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A video is generated in latent space by denoising 3D "patches", then transformed to standard space by a video decompressor. Re-captioning is used to augment training data, by using a video-to-text model to create detailed captions on videos. [6]
Artificially intelligent computer-aided design (CAD) can use text-to-3D, image-to-3D, and video-to-3D to automate 3D modeling. [64] AI-based CAD libraries could also be developed using linked open data of schematics and diagrams. [65] AI CAD assistants are used as tools to help streamline workflow. [66]
Once the 3D image effect has been achieved (), moving the viewer's head away from the screen increases the stereo effect even more. Moving horizontally and vertically a little also produces interesting effects. Figure 3 shows a Single Image Random Text Stereogram (SIRTS) based on the same idea as a Single Image Random Dot Stereogram . The word ...
A text-to-video model is a machine learning model that uses a natural language description as input to produce a video relevant to the input text. [1] Advancements during the 2020s in the generation of high-quality, text-conditioned videos have largely been driven by the development of video diffusion models .
A single image random text ASCII stereogram is an alternative to SIRDS using random ASCII text instead of dots to produce a 3D form of ASCII art. Map textured stereogram In a map textured stereogram, "a fitted texture is mapped onto the depth image and repeated a number of times" resulting in a pattern where the resulting 3D image is often ...
Text-to-3D using Gaussian Splatting: Applies 3D Gaussian splatting to text-to-3D generation. [10] End-to-end Autonomous Driving: Mentions 3D Gaussian splatting as a data-driven sensor simulation method for autonomous driving, highlighting its ability to generate realistic novel views of a scene. [11]