⭐TOP 10 Papers you loved - 2024⭐

👉Here the list of my posts you liked the most in 2024, thank you all 💙

𝐏𝐚𝐩𝐞𝐫𝐬:
⭐"Look Ma, no markers"
⭐T-Rex 2 Detector
⭐Models at Any Resolution

👉The full list with links: https://t.ly/GvQVy

🌳 HD Video Object Insertion 🌳

👉VideoAnydoor is a novel zero-shot video object insertion #AI with high-fidelity detail preservation and precise motion control. All-in-one: video VTON, face swapping, logo insertion, multi-region editing, etc.

👉Review https://t.ly/hyvRq
👉Paper arxiv.org/pdf/2501.01427
👉Project videoanydoor.github.io/
👉Repo TBA

⭐ Poll Alert!! ⭐

[EDIT] see below

🥮 SOTA probabilistic tracking🥮

👉ProTracker is a novel framework for robust and accurate long-term dense tracking of arbitrary points in videos. Code released under CC Attribution-NonCommercial💙

👉Review https://t.ly/YY_PH
👉Paper https://arxiv.org/pdf/2501.03220
👉Project michaelszj.github.io/protracker/
👉Code github.com/Michaelszj/pro-tracker

🧤World-Space Ego 3D Hands🧤

👉The Imperial College unveils HaWoR, a novel world-space 3D hand motion estimation for egocentric videos. The new SOTA on both cam pose estimation & hand motion reconstruction. Code under Attribution-NC-ND 4.0 Int.💙

👉Review https://t.ly/ozJn7
👉Paper arxiv.org/pdf/2501.02973
👉Project hawor-project.github.io/
👉Code github.com/ThunderVVV/HaWoR

⚽ FIFA 3D Human Pose ⚽

👉#FIFA WorldPose is a novel dataset for multi-person global pose estimation in the wild, featuring footage from the 2022 World Cup. 2.5M+ annotation, released 💙

👉Review https://t.ly/kvGVQ
👉Paper arxiv.org/pdf/2501.02771
👉Project https://lnkd.in/d5hFWpY2
👉Dataset https://lnkd.in/dAphJ9WA

🔥 Depth Any Camera (SOTA) 🔥

👉DAC is a novel and powerful zero-shot metric depth estimation framework that extends a perspective-trained model to effectively handle cams with varying FoVs (including large fisheye & 360◦). Code announced (not available yet)💙

👉Review https://t.ly/1qz4F
👉Paper arxiv.org/pdf/2501.02464
👉Project yuliangguo.github.io/depth-any-camera/
👉Repo github.com/yuliangguo/depth_any_camera

❤️‍🔥 Uncommon object in #3D ❤️‍🔥

👉#META releases uCO3D, a new object-centric dataset for 3D AI. The largest publicly-available collection of HD videos of objects with 3D annotations that ensures full-360◦ coverage. Code & data under CCA 4.0💙

👉Review https://t.ly/Z_tvA
👉Paper https://arxiv.org/pdf/2501.07574
👉Project https://uco3d.github.io/
👉Repo github.com/facebookresearch/uco3d

🏆Universal Detector-Free Match🏆

👉MatchAnything: novel detector-free universal matcher across unseen real-world single/cross-modality domains. Same weights for everything. Code announced, to be released 💙

👉Review https://t.ly/sx92L
👉Paper https://lnkd.in/dWwRwGyY
👉Project https://lnkd.in/dCwb2Yte
👉Repo https://lnkd.in/dnUXYzQ5

🆘 Help: Looking for Outstanding Speakers 🆘

👉Who would you suggest as a speaker for your ideal conference on AI (CV, LLM, RAG, ML, HW Optimization, AI & Space, etc.)? Only “hardcore” technical talks, no commercial at all. Please comment here with name, topic and affiliation (es: Paul Gascoigne, Computer Vision & Football, Scotland Team).

⭐Guaranteed tickets & more for the suggestions that will become invited speakers ;)

🧞‍♂️Omni-RGPT: SOTA MLLM Understanding🧞‍♂️

👉 #NVIDIA presents Omni-RGPT, MLLM for region-level comprehension for both images & videos. New SOTA on image/video-based commonsense reasoning.

👉Review https://t.ly/KHnQ7
👉Paper arxiv.org/pdf/2501.08326
👉Project miranheo.github.io/omni-rgpt/
👉Repo TBA soon

🔥 GAGA: Group Any Gaussians 🔥

👉GAGA is a framework that reconstructs and segments open-world 3D scenes by leveraging inconsistent 2D masks predicted by zero-shot segmentation models. Code available, recently updated💙

👉Review https://t.ly/Nk_jT
👉Paper www.gaga.gallery/static/pdf/Gaga.pdf
👉Project www.gaga.gallery/
👉Repo github.com/weijielyu/Gaga

🎁Free Book: LLM Foundations🎁

👉A fully free book just released on arXiv to outline the basic concepts of #LLMs and related techniques with a focus on the foundational aspects.

✅Chapter 1: basics of pre-training
✅Chapter 2: gen-models & LLMs
✅Chapter 3: prompting methods
✅Chapter 4: alignment methods

👉If you have any background in ML, along with a certain understanding of stuff like Transformers, this book will be "smooth". However, even without this prior knowledge, it is still perfectly fine because the contents of each chapter are self-contained.

👉Review https://t.ly/9LGCa
👉Book https://lnkd.in/d3VkswZf

🏄‍♀️ GSTAR: Gaussian Surface Tracking 🏄‍♀️

👉ETH Zurich unveils GSTAR, a novel framework for photo-realistic rendering, surface reconstruction, and 3D tracking for dynamic scenes while handling topology changes. Code announced💙

👉Review https://t.ly/udpMq
👉Paper arxiv.org/pdf/2501.10283
👉Project chengwei-zheng.github.io/GSTAR/
👉Repo TBA

🧽 Diffusion Video Inpainting 🧽

👉#Alibaba unveils a technical report about DiffuEraser, a video inpainting model based on stable diffusion, designed to fill masked regions with greater details and more coherent structures. Code & weights released under Apache💙

👉Review https://t.ly/7rEll
👉Paper arxiv.org/pdf/2501.10018
👉Project lixiaowen-xw.github.io/DiffuEraser-page/
👉Repo github.com/lixiaowen-xw/DiffuEraser

🌈 #Nvidia Foundation ZS-Stereo 🌈

👉Nvidia unveils FoundationStereo, a foundation model for stereo depth estimation with strong zero-shot generalization. In addition, a large-scale (1M stereo pairs) synthetic training dataset featuring large diversity and high photorealism. Code, model & dataset to be released💙

👉Review https://t.ly/rfBr5
👉Paper arxiv.org/pdf/2501.09898
👉Project nvlabs.github.io/FoundationStereo/
👉Repo github.com/NVlabs/FoundationStereo/tree/master

🔥 [SOTA] Long-Video Depth Anything 🔥

👉ByteDance unveils Video Depth Anything: HQ, consistent depth estimation in SUPER-long videos (over several minutes) without sacrificing efficiency. Based on Depth Anything V2 with a novel efficient spatial-temporal head. Repo available under Apache 2.0💙

👉Review https://t.ly/Q4ZZd
👉Paper arxiv.org/pdf/2501.12375
👉Project https://lnkd.in/dKNwJzbM
👉Repo https://lnkd.in/ddfwwpCj