Abstract: Techniques for machine-learning material data generation are described. In an example, a processing device is operable to receive a digital image depicting a material and generate a set of video frames depicting physically-based rendering properties of the material. The set of video frames is generated based on the digital image using a video generative machine-learning model. The processing device is operable to output material data based on the physically-based rendering properties of the material depicted by the set of video frames.

Abstract: In implementation of techniques for generating a re-illuminated reconstruction of an object, a computing device implements a reconstruction system to receive digital images depicting an object from different angles and a selection of an illumination direction for virtually illuminating the object. The reconstruction system determines a geometry of the object using a machine learning model based on the digital images. Based on the geometry of the object, the reconstruction system determines illumination parameters corresponding to the illumination direction. The reconstruction system renders a reconstructed virtual object that is a virtual representation of the object based on the geometry and illuminated based on the illumination parameters.

Abstract: Certain aspects and features of the present disclosure relate to providing interactive diffusion-based texture editing. For example, one or more textual prompts corresponding to an appearance of a texture can be provided. For example, a method involves accessing a texture image and a textual prompt corresponding to the texture image. The method further involves computing, using an image-conditioned diffusion model, image embeddings corresponding to the textual prompt. The method also involves defining, using the image embeddings, a varying appearance of the texture image. The varying appearance corresponds to the textual prompt. The method additionally involves presenting the varying appearance of the texture image for display in an interactive texture editing element.

Abstract: A method, apparatus, non-transitory computer readable medium, and system for image generation include obtaining a noise input and an input prompt comprising a pattern element. A coordinate frame of the noise input is shifted based on a diffusion step to obtain a shifted coordinate frame. A synthetic image is generated, using an image generation model, by denoising the noise input based on the input prompt and the shifted coordinate frame. The synthetic image comprises a repetition of the pattern element.

Abstract: Conditional procedural model generation techniques are described that enable generation of procedural models that are usable to recreate a visual appearance of an input image. A content processing system, for instance, receives a training dataset that includes a plurality of training pairs. The content processing system trains a machine learning model to generate procedural models based on input images. The content processing system then receives an input image that has a particular visual appearance. The content processing system leverages the trained machine learning model to generate a procedural model that is usable to recreate the particular visual appearance of the input digital image.

Abstract: Certain aspects and features of this disclosure relate to rendering images using target-augmented material maps. In one example, a graphics imaging application is loaded with a scene and an input material map, as well as a file for a target image. A stored, material generation prior is accessed by the graphics imaging application. This prior, as an example, is based on a pre-trained, generative adversarial network (GAN). An input material appearance from the input material map is encoded to produce a projected latent vector. The value for the projected latent vector is optimized to produce the material map that is used to render the scene, producing a material map augmented by a realistic target material appearance.

Abstract: Certain aspects and features of this disclosure relate to rendering images using target-augmented material maps. In one example, a graphics imaging application is loaded with a scene and an input material map, as well as a file for a target image. A stored, material generation prior is accessed by the graphics imaging application. This prior, as an example, is based on a pre-trained, generative adversarial network (GAN). An input material appearance from the input material map is encoded to produce a projected latent vector. The value for the projected latent vector is optimized to produce the material map that is used to render the scene, producing a material map augmented by a realistic target material appearance.

Abstract: Conditional procedural model generation techniques are described that enable generation of procedural models that are usable to recreate a visual appearance of an input image. A content processing system, for instance, receives a training dataset that includes a plurality of training pairs. The content processing system trains a machine learning model to generate procedural models based on input images. The content processing system then receives an input image that has a particular visual appearance. The content processing system leverages the trained machine learning model to generate a procedural model that is usable to recreate the particular visual appearance of the input digital image.

Abstract: Embodiments are disclosed for optimizing a material graph for replicating a material of the target image. Embodiments include receiving a target image and a material graph to be optimized for replicating a material of the target image. Embodiments include identifying a non-differentiable node of the material graph, the non-differentiable node including a set of input parameters. Embodiments include selecting a differentiable proxy from a library of the selected differentiable proxy is trained to replicate an output of the identified non-differentiable node. Embodiments include generating an optimized input parameters for the identified non-differentiable node using the corresponding trained neural network and the target image. Embodiments include replacing the set of input parameters of the non-differentiable node of the material graph with the optimized input parameters.

Abstract: Certain aspects and features of this disclosure relate to rendering images using target-augmented material maps. In one example, a graphics imaging application is loaded with a scene and an input material map, as well as a file for a target image. A stored, material generation prior is accessed by the graphics imaging application. This prior, as an example, is based on a pre-trained, generative adversarial network (GAN). An input material appearance from the input material map is encoded to produce a projected latent vector. The value for the projected latent vector is optimized to produce the material map that is used to render the scene, producing a material map augmented by a realistic target material appearance.

Abstract: A dry battery electrode plate includes: a metal current collector and a self-supporting electrode film. The metal current collector is provided with pores. The self-supporting electrode film includes a first electrode film and a second electrode film. The first electrode film is arranged on one side of the metal current collector. The second electrode film is arranged on the other side of the metal current collector facing away from the first electrode film. The first electrode film and the second electrode film are configured to be press-fit connected by an external force. The first electrode film and the second electrode film are attached to the metal current collector. The first electrode film and the second electrode film are connected to each other at positions corresponding to the pores.

Abstract: Embodiments are disclosed for optimizing a material graph for replicating a material of the target image. Embodiments include receiving a target image and a material graph to be optimized for replicating a material of the target image. Embodiments include identifying a non-differentiable node of the material graph, the non-differentiable node including a set of input parameters. Embodiments include selecting a differentiable proxy from a library of the selected differentiable proxy is trained to replicate an output of the identified non-differentiable node. Embodiments include generating an optimized input parameters for the identified non-differentiable node using the corresponding trained neural network and the target image. Embodiments include replacing the set of input parameters of the non-differentiable node of the material graph with the optimized input parameters.