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💄DEVIANT: SOTA in mono-3D detection💄
👉A novel Depth EquiVarIAnt NeTwork for 3D monocular detection in the wild
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Michigan + #Meta + Ford 🤯
✅Depth-equi. + scale equiv. steerable
✅New SOTA on KITTI & Waymo
✅Ok cross-dataset -> generalization
More: https://bit.ly/3OEFtgK
👉A novel Depth EquiVarIAnt NeTwork for 3D monocular detection in the wild
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Michigan + #Meta + Ford 🤯
✅Depth-equi. + scale equiv. steerable
✅New SOTA on KITTI & Waymo
✅Ok cross-dataset -> generalization
More: https://bit.ly/3OEFtgK
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🧱 Assembling #LEGO with #AI 🧱
👉Step-by-step assembly manual created by human into machine-interpretable instructions
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Stanford + MIT + #Google 🤯
✅MEPNet: Manual-to-Executable-Plan Net
✅Manual to machine-executable plan
✅2D manual - 3D geometric shape
✅Reasoning on 3D alignments of legos
More: https://bit.ly/3PCwn5C
👉Step-by-step assembly manual created by human into machine-interpretable instructions
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Stanford + MIT + #Google 🤯
✅MEPNet: Manual-to-Executable-Plan Net
✅Manual to machine-executable plan
✅2D manual - 3D geometric shape
✅Reasoning on 3D alignments of legos
More: https://bit.ly/3PCwn5C
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🎃New SOTA in UDA Semantic Seg.🎃
👉HRDA: multi-res Unsupervised Domain Adaptive Semantic Seg. -> SOTA
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅ETH + MPG + KU Leuven 🤯
✅HRDA: multi-res approach for UDA
✅Manageable GPU memory footprint
✅Small objects & fine segmentation detail
✅New SOTA on GTA and Synthia dataset
More: https://bit.ly/3cKtDEp
👉HRDA: multi-res Unsupervised Domain Adaptive Semantic Seg. -> SOTA
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅ETH + MPG + KU Leuven 🤯
✅HRDA: multi-res approach for UDA
✅Manageable GPU memory footprint
✅Small objects & fine segmentation detail
✅New SOTA on GTA and Synthia dataset
More: https://bit.ly/3cKtDEp
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⚗️ SemAbs: 3D Scene Understanding ⚗️
👉Framework that equips 2D Vision-Language Models (VLMs) with new 3D spatial capabilities
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅2D VLMs with 3D reasoning skills
✅ViTs Efficient MS Relevancy Extraction
✅Novel Open-World understanding tasks
✅Completing partially observed objects
✅Finding hidden objects from language
More: https://bit.ly/3PYYk7d
👉Framework that equips 2D Vision-Language Models (VLMs) with new 3D spatial capabilities
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅2D VLMs with 3D reasoning skills
✅ViTs Efficient MS Relevancy Extraction
✅Novel Open-World understanding tasks
✅Completing partially observed objects
✅Finding hidden objects from language
More: https://bit.ly/3PYYk7d
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🦚 TinyCD: Neural Change Detection 🦚
👉TinyCD: new SOTA in change detection with up to 150x fewer parameters.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅SOTA with up to 150X fewer params
✅Mixing blocks for s.t. cross-correlation
✅PW-MLP for pixel wise classification
✅MAMB: novel block for skip connection
More: https://bit.ly/3zFEngk
👉TinyCD: new SOTA in change detection with up to 150x fewer parameters.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅SOTA with up to 150X fewer params
✅Mixing blocks for s.t. cross-correlation
✅PW-MLP for pixel wise classification
✅MAMB: novel block for skip connection
More: https://bit.ly/3zFEngk
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🦊 3D-Aware "StyleGANv2" version 🦊
👉Upgrading StyleGANv2 into a novel 3D-aware GAN with just a minimal set of changes🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅MPI-like 3D-aware GAN w/ single-view
✅GMPI: generative multiplane image
✅2D GAN 3D-aware with a minimal changes
✅Encoding 3D-aware inductive biases
More: https://bit.ly/3OJ5gnS
👉Upgrading StyleGANv2 into a novel 3D-aware GAN with just a minimal set of changes🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅MPI-like 3D-aware GAN w/ single-view
✅GMPI: generative multiplane image
✅2D GAN 3D-aware with a minimal changes
✅Encoding 3D-aware inductive biases
More: https://bit.ly/3OJ5gnS
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📺 NeRF-ing "The Big Bang Theory" 📺
👉Berkeley unveils an approach for accurate estimation of actor’s 3D pose & location
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Input: images across the whole season
✅3D context (i.e. cams, structure, body)
✅Integrating context in 3D estimation
✅Re-ID, gaze, cinematography, pic editing
✅Knock, Knock, Penny!
More: https://bit.ly/3OLuaUb
👉Berkeley unveils an approach for accurate estimation of actor’s 3D pose & location
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Input: images across the whole season
✅3D context (i.e. cams, structure, body)
✅Integrating context in 3D estimation
✅Re-ID, gaze, cinematography, pic editing
✅Knock, Knock, Penny!
More: https://bit.ly/3OLuaUb
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🎩ShAPO: SOTA in object understanding🎩
👉Joint multi-object detection, #3D texture, 6D object pose & size estimation.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Disentangled shape & appearance
✅Efficient octree-based differentiable
✅Object-centric understanding pipeline
✅Detection, reconstruction , 6D & size
✅SOTA in reconstruction & pose est.
More: https://bit.ly/3oHN5EQ
👉Joint multi-object detection, #3D texture, 6D object pose & size estimation.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Disentangled shape & appearance
✅Efficient octree-based differentiable
✅Object-centric understanding pipeline
✅Detection, reconstruction , 6D & size
✅SOTA in reconstruction & pose est.
More: https://bit.ly/3oHN5EQ
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🏙️ CityNeRF: Neural Rendering of City Scenes 🏙️
👉Progressive NeRF model and training set on city-scenes
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅BungeeNeRF: novel progressive NeRF
✅Details on drastically varied scales
✅Growing with residual block structure
✅Inclusive multi-level data supervision
More: https://bit.ly/3cS9vk7
👉Progressive NeRF model and training set on city-scenes
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅BungeeNeRF: novel progressive NeRF
✅Details on drastically varied scales
✅Growing with residual block structure
✅Inclusive multi-level data supervision
More: https://bit.ly/3cS9vk7
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🍦🍦 Rewriting Geometry of GAN 🍦🍦
👉Drive GAN synthesizing many unseen objects with the desired shape
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅User-friendly "warping" with geometry
✅Low-rank update to layer for editing
✅Latent augmentation based on style-mix
✅Endless objects with defined changes
✅Latent space interpolation, image editing
More: https://bit.ly/3zIfOj8
👉Drive GAN synthesizing many unseen objects with the desired shape
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅User-friendly "warping" with geometry
✅Low-rank update to layer for editing
✅Latent augmentation based on style-mix
✅Endless objects with defined changes
✅Latent space interpolation, image editing
More: https://bit.ly/3zIfOj8
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🍏🍏 GAUDI: the Neural Architect 🍏🍏
👉Novel generative model for immersive 3D scenes from a moving camera
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Hundreds of thousands pics/scenes
✅Novel denoising optimization objective
✅New SOTA across multiple datasets
✅Un/conditional on images/text
More: https://bit.ly/3Bt65ye
👉Novel generative model for immersive 3D scenes from a moving camera
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Hundreds of thousands pics/scenes
✅Novel denoising optimization objective
✅New SOTA across multiple datasets
✅Un/conditional on images/text
More: https://bit.ly/3Bt65ye
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🚜NeDDF: the NeRF evolution!🚜
👉Novel 3D representation that reciprocally constrains distance & density fields
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NeRF provides no distance
✅Extending for arbitrary density
✅Density via dist-field & gradient
✅Alleviating the instability
More: https://bit.ly/3Bte8LC
👉Novel 3D representation that reciprocally constrains distance & density fields
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NeRF provides no distance
✅Extending for arbitrary density
✅Density via dist-field & gradient
✅Alleviating the instability
More: https://bit.ly/3Bte8LC
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🔥AND/OR: Composable Diffusion Models🔥
👉Novel neural compositional generation via Composable Diffusion Models
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅DM as energy-based models
✅Connecting diffusion models
✅Conjunction & negation, on top of DM
✅Zero-shot combinatorial generalization
More: https://bit.ly/3PYv1Cs
👉Novel neural compositional generation via Composable Diffusion Models
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅DM as energy-based models
✅Connecting diffusion models
✅Conjunction & negation, on top of DM
✅Zero-shot combinatorial generalization
More: https://bit.ly/3PYv1Cs
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🔥 MobileNeRF is out -> Pure Fire! 🔥
👉MobileNeRF is out: the mobile evolution of NeRF via textured polygons.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Same quality, 10x faster than SNeRG
✅Memory-- by storing surface textures
✅Integrated GPUs: less memory/power
✅Suitable for browser & viewer is HTML
More: https://bit.ly/3PUKPWy
👉MobileNeRF is out: the mobile evolution of NeRF via textured polygons.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Same quality, 10x faster than SNeRG
✅Memory-- by storing surface textures
✅Integrated GPUs: less memory/power
✅Suitable for browser & viewer is HTML
More: https://bit.ly/3PUKPWy
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🧣NeRF for Outdoor Scene Relighting🧣
👉NeRF-OSR: the first neural radiance fields approach for outdoor scene relighting
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NeRF-method for outdoor relighting
✅Simultaneous illumination/viewpoint
✅Control over shading, shadow, albedo
✅Self-Supervised training from outdoor
✅Dataset: 3240 viewpoints, 110+ times
More: https://bit.ly/3vBiH2G
👉NeRF-OSR: the first neural radiance fields approach for outdoor scene relighting
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NeRF-method for outdoor relighting
✅Simultaneous illumination/viewpoint
✅Control over shading, shadow, albedo
✅Self-Supervised training from outdoor
✅Dataset: 3240 viewpoints, 110+ times
More: https://bit.ly/3vBiH2G
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👩🦰 Real-Time Neural Hair 👩🦰
👉Accurate hair geometry & appearance from multi-pics
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Bonn, CMU and Reality Labs
✅Photorealistic Real-Time render
✅HQ strand geometry/appearance
✅Novel scalp texture description
✅Intuitive manipulation of 3D hair
More: https://bit.ly/3vBiH2G
👉Accurate hair geometry & appearance from multi-pics
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Bonn, CMU and Reality Labs
✅Photorealistic Real-Time render
✅HQ strand geometry/appearance
✅Novel scalp texture description
✅Intuitive manipulation of 3D hair
More: https://bit.ly/3vBiH2G
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🚀 #VR by NASA - 1985 🚀
👉Q: is #VR the technology that developed least in the last 40 years? 🤔
Let's talk: https://bit.ly/3JxDZ7i
👉Q: is #VR the technology that developed least in the last 40 years? 🤔
Let's talk: https://bit.ly/3JxDZ7i
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