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Посты канала
🐈 TTT Long Video Generation🐈
👉A novel architecture for video generation adapting the CogVideoX 5B model by incorporating Test-Time Training layers. Adding TTT layers into a pre-trained Transformer -> one-minute clip from text storyboards. Videos, code & annotations released💙
👉Review https://t.ly/mhlTN
👉Paper arxiv.org/pdf/2504.05298
👉Project test-time-training.github.io/video-dit/
👉Repo github.com/test-time-training/ttt-video-dit
| 2 |  🐟Segment Any Motion in Video🐟
👉From CVPR2025 a novel approach for moving object segmentation that combines DINO-based semantic features and SAM2. Code under MIT license💙
👉Review https://t.ly/4aYjJ
👉Paper arxiv.org/pdf/2503.22268
👉Project motion-seg.github.io/
👉Repo github.com/nnanhuang/SegAnyMo | 222 |
| 3 |  🔥 Dereflection Any Image 🔥
👉SJTU & #Huawei unveils DAI, novel diffusion-based framework able to recover from a wide range of reflection types. One-step diffusion with deterministic outputs & fast inference. Inference, pretrained models & training released💙
👉Review https://t.ly/PDA9K
👉Paper https://arxiv.org/pdf/2503.17347
👉Project abuuu122.github.io/DAI.github.io/
👉Repo github.com/Abuuu122/Dereflection-Any-Image | 284 |
| 4 |  🥎LLM Spatial Understanding🥎
👉SpatialLM by Manycore: novel LLM designed to process 3D point cloud data and generate structured 3D scene understanding outputs. Code, model & data 💙
👉Review https://t.ly/ejr1s
👉Project manycore-research.github.io/SpatialLM/
👉Code github.com/manycore-research/SpatialLM
🤗Models https://huggingface.co/manycore-research | 301 |
| 5 |  🧸 Occluded 3D Reconstruction 🧸
👉Oxford unveils a novel 3D generative model to reconstruct 3D objects from partial observations. Code (TBR), demo, model on HF💙
👉Review https://t.ly/Lr5D7
👉Paper arxiv.org/pdf/2503.13439
👉Project sm0kywu.github.io/Amodal3R/
🤗huggingface.co/spaces/Sm0kyWu/Amodal3R | 277 |
| 6 |  🔥Distill-Any-Depth: SOTA MDE🔥
👉Distill-Any-Depth is the new SOTA monocular depth estimation model trained with a novel knowledge distillation. Authors: ZJUT, WestLake University, LZU & NTU. Source Code, pre-trained models & HF-demo released💙
👉Review https://t.ly/GBJgi
👉Paper arxiv.org/pdf/2502.19204
👉Repo https://lnkd.in/dPtxNrQh
🤗Demo https://lnkd.in/d2TMPf4b | 297 |
| 7 |  🧠 Distractor-Aware SAM2 🧠
👉A novel distractor-aware memory for SAM2 and an introspection-based update strategy for VOT. Code & Dataset released💙
👉Review https://t.ly/RBRpQ
👉Paper arxiv.org/pdf/2411.17576
👉Project jovanavidenovic.github.io/dam-4-sam
👉Repo github.com/jovanavidenovic/DAM4SAM/ | 299 |
| 8 |  🧪 SUPIR: SOTA restoration 🧪
👉SUPIR is the new SOTA in image restoration; suitable for restoration of blurry objects, defining the material texture of objects, and adjusting restoration based on high-level semantics
👉Review https://t.ly/wgObH
👉Project https://supir.xpixel.group/
👉Paper https://lnkd.in/dZPYcUuq
👉Demo coming 🩷 but no code announced :( | 36 |
| 9 |  🕷️ Gen-NeRF2NeRF Translation 🕷️
👉GenN2N: unified NeRF-to-NeRF translation for editing tasks such as text-driven NeRF editing, colorization, super-resolution, inpainting, etc.
👉Review https://t.ly/VMWAH
👉Paper arxiv.org/pdf/2404.02788.pdf
👉Project xiangyueliu.github.io/GenN2N/
👉Code github.com/Lxiangyue/GenN2N | 258 |
| 10 |  👩🦰 SOTA Gaussian Haircut 👩🦰
👉ETH et. al unveils Gaussian Haircut, the new SOTA in hair reconstruction via dual representation (classic + 3D Gaussian). Code and Model announced💙
👉Review https://t.ly/aiOjq
👉Paper arxiv.org/pdf/2409.14778
👉Project https://lnkd.in/dFRm2ycb
👉Repo https://lnkd.in/d5NWNkb5 | 200 |
| 11 |  📫MeshPose: DensePose+HMR📫
👉MeshPose: novel approach to jointly tackle DensePose and Human Mesh Reconstruction in a while. A natural fit for #AR applications requiring real-time mobile inference.
👉Review https://t.ly/a-5uN
👉Paper arxiv.org/pdf/2406.10180
👉Project https://meshpose.github.io/ | 201 |
| 12 |  🎹 PianoMotion10M for gen-hands 🎹
👉PianoMotion10M: 116 hours of piano playing videos from a bird’s-eye view with 10M+ annotated hand poses. A big contribution in hand motion generation. Code & Dataset released💙
👉Review https://t.ly/_pKKz
👉Paper arxiv.org/pdf/2406.09326
👉Code https://lnkd.in/dcBP6nvm
👉Project https://lnkd.in/d_YqZk8x
👉Dataset https://lnkd.in/dUPyfNDA | 183 |
| 13 |  👽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 | 199 |
| 14 |  🌾 New SOTA Edge Detection 🌾
👉CUP (+ ESPOCH) unveils the new SOTA for Edge Detection (NBED); superior performance consistently across multiple benchmarks, even compared with huge computational cost and complex training models. Source Code released💙
👉Review https://t.ly/zUMcS
👉Paper arxiv.org/pdf/2409.14976
👉Code github.com/Li-yachuan/NBED | 194 |
| 15 |  🔥 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 | 215 |
| 16 |  🔥 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 | 248 |
| 17 |  🤖 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 | 298 |
| 18 |  🛸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 | 283 |
| 19 |  🐙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 | 252 |
| 20 |  🈯SOTA 0-Shot Multi-View Diffusion🈯
👉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 | 235 |
