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Large Vision-Language Models (LVLMs) combine computer vision and natural language processing to generate text descriptions of visual content. These models have shown remarkable progress in various applications, including image captioning, visible question answering, and image retrieval. However, despite their impressive performance, LVLMs…
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Diffusion models have shown to be very successful in producing high-quality photographs when given text suggestions. This paradigm for Text-to-picture (T2I) production has been successfully used for several downstream applications, including depth-driven picture generation and subject/segmentation identification. Two popular text-conditioned diffusion models,…
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It isn’t easy to generate detailed and realistic 3D models from a single RGB image. Researchers from Shanghai AI Laboratory, The Chinese University of Hong Kong, Shanghai Jiao Tong University, and S-Lab NTU have presented HyperDreamer to address this issue. This framework…
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Volumetric recording and realistic representation of 4D (spacetime) human performance dissolve the barriers between spectators and performers. It offers a variety of immersive VR/AR experiences, such as telepresence and tele-education. Some early systems use nonrigid registration explicitly to recreate textured models from…
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In a groundbreaking move, researchers at Meta AI have tackled the longstanding challenge of achieving high-fidelity relighting for dynamic 3D head avatars. Traditional methods have often needed to catch up when capturing the intricate details of facial expressions, especially in real-time applications…
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Transformers were first introduced and quickly rose to prominence as the primary architecture in natural language processing. More lately, they have gained immense popularity in computer vision as well. Dosovitskiy et al. demonstrated how to create effective image classifiers that beat CNN-based…
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Natural language processing (NLP) has entered a transformational period with the introduction of Large Language Models (LLMs), like the GPT series, setting new performance standards for various linguistic tasks. Autoregressive pretraining, which teaches models to forecast the most likely tokens in a…
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The problem of generating synchronized motions of objects and humans within a 3D scene has been addressed by researchers from Stanford University and FAIR Meta by introducing CHOIS. The system operates based on sparse object waypoints, an initial state of things and…
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Text-to-image diffusion models represent an intriguing field in artificial intelligence research. They aim to create lifelike images based on textual descriptions utilizing diffusion models. The process involves iteratively generating samples from a basic distribution, gradually transforming them to resemble the target image…
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The researchers from The University of Hong Kong, Alibaba Group, and Ant Group developed LivePhoto to solve the issue of temporal motions being overlooked in current text-to-video generation studies. LivePhoto enables users to animate images with text descriptions while reducing ambiguity in…