glyph's latent code to its graph representation that matches expert knowledge, which is trained to help the translation task.
1 code implementation • CVPR 2022 • Sanjeev Muralikrishnan, Siddhartha Chaudhuri, Noam Aigerman, Vladimir Kim, Matthew Fisher, Niloy Mitra
We investigate the problem of training generative models on a very sparse collection of 3D models.
1 code implementation • NeurIPS 2021 • Pradyumna Reddy, Zhifei Zhang, Matthew Fisher, Hailin Jin, Zhaowen Wang, Niloy J. Mitra
Fonts are ubiquitous across documents and come in a variety of styles.
1 code implementation • NeurIPS 2021 • Dmitriy Smirnov, Michael Gharbi, Matthew Fisher, Vitor Guizilini, Alexei A. Efros, Justin Solomon
Artists and video game designers often construct 2D animations using libraries of sprites -- textured patches of objects and characters.
1 code implementation • 5 Feb 2021 • Tobias Hinz, Matthew Fisher, Oliver Wang, Eli Shechtman, Stefan Wermter
Our model generates novel poses based on keypoint locations, which can be modified in real time while providing interactive feedback, allowing for intuitive reposing and animation.
1 code implementation • CVPR 2021 • Zhiqin Chen, Vladimir G. Kim, Matthew Fisher, Noam Aigerman, Hao Zhang, Siddhartha Chaudhuri
During testing, a style code is fed into the generator to condition the refinement.
1 code implementation • CVPR 2021 • Akshay Gadi Patil, Manyi Li, Matthew Fisher, Manolis Savva, Hao Zhang
In particular, retrieval results by our network better match human judgement of structural layout similarity compared to both IoUs and other baselines including a state-of-the-art method based on graph neural networks and image convolution.
no code implementations • 5 Aug 2020 • Kangxue Yin, Zhiqin Chen, Siddhartha Chaudhuri, Matthew Fisher, Vladimir G. Kim, Hao Zhang
We introduce COALESCE, the first data-driven framework for component-based shape assembly which employs deep learning to synthesize part connections.
no code implementations • ECCV 2020 • Omid Poursaeed, Matthew Fisher, Noam Aigerman, Vladimir G. Kim
We propose a novel neural architecture for representing 3D surfaces, which harnesses two complementary shape representations: (i) an explicit representation via an atlas, i. e., embeddings of 2D domains into 3D; (ii) an implicit-function representation, i. e., a scalar function over the 3D volume, with its levels denoting surfaces.
1 code implementation • ECCV 2020 • Hung-Yu Tseng, Matthew Fisher, Jingwan Lu, Yijun Li, Vladimir Kim, Ming-Hsuan Yang
People often create art by following an artistic workflow involving multiple stages that inform the overall design.
3 code implementations • 25 Mar 2020 • Tobias Hinz, Matthew Fisher, Oliver Wang, Stefan Wermter
Recently there has been an interest in the potential of learning generative models from a single image, as opposed to from a large dataset.
no code implementations • ICCV 2019 • Wenqi Xian, Zhengqi Li, Matthew Fisher, Jonathan Eisenmann, Eli Shechtman, Noah Snavely
We introduce UprightNet, a learning-based approach for estimating 2DoF camera orientation from a single RGB image of an indoor scene.
3 code implementations • NeurIPS 2019 • Theo Deprelle, Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape.
Ranked #9 on
3D Dense Shape Correspondence
on SHREC'19
(using extra training data)
no code implementations • 6 Jul 2019 • Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
We propose a self-supervised approach to deep surface deformation.
1 code implementation • CVPR 2020 • Dmitriy Smirnov, Matthew Fisher, Vladimir G. Kim, Richard Zhang, Justin Solomon
Many tasks in graphics and vision demand machinery for converting shapes into consistent representations with sparse sets of parameters; these representations facilitate rendering, editing, and storage.
1 code implementation • ICCV 2019 • Zhiqin Chen, Kangxue Yin, Matthew Fisher, Siddhartha Chaudhuri, Hao Zhang
The unsupervised BAE-NET is trained with a collection of un-segmented shapes, using a shape reconstruction loss, without any ground-truth labels.
1 code implementation • CVPR 2019 • Chen-Hsuan Lin, Oliver Wang, Bryan C. Russell, Eli Shechtman, Vladimir G. Kim, Matthew Fisher, Simon Lucey
In this paper, we address the problem of 3D object mesh reconstruction from RGB videos.
no code implementations • ECCV 2018 • Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
By predicting this feature for a new shape, we implicitly predict correspondences between this shape and the template.
1 code implementation • 13 Jun 2018 • Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
By predicting this feature for a new shape, we implicitly predict correspondences between this shape and the template.
Ranked #10 on
3D Dense Shape Correspondence
on SHREC'19
(using extra training data)
no code implementations • CVPR 2018 • Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
We introduce a method for learning to generate the surface of 3D shapes.
3 code implementations • 15 Feb 2018 • Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
We introduce a method for learning to generate the surface of 3D shapes.
Ranked #1 on
Point Cloud Completion
on Completion3D
6 code implementations • CVPR 2018 • Samaneh Azadi, Matthew Fisher, Vladimir Kim, Zhaowen Wang, Eli Shechtman, Trevor Darrell
In this work, we focus on the challenge of taking partial observations of highly-stylized text and generalizing the observations to generate unobserved glyphs in the ornamented typeface.
no code implementations • 22 Apr 2016 • Zachary DeVito, Michael Mara, Michael Zollhöfer, Gilbert Bernstein, Jonathan Ragan-Kelley, Christian Theobalt, Pat Hanrahan, Matthew Fisher, Matthias Nießner
Many graphics and vision problems can be expressed as non-linear least squares optimizations of objective functions over visual data, such as images and meshes.
2 code implementations • CVPR 2017 • Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, Thomas Funkhouser
To amass training data for our model, we propose a self-supervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions.
Ranked #2 on
3D Reconstruction
on Scan2CAD