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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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🔥 MinVIS, a new SOTA is out 🔥
👉#Nvidia miniVIS: no video-based architectures nor training procedures🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Video architecture/train not required
✅MinVIS outperforms the previous SOTA
✅Occluded VIS (OVIS): >10% improvement
✅1% of labeled frames >> fully-supervised
More: https://bit.ly/3pcYzk1
👉#Nvidia miniVIS: no video-based architectures nor training procedures🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Video architecture/train not required
✅MinVIS outperforms the previous SOTA
✅Occluded VIS (OVIS): >10% improvement
✅1% of labeled frames >> fully-supervised
More: https://bit.ly/3pcYzk1
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🔥🔥MultiNeRF: three NeRFs are out!🔥🔥
👉Google opens the code of three #cvpr2022 papers: Mip-NeRF 360, Ref-NeRF, RawNeRF
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Paper_1: Mip-NeRF 360
✅Paper_2: Ref-NeRF
✅Paper_3: NeRF in the Dark
More: https://bit.ly/3QjpRRc
👉Google opens the code of three #cvpr2022 papers: Mip-NeRF 360, Ref-NeRF, RawNeRF
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Paper_1: Mip-NeRF 360
✅Paper_2: Ref-NeRF
✅Paper_3: NeRF in the Dark
More: https://bit.ly/3QjpRRc
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☀️LocoProp: Neural Layers Composition☀️
👉Google AI unveils LocoProp: novel neural paradigm for modular composition of layers.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Backprop++ via Local Loss Optimization
✅Layer-based w-reg, target output, loss
✅Multiple local update via first-order opt.
✅Superior performance and efficiency
More: https://bit.ly/3Q40YJn
👉Google AI unveils LocoProp: novel neural paradigm for modular composition of layers.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Backprop++ via Local Loss Optimization
✅Layer-based w-reg, target output, loss
✅Multiple local update via first-order opt.
✅Superior performance and efficiency
More: https://bit.ly/3Q40YJn
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🔥PCVOS: clip-wise mask VOS🔥
👉PCVOS: new semi-supervised video object segmentation method
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Reformulating semi-supervised VOS
✅Novel per-clip inference perspective
✅Clip-wise operation on intra-clip
✅PCVOS: model for per-clip inference
✅New SOTA on multiple benchmarks
More: https://bit.ly/3vJtmbz
👉PCVOS: new semi-supervised video object segmentation method
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Reformulating semi-supervised VOS
✅Novel per-clip inference perspective
✅Clip-wise operation on intra-clip
✅PCVOS: model for per-clip inference
✅New SOTA on multiple benchmarks
More: https://bit.ly/3vJtmbz
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🍑 World-Object Detection via ViT 🍑
👉Google unveils OWL-ViT: open-vocabulary detector based on ViTs 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅ViTs for Open-World Localization
✅Img-level to open-vocabulary detection
✅SOTA one-shot (img.cond.) detection
More: https://bit.ly/3Sy3jOj
👉Google unveils OWL-ViT: open-vocabulary detector based on ViTs 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅ViTs for Open-World Localization
✅Img-level to open-vocabulary detection
✅SOTA one-shot (img.cond.) detection
More: https://bit.ly/3Sy3jOj
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🎹🎹 Learning Piano in #AR 🎹🎹
👉PianoVision (on #META #Quest2) accelerates the piano learning via Passthrough #AR & hand tracking
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Sheet Insight to learn sight-read
✅MIDI keyboard connectivity
✅Air piano for no physical pianos
✅Multiplayer Music Instruction
✅PianoVision Music Hall in #VR
More: https://bit.ly/3zYvwGX
👉PianoVision (on #META #Quest2) accelerates the piano learning via Passthrough #AR & hand tracking
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Sheet Insight to learn sight-read
✅MIDI keyboard connectivity
✅Air piano for no physical pianos
✅Multiplayer Music Instruction
✅PianoVision Music Hall in #VR
More: https://bit.ly/3zYvwGX
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🧊EPro-PnP: Persp-n-Points Detection🧊
👉EPro-PnP: probabilistic PnP layer for general e2e pose estimation
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Probabilistic PnP for general e2e pose
✅Top-tier in 6DoF by inserting into CDPN
✅Deformable accurate detection
✅2D-3D corresp. learned from scratch
More: https://bit.ly/3BNPXYr
👉EPro-PnP: probabilistic PnP layer for general e2e pose estimation
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Probabilistic PnP for general e2e pose
✅Top-tier in 6DoF by inserting into CDPN
✅Deformable accurate detection
✅2D-3D corresp. learned from scratch
More: https://bit.ly/3BNPXYr
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🥇#NVIDIA wins SIGGRAPH's Best Paper🥇
👉Instant #NeRF awarded as a best paper at SIGGRAPH 2022!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Speed-up of several orders of magnitude
✅HQ neural primitives in a matter of secs
✅Render in tens of milliseconds at 1080p
✅Source code and resources available!
More: https://bit.ly/3Qt8c9D
👉Instant #NeRF awarded as a best paper at SIGGRAPH 2022!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Speed-up of several orders of magnitude
✅HQ neural primitives in a matter of secs
✅Render in tens of milliseconds at 1080p
✅Source code and resources available!
More: https://bit.ly/3Qt8c9D
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🪰 EasyMocap: Open Neural Mocap 🪰
👉EasyMocap: open-source marker-less mocap with novel view synthesis from RGB
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 (of last paper added):
✅Editable free-viewpoint video
✅Layered neural representation of humans
✅Multi-pax -> instances, weakly-supervised
✅HQ neural representation of the humans
✅Addressing camera error by human poses
More: https://bit.ly/3p6lUDO
👉EasyMocap: open-source marker-less mocap with novel view synthesis from RGB
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 (of last paper added):
✅Editable free-viewpoint video
✅Layered neural representation of humans
✅Multi-pax -> instances, weakly-supervised
✅HQ neural representation of the humans
✅Addressing camera error by human poses
More: https://bit.ly/3p6lUDO
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