🌅 Generative Human Mesh Recovery 🌅

👉GenHMR is a novel generative framework that reformulates monocular HMR as an image-conditioned generative task, explicitly modeling and mitigating uncertainties in 2D-to-3D mapping process. Impressive results but no code announced 🥺

👉Review https://t.ly/Rrzpj
👉Paper https://arxiv.org/pdf/2412.14444
👉Project m-usamasaleem.github.io/publication/GenHMR/GenHMR.html

💎AI-driven Docs Conversion💎

👉Docling by IBM, is the ALL-in-ONE, open source solution for documents; parsing several types of popular formats into a unified, richly structured representation. Powered by SOTA models for layout (DocLayNet) and table structure (TableFormer), it runs efficiently on low-cost hardware. Code under MIT💙

👉Review https://t.ly/nSCfT
👉Paper https://lnkd.in/dc5Kpc2F
👉Repo https://lnkd.in/d9gvw9bt

🈯 SOTA 0-Shot Multi-View 🈯

👉MVGD by #TOYOTA is the SOTA method that generates images and scale-consistent depth maps from novel viewpoints given an arbitrary number of posed input views. A novel diffusion-based architecture capable of direct pixel-level generation. Code announced 💙

👉Review https://t.ly/_ecKl
👉Paper arxiv.org/pdf/2501.18804
👉Project mvgd.github.io/
👉Repo TBA

🐙MambaGlue: SOTA feats. matching🐙

👉MambaGlue is a hybrid neural network combining the Mamba and the Transformer architectures to match local features. Source Code announced, to be released💙

👉Review https://shorturl.at/LxDG1
👉Paper arxiv.org/pdf/2502.00462
👉Repo https://lnkd.in/dAujfGZQ

🛸Real-Time Differentiable Tracing🛸

👉 Radiant Foam is a novel scene representation by leveraging the decades-old efficient volumetric mesh ray tracing algorithm (largely overlooked in recent research). Performing like Gaussian Splatting, without the constraints of rasterization. Code announced💙

👉Review https://shorturl.at/26U06
👉Paper https://arxiv.org/pdf/2502.01157
👉Project https://radfoam.github.io/
👉Repo https://github.com/theialab/radfoam

🔥 VideoJAM: #META's Video-Model (SOTA) 🔥

👉#META's VideoJAM: the new SOTA (by large margin) in motion coherence for video generation, much better than SORA! A strong motion prior into any video-gen model. Impressive results, no code announced🥲

👉Review https://shorturl.at/id7Bt
👉Paper https://arxiv.org/pdf/2502.02492
👉Project https://hila-chefer.github.io/videojam-paper.github.io/

👗3D Dynamic Garments👗

👉UCLA introduces Dress-1-to-3, a novel pipeline that reconstructs physics-plausible, simulation-ready separated garments with sewing patterns and humans from an in-the-wild image.

👉Review https://t.ly/qciHV
👉Paper arxiv.org/pdf/2502.03449
👉Project dress-1-to-3.github.io

🤖 META Human-Robot 🤖

👉#META PARTNR: novel benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration. The largest benchmark of its kind: 100,000+ natural language tasks, spanning 60 houses and 5,819 unique objects. Code & Data (🤗) under MIT💙

👉Review https://t.ly/zcN0K
👉Paper arxiv.org/pdf/2411.00081
👉Repo github.com/facebookresearch/partnr-planner
🤗Data huggingface.co/datasets/ai-habitat/partnr_episodes

💃HumanDiT Long-form Human💃

👉HumanDiT is a novel pose-guided Diffusion trained on a large and wild dataset w/ 14,000 hours of HQ video to produce HD videos with fine-grained bodies. Stunning results but no code announced🥲

👉Review https://t.ly/7rTRr
👉Paper https://arxiv.org/pdf/2502.04847
👉Project https://agnjason.github.io/HumanDiT-page/

🔮Flow-Based Foundation GenAI🔮

👉Goku is the novel SOTA family of joint image-and-video generation models leveraging rectified flow Transformers to achieve industry-leading performance. Amazing results! Repo released (now, empty)💙

👉Review https://t.ly/dzi0O
👉Paper https://arxiv.org/pdf/2502.04896
👉Project saiyan-world.github.io/goku/
👉Repo github.com/Saiyan-World/goku

🥛HAMSTER: Hierarchical VLA Manipulation🥛

👉#Nvidia unveils HAMSTER: novel Hierarchical VLA architecture to enable robotic manipulation with semantic, visual & geometric generalization trained on easy to collect, off-domain data. Source Code announced💙

👉Review https://t.ly/2yXaY
👉Paper https://arxiv.org/pdf/2502.05485
👉Project https://hamster-robot.github.io/
👉Repo TBA

🦶 It's all About Foot 🦶

👉 A collection of three works all about human foot: synthetic foot renders, reconstruction and surface normals. Repos & Datasets available💙

👉Review https://t.ly/GY8mL
👉Paper (last) arxiv.org/pdf/2502.06367
👉Projects www.ollieboyne.com/
👉Repo github.com/OllieBoyne/FOUND
👉Repo github.com/OllieBoyne/SynFoot
👉Repo github.com/OllieBoyne/FOCUS (coming)

🪛 Make anything "Rig-Ready" 🪛

👉RigAnything is a novel autoregressive transformer-based model, which makes 3D assets rig-ready by probabilistically generating joints, skeleton topologies, and assigning skinning weights in a template-free manner. Online demo announced💙

👉Review https://t.ly/bNwxq
👉Paper arxiv.org/pdf/2502.09615
👉Project www.liuisabella.com/RigAnything

🔥 Animate Anyone 2 🔥

👉 The evolution of the first version that enables character animation w/ environment affordance. Amazing results but no code announced 🥲

👉Review https://t.ly/iNNLB
👉Paper https://arxiv.org/pdf/2502.06145
👉Project https://humanaigc.github.io/animate-anyone-2

🔥Large Language DIFFUSION Model🔥

👉Renmin University introduces LLaDA, a DIFFUSION model trained entirely from scratch, rivaling LLaMA3 8B in performance. Pre-trained from scratch on 2.3T tokens using 0.13M H800 GPU hours, followed by SFT on 4.5M pairs. A new paradigm is born? Repo by the end of Feb.25 💙

👉Review https://t.ly/7Cnrh
👉Paper https://lnkd.in/dCWi3byk
👉Project https://lnkd.in/dB7JRYeA
👉Repo https://lnkd.in/dAqzeCHJ

🌈Unified Low-Level 4D Vision🌈

👉#Nvidia L4P is a novel feedforward, general-purpose, architecture to solve low-level 4D perception tasks in a unified framework. L4P combines a ViTbased backbone with per-task heads that are lightweight and therefore do not require extensive training. One backbone - many SOTAs. Code announced 💙

👉Review https://t.ly/04DGj
👉Paper arxiv.org/pdf/2502.13078
👉Project research.nvidia.com/labs/lpr/l4p/
👉Repo TBA

🔥 YOLOv12 is out (new SOTA) 🔥

👉YOLOv12 is a novel attention-centric YOLO framework that matches the speed of previous CNN-based ones while harnessing the performance benefits of attention mechanisms. Source Code & Demo released💙

👉Review https://t.ly/jj1oR
👉Paper arxiv.org/pdf/2502.12524
👉Repo github.com/sunsmarterjie/yolov12
🤗Demo https://t.ly/w5rno

👽Neural-Free Sparse Voxels Rasterization👽

👉#Nvidia unveils a novel efficient radiance field rendering algorithm that incorporates a rasterization process on adaptive sparse voxels without neural networks or 3D Gaussians. Code released (custom license)💙

👉Review https://t.ly/Nh_ic
👉Paper https://lnkd.in/g8k8Zs6R
👉Project https://lnkd.in/gR-bD4Wx
👉Repo https://lnkd.in/gNHX-w4t