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    PHYSICS-GUIDED MOTION DIFFUSION MODEL
    1.
    发明公开
    PHYSICS-GUIDED MOTION DIFFUSION MODEL 审中-公开

    公开(公告)号：US20240169636A1

    公开(公告)日：2024-05-23

    申请号：US18317378

    申请日：2023-05-15

    Applicant: NVIDIA Corporation

    Inventor： Ye Yuan ,  Jiaming Song ,  Umar Iqbal ,  Arash Vahdat ,  Jan Kautz

    IPC: G06T13/40 ,  G06T5/00 ,  G06T13/80

    CPC classification number: G06T13/40 ,  G06T5/002 ,  G06T13/80 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: Systems and methods are disclosed that improve performance of synthesized motion generated by a diffusion neural network model. A physics-guided motion diffusion model incorporates physical constraints into the diffusion process to model the complex dynamics induced by forces and contact. Specifically, a physics-based motion projection module uses motion imitation in a physics simulator to project the denoised motion of a diffusion step to a physically plausible motion. The projected motion is further used in the next diffusion iteration to guide the denoising diffusion process. The use of physical constraints in the physics-guided motion diffusion model iteratively pulls the motion toward a physically-plausible space, reducing artifacts such as floating, foot sliding, and ground penetration.

    DIFFUSION-BASED OPEN-VOCABULARY SEGMENTATION
    2.
    发明公开
    DIFFUSION-BASED OPEN-VOCABULARY SEGMENTATION 审中-公开

    公开(公告)号：US20240153093A1

    公开(公告)日：2024-05-09

    申请号：US18310414

    申请日：2023-05-01

    Applicant: NVIDIA Corporation

    Inventor： Jiarui Xu ,  Shalini De Mello ,  Sifei Liu ,  Arash Vahdat ,  Wonmin Byeon

    IPC: G06T7/10 ,  G06V10/40

    CPC classification number: G06T7/10 ,  G06V10/40 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: An open-vocabulary diffusion-based panoptic segmentation system is not limited to perform segmentation using only object categories seen during training, and instead can also successfully perform segmentation of object categories not seen during training and only seen during testing and inferencing. In contrast with conventional techniques, a text-conditioned diffusion (generative) model is used to perform the segmentation. The text-conditioned diffusion model is pre-trained to generate images from text captions, including computing internal representations that provide spatially well-differentiated object features. The internal representations computed within the diffusion model comprise object masks and a semantic visual representation of the object. The semantic visual representation may be extracted from the diffusion model and used in conjunction with a text representation of a category label to classify the object. Objects are classified by associating the text representations of category labels with the object masks and their semantic visual representations to produce panoptic segmentation data.

    LANDMARK DETECTION WITH AN ITERATIVE NEURAL NETWORK
    3.
    发明公开
    LANDMARK DETECTION WITH AN ITERATIVE NEURAL NETWORK 审中-公开

    公开(公告)号：US20240096115A1

    公开(公告)日：2024-03-21

    申请号：US18243555

    申请日：2023-09-07

    Applicant: NVIDIA Corporation

    Inventor： Pavlo Molchanov ,  Jan Kautz ,  Arash Vahdat ,  Hongxu Yin ,  Paul Micaelli

    IPC: G06V20/59 ,  G06T7/70 ,  G06V10/82 ,  G06V20/70 ,  G06V40/16

    CPC classification number: G06V20/597 ,  G06T7/70 ,  G06V10/82 ,  G06V20/70 ,  G06V40/171 ,  G06T2207/30201 ,  G06V2201/07

    Abstract: Landmark detection refers to the detection of landmarks within an image or a video, and is used in many computer vision tasks such emotion recognition, face identity verification, hand tracking, gesture recognition, and eye gaze tracking. Current landmark detection methods rely on a cascaded computation through cascaded networks or an ensemble of multiple models, which starts with an initial guess of the landmarks and iteratively produces corrected landmarks which match the input more finely. However, the iterations required by current methods typically increase the training memory cost linearly, and do not have an obvious stopping criteria. Moreover, these methods tend to exhibit jitter in landmark detection results for video. The present disclosure improves current landmark detection methods by providing landmark detection using an iterative neural network. Furthermore, when detecting landmarks in video, the present disclosure provides for a reduction in jitter due to reuse of previous hidden states from previous frames.

    JOINT REPRESENTATION LEARNING FROM IMAGES AND TEXT
    5.
    发明申请
    JOINT REPRESENTATION LEARNING FROM IMAGES AND TEXT 有权

    公开(公告)号：US20210056353A1

    公开(公告)日：2021-02-25

    申请号：US17000048

    申请日：2020-08-21

    Applicant: Nvidia Corporation

    Inventor： Arash Vahdat ,  Tanmay Gupta ,  Xiaodong Yang ,  Jan Kautz

    IPC: G06K9/62 ,  G06N3/08

    Abstract: The disclosure provides a framework or system for learning visual representation using a large set of image/text pairs. The disclosure provides, for example, a method of visual representation learning, a joint representation learning system, and an artificial intelligence (AI) system that employs one or more of the trained models from the method or system. The AI system can be used, for example, in autonomous or semi-autonomous vehicles. In one example, the method of visual representation learning includes: (1) receiving a set of image embeddings from an image representation model and a set of text embeddings from a text representation model, and (2) training, employing mutual information, a critic function by learning relationships between the set of image embeddings and the set of text embeddings.

    VARIATIONAL INFERENCING BY A DIFFUSION MODEL
    6.
    发明申请
    VARIATIONAL INFERENCING BY A DIFFUSION MODEL 有权

    公开(公告)号：US20250045892A1

    公开(公告)日：2025-02-06

    申请号：US18593742

    申请日：2024-03-01

    Applicant: NVIDIA Corporation

    Inventor： Morteza Mardani ,  Jiaming Song ,  Jan Kautz ,  Arash Vahdat

    IPC: G06T7/00 ,  G06T3/40 ,  G06T5/70 ,  G06T5/73 ,  G06T5/77

    Abstract: Diffusion models are machine learning algorithms that are uniquely trained to generate high-quality data from an input lower-quality data. For example, they can be trained in the image domain, for example, to perform specific image restoration tasks, such as inpainting (e.g. completing an incomplete image), deblurring (e.g. removing blurring from an image), and super-resolution (e.g. increasing a resolution of an image), or they can be trained to perform image rendering tasks, including 2D-to-3D image generation tasks. However, current approaches to training diffusion models only allow the models to be optimized for a specific task such that they will not achieve high-quality results when used for other tasks. The present disclosure provides a diffusion model that uses variational inferencing to approximate a distribution of data, which allows the diffusion model to universally solve different tasks without having to be re-trained specifically for each task.

    FAIRNESS-BASED NEURAL NETWORK MODEL TRAINING USING REAL AND GENERATED DATA
    7.
    发明公开
    FAIRNESS-BASED NEURAL NETWORK MODEL TRAINING USING REAL AND GENERATED DATA 审中-公开

    公开(公告)号：US20240144000A1

    公开(公告)日：2024-05-02

    申请号：US18307227

    申请日：2023-04-26

    Applicant: NVIDIA Corporation

    Inventor： Yuji Roh ,  Weili Nie ,  De-An Huang ,  Arash Vahdat ,  Animashree Anandkumar

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: A neural network model is trained for fairness and accuracy using both real and synthesized training data, such as images. During training a first sampling ratio between the real and synthesized training data is optimized. The first sampling ratio may comprise a value for each group (or attribute), where each value is optimized. A second sampling ratio defines relative amounts of training data that are used for each one of the groups. Furthermore, a neural network model accuracy and a fairness metric are both used for updating the first and second sampling ratios during training iterations. The neural network model may be trained using different classes of training data. The second sampling ratio may vary for each class.

    DENOISING DIFFUSION GENERATIVE ADVERSARIAL NETWORKS
    10.
    发明申请
    DENOISING DIFFUSION GENERATIVE ADVERSARIAL NETWORKS 有权

    公开(公告)号：US20230095092A1

    公开(公告)日：2023-03-30

    申请号：US17957143

    申请日：2022-09-30

    Applicant: Nvidia Corporation

    Inventor： Zhisheng Xiao ,  Karsten Kreis ,  Arash Vahdat

    IPC: G06T5/00

    Abstract: Apparatuses, systems, and techniques are presented to train and utilize one or more neural networks. A denoising diffusion generative adversarial network (denoising diffusion GAN) reduces a number of denoising steps during a reverse process. The denoising diffusion GAN does not assume a Gaussian distribution for large steps of the denoising process and applies a multi-model model to permit denoising with fewer steps. Systems and methods further minimize a divergence between a diffused real data distribution and a diffused generator distribution over several timesteps. Accordingly, various embodiments may enable faster sample generation, in which the samples are generated from noise using the denoising diffusion GAN.

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