🌩️ It's "Time-to-Move" 🌩️

👉Technion + Nvidia Time-to-Move (TTM) is a training-free, plug-and-play framework for motion- and appearance-controlled video generation with I2V diffusion models (Wan 2.2, CogVideoX, & Stable VD). Impressive results!

👉Review https://t.ly/0pwXm
👉Paper https://lnkd.in/dxD3uHYb
👉Project https://lnkd.in/dcE5juyM
👉Repo https://lnkd.in/dMMUjybJ

⌚ Multi-Shot Video Segmentation ⌚

👉Fudan focuses on an underexplored task of multi-shot video object segmentation (MVOS). Benchmark and repo available (the extension part of SAM) under Apache 2.0💙

👉Review https://t.ly/WBW00
👉Paper https://arxiv.org/pdf/2511.13715
👉Project https://henghuiding.com/SAAS/
👉Repo https://github.com/FudanCVL/SAAS

🔥 SAM 3/3D are OUT!! 🔥

👉#META released SAM 3, a unified model for detection, segmentation, tracking of objects in images & video using text, exemplar & visual prompts. Repo/Models under proprietary license💙

👉Review https://t.ly/lnRZN
👉Paper https://t.ly/5tq9N
👉Project https://ai.meta.com/sam3/
👉Demo: https://segment-anything.com
👉Repo https://github.com/facebookresearch/sam3

🍯Unwrapping of 3D Meshes🍯

👉PartUV is a novel part-based UV unwrapping method for 3D meshes; it combines learned part priors with geometric cues to generate a compact set of part-aligned charts. Repo released💙

👉Review https://t.ly/8dNIY
👉Paper arxiv.org/pdf/2511.16659
👉Project www.zhaoningwang.com/PartUV/
👉Repo github.com/EricWang12/PartUV

🍕 Upsample Anything 🍕

👉Upsample Anything, a novel universal, training-free up-sampler via lightweight test-time optimization. No code but it's a relevant paper💙

👉Review https://t.ly/7LE6G
👉Paper https://lnkd.in/dsUfdtih

🦞Single Synthetic Image per Class🦞

👉MIT unveils Linear Gradient Matching (H/T Torralba), a novel method of distillation to use a single synthetic image per class for linear classifiers training (and more). Repo available💙

👉Review https://t.ly/dD3un
👉Paper arxiv.org/pdf/2511.16674
👉Project linear-gradient-matching.github.io/
👉Repo github.com/GeorgeCazenavette/linear-gradient-matching

🧪 EfficientSAM3 is out 🧪

👉Bristol announces EfficientSAM3, a family of efficient models built on Progressive Hierarchical Distillation that transfers capability from SAM3 to lightweight students. Code coming (in sync with SAM3 release)💙

👉Review https://t.ly/bfXP2
👉Paper arxiv.org/pdf/2511.15833
👉Project simonzeng7108.github.io/efficientsam3/
👉Repo github.com/SimonZeng7108/efficientsam3

🌩️ Cloud4D in time 🌩️

👉Cloud4D: physically-realistic 3D cloud fields using ground-based cameras at a 25 m spatial resolution and 5 s temporal resolution. Repo coming, Data released💙

👉Review https://t.ly/w7Zly
👉Paper arxiv.org/pdf/2511.19431
👉Project cloud4d.jacob-lin.com/
👉Data https://drive.google.com/drive/folders/1QU_0kIUXIVt8h3uqygBeaF3Gvr_L5SdX?usp=drive_link
👉Repo TBA

🍓MotionV2V: Editing Motion in Video🍓

👉 Google unveils motion edits, a new approach for editing videos by controlling the change in motion from the original to the edited video using diffusion models. Impressive results. Repo released soon💙

👉Review https://t.ly/s0sIT
👉Paper https://arxiv.org/pdf/2511.20640
👉Project https://ryanndagreat.github.io/MotionV2V/
👉Repo https://github.com/RyannDaGreat/MotionV2V

🔥 Smell Like Vision Spirit 🔥

👉New York Smells is a novel large-scale dataset of paired vision and olfaction captured in-the-wild, enabling the new task of cross-modal learning between smell and sight. With the lights out, it's less dangerous. Dataset available💙

👉Review https://t.ly/Ycn_B
👉Paper arxiv.org/pdf/2511.20544
👉Project smell.cs.columbia.edu/

🕶️ Seeing without Pixels 🕶️

👉Is it possible to perceive a video’s content without seeing its pixels, just from the camera trajectory? Deepmind (+ UTexas) is the first to systematically investigate this seemingly implausible question💙

👉Review https://t.ly/Ymd1c
👉Paper arxiv.org/pdf/2511.21681
👉Project sites.google.com/view/seeing-without-pixels