Duc Liem Dinh’s Post

🤔Can #AI help people to sleep better? BodyMap the new #method developed by Carnegie Mellon University can predict the #3D posture of the human body and its pressure in bed. BodyMap works in three steps: 1️⃣It analyzes a depth image of the body in bed and its pressure image from a special mattress 2️⃣It creates a detailed 3D model of the body, with precise shape, posture and pressure on each point 3️⃣It aligns pressure images with 3D model using AI, improving reconstruction and localization of high-pressure areas BodyMAP exceeds the #state of the art by 25% in terms of accuracy for both body mesh prediction and applied 3D pressure map, using real data on people in bed. 👉Follow us if you want to know what's new in the world of AI. NVIDIA, Abhishek Tandon, Anujraaj Goyal, Henry Clever, Zackory Erickson Paper 👉🏻 https://lnkd.in/dRrbnpYm Project 👉🏻 https://lnkd.in/d5mEa9kE

🪬🪬UHM: Authentic Hand Avatar by Phone🪬🪬 👉 META unveils UHM, novel 3D high-fidelity avatarization of your (yes, the your one) hand. Adaptation pipeline fits the pre-trained UHM via phone scan. Source Code released under CC NonCommercial 4.0💙 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅UHM a novel neural 3D hand model ✅Universal HD 3D hand meshes of arbitrary IDs ✅Adapting to each person by quick phone scan ✅UHM performs tracking & modeling at same time ✅Novel image matching loss function for skin sliding issue #artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse 👉Discussion https://lnkd.in/dMgakzWm 👉Paper https://lnkd.in/dyGaiAnq 👉Code https://lnkd.in/d9B_XFAA

The Benefits of Virtual Sensor Representation in Engineering 🎥 Watch the full episode here: https://lnkd.in/exVXCb6J 🌎 Connect with Paola on LinkedIn: https://lnkd.in/ek2YPPTx 👉 AI with Model-Based Design: https://lnkd.in/eEWKJu-3 #engineering #mathworks #artificialintelligence

Advancing the field of human mesh recovery, the introduction of GenHMR marks a significant milestone in computer vision applications, particularly in health, arts, and entertainment. Despite historical challenges in extracting 3D human poses from monocular images due to depth ambiguity and occlusions, traditional methods and probabilistic approaches have faced limitations. GenHMR revolutionizes monocular human mesh recovery by framing it as an image-conditioned generative task. This innovative framework addresses uncertainties in 2D-to-3D mapping through two key components: 1. Pose Tokenizer: Converting 3D poses into discrete tokens within a latent space. 2. Image-Conditional Masked Transformer: Learning probabilistic pose token distributions based on image cues and masked token sequences. By decoding high-confidence pose tokens iteratively and employing a 2D pose-guided refinement approach, GenHMR ensures accurate alignment of 3D body meshes with 2D pose references. The groundbreaking results speak for themselves - GenHMR surpasses existing methodologies on standard datasets, establishing a new benchmark in 3D reconstruction precision. For further insights, explore Muhammad Usama Saleem's research on GenHMR at: https://lnkd.in/dF4Tq3yY Join me as I delve into the future prospects of generative AI. Your valuable thoughts on this advancement are highly appreciated. #AI #ComputerVision #HumanMeshRecovery #GenerativeAI #ResearchInnovation

The future of AI image inpainting has arrived. BrushNet, a revolutionary dual-branch framework, guarantees coherence and enhanced outcomes for complex image inpainting tasks. By embedding masked image features into any pre-trained diffusion model, BrushNet overcomes the limitations of traditional methods and achieves superior results. With BrushNet, developers can prioritize ethical guidelines in AI development while still benefiting from the remarkable potential of diffusion models. The framework's flexible preservation scale and blurring operations offer fine-grained control and customizable options for image inpainting. How can BrushNet revolutionize the field of AI image inpainting? — Hi, 👋🏼 my name is Doug, I love AI, and I post content to keep you up to date with the latest AI news. Follow and ♻️ repost to share the information! #aiimageinpainting #brushnet #diffusionmodels

I shared an image today from a source I trust without doing a background check. This picture is widely spreading. This image is #AI-generated due to the following key factors: - Unnatural Soldier Alignment: The soldiers are positioned too uniformly, with little variation in posture or movement, which is unlikely in a real-life raid scenario. - Identical Gear: The soldiers' equipment (helmets, backpacks, and knee pads) looks almost identical, which #AI often struggles with when trying to add natural variation to objects. The shoe color is different from the ones the soldiers are wearing in the videos Al Jazeera shared. - Flat Lighting: The lighting lacks depth, with minimal shadow variation. - Distorted Text: The "Al Jazeera" text on the doors shows slight distortions, which is common in AI-generated #images. - Awkward Weapon Handling: The way some soldiers hold their weapons seems weird, as AI struggles with fine details like hand-object interactions. When it comes to AI-generated images, body parts are often a sign of manipulation. AI can struggle with rendering complex details like hands, fingers, and facial features, which may appear distorted or unnatural. For instance, hands may have the wrong number of fingers. These inconsistencies in body parts are one of the clearest ways to spot AI-generated images, as the #technology often fails to replicate the subtleties and variations found in real human anatomy.

🔫🔫 Free-Moving Object Reconstruction 🔫🔫 👉EPFL (+#MagicLeap) unveils a novel approach for reconstructing free-moving object from monocular RGB clip. Free interaction with objects in front of a moving cam without relying on any prior, and optimizes the sequence globally without any segments. EDIT: code to be released soon (author announced in the comments below). 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Joint reconstruction/pose of free-moving objects ✅Globally optimized with a single network only ✅OTS-2D segmentation for masks in each frames ✅Novel simple-but-effective strategy with virtual cam #artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse 👉Discussion https://lnkd.in/dMgakzWm 👉Paper https://lnkd.in/dYRmv9sP 👉Project https://lnkd.in/dmaiBnn3 👉Code https://lnkd.in/dJRbCu6M

🚀 Excited to share my new preprint: FaceGemma: Enhancing Image Captioning with Facial Attributes for Portrait Images 🎉 We introduce FaceGemma, a model designed to capture and describe nuanced facial attributes for more descriptive, accessible image captions. Using FaceAttdb data and fine-tuning PaliGemma with Llama 3 - 70B, we generated captions for faces, showcasing details like facial attributes (in black), face wears i.e.; eyeglasses (in green), and facial expressions (in red). Priprint: https://lnkd.in/gWS9z4X4 Results: FaceGemma achieved a BLEU-1 score of 0.364 and a METEOR score of 0.355, demonstrating the impact of facial attributes on enhanced captioning accuracy. 🌟 #AI #ComputerVision #ImageCaptioning #LLM

China’s tech firm, Shengshu Technology and Tsinghua University, has revealed Vidu, their text-to-video AI model, which can produce 16-second clips at 1080p resolution. Vidu can create videos with intricate scenarios that follow the laws of real-world physics, including lifelike lighting, shadows, and nuanced facial expressions. This model also showcases vibrant creativity, crafting imaginative content about surreal realms, with depth and intricacy. Vidu's multi-camera functionality enables the generation of dynamic sequences, smoothly shifting between wide-angle shots, close-ups, and mid-range views within a unified scene. Like and follow GenArtiFica for more updates. #ai #generativeai #texttovideo #aitools #vidu #videogeneration #monday #technology

The image shows a comparison of faces in three different versions in each row: First column: Original photos showing unmodified faces. Second column: Photos of the same individuals after applying a Gaussian blur filter, which introduces blur on the faces, simulating conditions where image quality is significantly degraded. Third column: The results produced by the MPRNet model, which is designed to remove the Gaussian blur and restore facial details. Each set demonstrates the comparison between the original images, their blurred versions, and the restored images. It's evident how MPRNet attempts to restore clarity, though in some cases the reconstructed images differ slightly in detail from the originals. In the further development of this project, after completing the work on the GAN-based MPRNet model, the next step will involve incorporating diffusion models. These models hold potential for even more precise detail restoration, particularly in tasks such as deblurring or image quality enhancement. Diffusion models provide a cutting-edge approach to generating and restoring images, using a gradual denoising process that transforms blurry input data into clear images. #AI #MachineLearning #ImageRestoration #Deblurring #ComputerVision #MPRNet #GAN #DiffusionModels #DeepLearning #ArtificialIntelligence #ImageProcessing #NeuralNetworks