Author: Daniele Lorenzi

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Daniele Lorenzi received his M.Sc. in ICT for Internet and Multimedia Engineering in 2021 from the University of Padua, Italy. He is a Ph.D. candidate at the Institute of Information Technology (ITEC) at the Alpen-Adria-Universität (AAU) Klagenfurt. He is currently working in the Christian Doppler Laboratory ATHENA and his research interests include adaptive video streaming, immersive media, machine learning, and QoS/QoE evaluation.

Meet POCO: A Novel Artificial Intelligence Framework for 3D Human Pose and Shape Estimation

Estimating 3D Human Pose and Shape (HPS) from photos and moving pictures is necessary to reconstruct human actions in real-world settings. Nevertheless, 3D inference...

AI Researchers from Bytedance and the King Abdullah University of Science and Technology Present a Novel Framework For Animating Hair Blowing in Still Portrait...

Hair is one of the most remarkable features of the human body, impressing with its dynamic qualities that bring scenes to life. Studies have...

Revolutionizing CPR Training With CPR-Coach: Harnessing Artificial Intelligence for Error Recognition and Assessment

Cardiopulmonary Resuscitation (CPR) is a life-saving medical procedure designed to revive individuals who have experienced cardiac arrest, meaning the heart suddenly stops beating effectively...

Meet ReVersion: A Novel AI Diffusion-Based Framework to Address the Relation Inversion Task from Images

Recently, text-to-image (T2I) diffusion models have exhibited promising outcomes, sparking explorations into numerous generative tasks. Some efforts have been made to invert pre-trained text-to-image...

Advancing Image Inpainting: Bridging the Gap Between 2D and 3D Manipulations with this Novel AI Inpainting for Neural Radiance Fields

There has been enduring interest in the manipulation of images due to its wide range of applications in content creation. One of the most...

Meet StableSR: A Novel AI Super-Resolution Approach Exploiting the Power of Pre-Trained Diffusion Models

Significant progress has been observed in the development of diffusion models for various image synthesis tasks in the field of computer vision. Prior research...

Meet BLIVA: A Multimodal Large Language Model for Better Handling of Text-Rich Visual Questions

Recently, Large Language Models (LLMs) have played a crucial role in the field of natural language understanding, showcasing remarkable capabilities in generalizing across a...

A New AI Research from Tel Aviv and the University of Copenhagen Introduces a ‘Plug-and-Play’ Approach for Rapidly Fine-Tuning Text-to-Image Diffusion Models by Using...

Text-to-image diffusion models have exhibited impressive success in generating diverse and high-quality images based on input text descriptions. Nevertheless, they encounter challenges when the...

Meet WavJourney: An AI Framework For Compositional Audio Creation With Large Language Models

The emerging field of multi-modal artificial intelligence (AI) converges visual, auditory, and textual data, offering exciting potential in various domains, from personalized entertainment to...

Unveil The Secrets Of Anatomical Segmentation With HybridGNet: An AI Encoder-Decoder For Plausible Anatomical Structures Decoding

Recent advancements in deep neural networks have enabled new approaches to address anatomical segmentation. For instance, state-of-the-art performance in the anatomical segmentation of biomedical...

Meet DenseDiffusion: A Training-free AI Technique To Address Dense Captions and Layout Manipulation In Text-to-Image Generation

Recent advancements in text-to-image models have led to sophisticated systems capable of generating high-quality images based on brief scene descriptions. Nevertheless, these models encounter...

Decoding Emotions: Unveiling Feelings And Mental States with EmoTX, A Novel Transformer-Powered AI Framework

Movies are among the most artistic expressions of stories and feelings. For instance, in "The Pursuit of Happyness," the protagonist goes through a range...