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.

Abstract: Described are methods and systems for training generative artificial intelligence (AI) models. A method for training a generative AI model can include obtaining a first dataset comprising an observed data sample from a data distribution of observed data and a second dataset comprising a prior sample from a prior distribution; synthesizing first data at a first time step based on the observed data sample and the prior sample; synthesizing second data at a second time step based on the observed data sample and the first data; generating, using a neural network, a first output and a second output at the target time step; and obtaining a trained generative AI model by updating parameters of the neural network, wherein the updating comprises at least computing a loss between a first distribution of the first output and a second distribution of the second output.

Abstract: A diffusion model is augmented with pseudoinverse guidance to restore data, removing artifacts and generating high-quality reconstructed data from limited, low-quality and/or noisy input data. The low-quality input data is denoised by a diffusion model and the denoised input data is combined with a guidance term to produce output data of higher-quality compared with the low-quality input data. The guidance term is a vector-Jacobian product that encourages consistency between the denoised input data and measurements after a pseudoinverse transformation. The denoising process may be applied in an iterative fashion to generate valid solutions to the inverse problem. The augmented diffusion model is a problem-agnostic (e.g., plug-and-play) denoiser that can restore data for a variety of tasks. Example image restoration tasks include denoising, JPEG denoising, deblurring, outpainting, inpainting, colorization, high-dynamic range, and super-resolution.

Abstract: Apparatuses, systems, and techniques to generate 3D models. In at least one embodiment, a 3D model, generated by a second neural network, is refined by a first neural network. In at least one embodiment, the first neural network is adjusted based on a determination made by the first neural network.

Abstract: Virtual reality and augmented reality bring increasing demand for 3D content creation. In an effort to automate the generation of 3D content, artificial intelligence-based processes have been developed. However, these processes are limited in terms of the quality of their output because they typically involve a model trained on limited 3D data thereby resulting in a model that does not generalize well to unseen objects, or a model trained on 2D data thereby resulting in a model that suffers from poor geometry due to ignorance of 3D information. The present disclosure jointly uses both 2D and 3D data to train a machine learning model to be able to generate 3D content from a single 2D image.

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.

Abstract: Apparatuses, systems, and techniques to calculate a combined loss value based on applying one or more loss functions to the plurality of samples generated by a diffusion model to update the samples to determine a synthesized motions of one or more objects.

Abstract: Apparatuses, systems, and techniques to generate images. In at least one embodiment, one or more machine learning models generate an output image based, at least in part, on calculating attention scores using time embeddings.

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.

Abstract: Techniques are disclosed herein for generating an image. The techniques include performing one or more first denoising operations based on a first machine learning model and an input image that includes a first object to generate a mask that indicates a spatial arrangement associated with a second object interacting with the first object, and performing one or more second denoising operations based on a second machine learning model, the input image, and the mask to generate an image of the second object interacting with the first object.

Abstract: Techniques are disclosed herein for generating a content item. The techniques include performing one or more first denoising operations based on an input and a first machine learning model to generate a first content item, and performing one or more second denoising operations based on the input, the first content item, and a second machine learning model to generate a second content item, where the first machine learning model is trained to denoise content items having an amount of corruption within a first corruption range, the second machine learning model is trained to denoise content items having an amount of corruption within a second corruption range, and the second corruption range is lower than the first corruption range.

Abstract: A diffusion model is augmented with pseudoinverse guidance to restore data, removing artifacts and generating high-quality reconstructed data from limited, low-quality and/or noisy input data. The low-quality input data is denoised by a diffusion model and the denoised input data is combined with a guidance term to produce output data of higher-quality compared with the low-quality input data. The guidance term is a vector-Jacobian product that encourages consistency between the denoised input data and measurements after a pseudoinverse transformation. The denoising process may be applied in an iterative fashion to generate valid solutions to the inverse problem. The augmented diffusion model is a problem-agnostic (e.g., plug-and-play) denoiser that can restore data for a variety of tasks. Example image restoration tasks include denoising, JPEG denoising, deblurring, outpainting, inpainting, colorization, high-dynamic range, and super-resolution.