Nvidia Corporation

Mitsubishi Electric Research Laboratories, Inc.

Nvidia Corporaton

Ming-Yu Liu

Jan Kautz

Arun Mohanray Mallya

Oncel C Tuzel

Ting-Chun Wang

Xiaodong Yang

Deqing Sun

Samuli Matias Laine

David Patrick Luebke

Timo Oskari Aila

Tero Tapani Karras

Miika Samuli Aittala

Yuichi Taguchi

Ming-Hsuan Yang

Jaakko Tapani Lehtinen

Sanja Fidler

Srikumar Ramalingam

Varun Jampani

Jinwei Gu

Yi-Hsuan Tsai

Jun Gao

Orazio Gallo

Antonio Torralba Barriuso

David Jesus Acuna Marrero

Amlan Kar

Alexander Georg Keller

Karl Berntorp

Milind R Naphade

Aayush Prakash

Kangxue Yin

Teng-Yok Lee

Xitong Yang

Karan Sapra

Aurobinda Maharana

Raviteja Vemulapalli

Wei-Chih Tu

Fanyi Xiao

Chieh-Chi Kao

Kihwan Kim

Dieter Fox

Yijun Li

Arvind Raghunathan

Avishai Weiss

John R Zedlewski

Jonathan Tremblay

Sifei Liu

Alejandro Jose Troccoli

Chen Feng

Pradeep Sen

Arash Vahdat

Zhiding Yu

Cuneyt Oncel Tuzel

Seung-Hwan Baek

Abhijit Patait

Amir Massoud Farahmand

Huaijin Chen

Shuo Wang

Sergey Tulyakov

Zekun Hao

Alex John Bauld Evans

Tianchang Shen

Aysegul Dundar

Zhongheng Ren

Tim Brooks

Tero Karras

Siddharth Gururani

Kevin Lin

Jaakko Lehtinen

Thomas Müller-Höhne

Chen-Hsuan Lin

Jose Rafael Valle Da Costa

Vignesh Ungrapalli

Philip Ammirato

Kuniaki Saito

Donghoon Lee

Zhaoshuo Li

Changhyun Choi

Abhishek Sharma

Kilho Son

Kota Hara

Tingting Huang

Jason Ren

Apparatuses, systems, and techniques to facilitate application of a style, for which one or more neural networks have not been trained by a training framework, from one image to content of another image. In at least one embodiment, a styled output image is generated by one or more neural networks based on a style contained in a style image and content of a content image where said one or more neural networks have not been trained by a training framework on said style.

Content-aware style encoding using neural networks

A method, computer readable medium, and system are disclosed for action video generation. The method includes the steps of generating, by a recurrent neural network, a sequence of motion vectors from a first set of random variables and receiving, by a generator neural network, the sequence of motion vectors and a content vector sample. The sequence of motion vectors and the content vector sample are sampled by the generator neural network to produce a video clip.

System and method for content and motion controlled action video generation

Apparatuses, systems, and techniques to perform compression of video data using neural networks to facilitate video streaming, such as video conferencing. In at least one embodiment, a sender transmits to a receiver a key frame from video data and one or more keypoints identified by a neural network from said video data, and a receiver reconstructs video data using said key frame and one or more received keypoints.

Video compression using neural networks

In various examples, a generative model is used to synthesize datasets for use in training a downstream machine learning model to perform an associated task. The synthesized datasets may be generated by sampling a scene graph from a scene grammar—such as a probabilistic grammar— and applying the scene graph to the generative model to compute updated scene graphs more representative of object attribute distributions of real-world datasets. The downstream machine learning model may be validated against a real-world validation dataset, and the performance of the model on the real-world validation dataset may be used as an additional factor in further training or fine-tuning the generative model for generating the synthesized datasets specific to the task of the downstream machine learning model.

Learning to generate synthetic datasets for training neural networks

One embodiment of a method includes applying a first generator model to a semantic representation of an image to generate an affine transformation, where the affine transformation represents a bounding box associated with at least one region within the image. The method further includes applying a second generator model to the affine transformation and the semantic representation to generate a shape of an object. The method further includes inserting the object into the image based on the bounding box and the shape.

Context-aware synthesis and placement of object instances

Apparatuses, systems, and techniques to determine orientation of an objects in an image. In at least one embodiment, images are processed using a neural network trained to determine orientation of an object.

Determining object orientation from an image with machine learning

Apparatuses, systems, and techniques to detect object in images including digital representations of those objects. In at least one embodiment, one or more objects are detected in an image based, at least in part, on one or more pseudo-labels corresponding to said one or more objects.

Weakly-supervised object detection using one or more neural networks

Apparatuses, systems, and techniques are presented to synthesize consistent images or video. In at least one embodiment, one or more neural networks are used to generate one or more second images based, at least in part, on one or more point cloud representations of one or more first images.

Video synthesis using one or more neural networks

In various examples, a deep three-dimensional (3D) conditional generative model is implemented that can synthesize high resolution 3D shapes using simple guides—such as coarse voxels, point clouds, etc.—by marrying implicit and explicit 3D representations into a hybrid 3D representation. The present approach may directly optimize for the reconstructed surface, allowing for the synthesis of finer geometric details with fewer artifacts. The systems and methods described herein may use a deformable tetrahedral grid that encodes a discretized signed distance function (SDF) and a differentiable marching tetrahedral layer that converts the implicit SDF representation to an explicit surface mesh representation. This combination allows joint optimization of the surface geometry and topology as well as generation of the hierarchy of subdivisions using reconstruction and adversarial losses defined explicitly on the surface mesh.

Synthesizing high resolution 3D shapes from lower resolution representations for synthetic data generation systems and applications

Apparatuses, systems, and techniques to generate pixels based on other pixels. In at least one embodiment, one or more neural networks are used to generate one or more pixels based, at least in part, on sets of pixels surrounding the one or more pixels.

Neural networks to generate pixels

Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images based, at least in part, upon one or more semantic features projected from a three-dimensional environment.

Generating images of virtual environments using one or more neural networks

Apparatuses, systems, and techniques are presented to generate digital content. In at least one embodiment, one or more neural networks are used to generate video information based at least in part upon voice information and a combination of image features and facial landmarks corresponding to one or more images of a person.

Speech-driven animation using one or more neural networks

Apparatuses, systems, and techniques to transfer grammar between sentences. In at least one embodiment, one or more first sentences are translated into one or more second sentences having different grammar using one or more neural networks.

Grammar transfer using one or more neural networks

Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more neural networks are used to generate one or more images of one or more objects based, at least in part, on input indicating motion of the one or more objects.

Generating images of object motion using one or more neural networks

In at least one embodiment, differentiable neural architecture search and reinforcement learning are combined under one framework to discover network architectures with desired properties such as high accuracy, low latency, or both. In at least one embodiment, an objective function for search based on generalization error prevents the selection of architectures prone to overfitting.

Machine-learning-based architecture search method for a neural network

Growing community of inventors

Sunnyvale, CA, United States of America

Ming-Yu Liu