Search Results for author: Rundi Wu

Found 9 papers, 7 papers with code

Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape

1 code implementation24 May 2023 Rundi Wu, Ruoshi Liu, Carl Vondrick, Changxi Zheng

Specifically, we encode the input 3D textured shape into triplane feature maps that represent the signed distance and texture fields of the input.


Zero-1-to-3: Zero-shot One Image to 3D Object

1 code implementation20 Mar 2023 Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, Carl Vondrick

We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image.

3D Reconstruction Image to 3D +2

Implicit Neural Spatial Representations for Time-dependent PDEs

no code implementations30 Sep 2022 Honglin Chen, Rundi Wu, Eitan Grinspun, Changxi Zheng, Peter Yichen Chen

Whereas classical solvers can dynamically adapt their spatial representation only by resorting to complex remeshing algorithms, our INSR approach is intrinsically adaptive.

Contact mechanics

Learning to Generate 3D Shapes from a Single Example

no code implementations5 Aug 2022 Rundi Wu, Changxi Zheng

Existing generative models for 3D shapes are typically trained on a large 3D dataset, often of a specific object category.

DeepCAD: A Deep Generative Network for Computer-Aided Design Models

1 code implementation ICCV 2021 Rundi Wu, Chang Xiao, Changxi Zheng

We present the first 3D generative model for a drastically different shape representation --- describing a shape as a sequence of computer-aided design (CAD) operations.

Listening to Sounds of Silence for Speech Denoising

1 code implementation NeurIPS 2020 Ruilin Xu, Rundi Wu, Yuko Ishiwaka, Carl Vondrick, Changxi Zheng

We introduce a deep learning model for speech denoising, a long-standing challenge in audio analysis arising in numerous applications.

Denoising Speech Denoising

Multimodal Shape Completion via Conditional Generative Adversarial Networks

1 code implementation ECCV 2020 Rundi Wu, Xuelin Chen, Yixin Zhuang, Baoquan Chen

Several deep learning methods have been proposed for completing partial data from shape acquisition setups, i. e., filling the regions that were missing in the shape.

Learning Character-Agnostic Motion for Motion Retargeting in 2D

2 code implementations5 May 2019 Kfir Aberman, Rundi Wu, Dani Lischinski, Baoquan Chen, Daniel Cohen-Or

In order to achieve our goal, we learn to extract, directly from a video, a high-level latent motion representation, which is invariant to the skeleton geometry and the camera view.

3D Reconstruction motion retargeting +2

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