Search Results for author: Matthew Fisher

Found 33 papers, 25 papers with code

Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly

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.

3D Shape Reconstruction Retrieval

Spotting Temporally Precise, Fine-Grained Events in Video

2 code implementations20 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).

Action Detection Action Spotting +1

ASSET: Autoregressive Semantic Scene Editing with Transformers at High Resolutions

1 code implementation24 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.

Semantic Segmentation Vocal Bursts Intensity Prediction

Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency

1 code implementation21 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 +1

Neural Strokes: Stylized Line Drawing of 3D Shapes

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.

Font Completion and Manipulation by Cycling Between Multi-Modality Representations

1 code implementation30 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.

Image-to-Image Translation Representation Learning +2

MarioNette: Self-Supervised Sprite Learning

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.

CharacterGAN: Few-Shot Keypoint Character Animation and Reposing

1 code implementation5 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.

LayoutGMN: Neural Graph Matching for Structural Layout Similarity

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.

Graph Matching Metric Learning +1

COALESCE: Component Assembly by Learning to Synthesize Connections

no code implementations5 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.

Coupling Explicit and Implicit Surface Representations for Generative 3D Modeling

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.

Surface Reconstruction

Modeling Artistic Workflows for Image Generation and Editing

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.

Image Generation

Improved Techniques for Training Single-Image GANs

3 code implementations25 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.

Image Generation single-image-generation

UprightNet: Geometry-Aware Camera Orientation Estimation from Single Images

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.

Camera Calibration

Learning elementary structures for 3D shape generation and matching

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 #7 on 3D Dense Shape Correspondence on SHREC'19 (using extra training data)

3D Dense Shape Correspondence 3D Shape Generation +1

Deep Parametric Shape Predictions using Distance Fields

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.

BAE-NET: Branched Autoencoder for Shape Co-Segmentation

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.

One-Shot Learning Representation Learning

3D-CODED: 3D Correspondences by Deep Deformation

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.

3D-CODED : 3D Correspondences by Deep Deformation

1 code implementation13 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 #8 on 3D Dense Shape Correspondence on SHREC'19 (using extra training data)

3D Dense Shape Correspondence 3D Human Pose Estimation +2

Multi-Content GAN for Few-Shot Font Style Transfer

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.

Font Style Transfer

Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging

no code implementations22 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.

3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions

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.

3D Reconstruction Point Cloud Registration

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