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πΊ NeRF-ing "The Big Bang Theory" πΊ
πBerkeley unveils an approach for accurate estimation of actorβs 3D pose & location
ππ’π π‘π₯π’π π‘ππ¬:
β Input: images across the whole season
β 3D context (i.e. cams, structure, body)
β Integrating context in 3D estimation
β Re-ID, gaze, cinematography, pic editing
β Knock, Knock, Penny!
More: https://bit.ly/3OLuaUb
πBerkeley unveils an approach for accurate estimation of actorβs 3D pose & location
ππ’π π‘π₯π’π π‘ππ¬:
β Input: images across the whole season
β 3D context (i.e. cams, structure, body)
β Integrating context in 3D estimation
β Re-ID, gaze, cinematography, pic editing
β Knock, Knock, Penny!
More: https://bit.ly/3OLuaUb
π₯7π€―5π₯°2β€1
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π©ShAPO: SOTA in object understandingπ©
πJoint multi-object detection, #3D texture, 6D object pose & size estimation.
ππ’π π‘π₯π’π π‘ππ¬:
β Disentangled shape & appearance
β Efficient octree-based differentiable
β Object-centric understanding pipeline
β Detection, reconstruction , 6D & size
β SOTA in reconstruction & pose est.
More: https://bit.ly/3oHN5EQ
πJoint multi-object detection, #3D texture, 6D object pose & size estimation.
ππ’π π‘π₯π’π π‘ππ¬:
β Disentangled shape & appearance
β Efficient octree-based differentiable
β Object-centric understanding pipeline
β Detection, reconstruction , 6D & size
β SOTA in reconstruction & pose est.
More: https://bit.ly/3oHN5EQ
π7π€―1
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ποΈ CityNeRF: Neural Rendering of City Scenes ποΈ
πProgressive NeRF model and training set on city-scenes
ππ’π π‘π₯π’π π‘ππ¬:
β BungeeNeRF: novel progressive NeRF
β Details on drastically varied scales
β Growing with residual block structure
β Inclusive multi-level data supervision
More: https://bit.ly/3cS9vk7
πProgressive NeRF model and training set on city-scenes
ππ’π π‘π₯π’π π‘ππ¬:
β BungeeNeRF: novel progressive NeRF
β Details on drastically varied scales
β Growing with residual block structure
β Inclusive multi-level data supervision
More: https://bit.ly/3cS9vk7
π₯°7π3π€―3π±1
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π¦π¦ Rewriting Geometry of GAN π¦π¦
πDrive GAN synthesizing many unseen objects with the desired shape
ππ’π π‘π₯π’π π‘ππ¬:
β User-friendly "warping" with geometry
β Low-rank update to layer for editing
β Latent augmentation based on style-mix
β Endless objects with defined changes
β Latent space interpolation, image editing
More: https://bit.ly/3zIfOj8
πDrive GAN synthesizing many unseen objects with the desired shape
ππ’π π‘π₯π’π π‘ππ¬:
β User-friendly "warping" with geometry
β Low-rank update to layer for editing
β Latent augmentation based on style-mix
β Endless objects with defined changes
β Latent space interpolation, image editing
More: https://bit.ly/3zIfOj8
π8π±7π3π2β€1π₯1
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ππ GAUDI: the Neural Architect ππ
πNovel generative model for immersive 3D scenes from a moving camera
ππ’π π‘π₯π’π π‘ππ¬:
β Hundreds of thousands pics/scenes
β Novel denoising optimization objective
β New SOTA across multiple datasets
β Un/conditional on images/text
More: https://bit.ly/3Bt65ye
πNovel generative model for immersive 3D scenes from a moving camera
ππ’π π‘π₯π’π π‘ππ¬:
β Hundreds of thousands pics/scenes
β Novel denoising optimization objective
β New SOTA across multiple datasets
β Un/conditional on images/text
More: https://bit.ly/3Bt65ye
π₯6
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πNeDDF: the NeRF evolution!π
πNovel 3D representation that reciprocally constrains distance & density fields
ππ’π π‘π₯π’π π‘ππ¬:
β NeRF provides no distance
β Extending for arbitrary density
β Density via dist-field & gradient
β Alleviating the instability
More: https://bit.ly/3Bte8LC
πNovel 3D representation that reciprocally constrains distance & density fields
ππ’π π‘π₯π’π π‘ππ¬:
β NeRF provides no distance
β Extending for arbitrary density
β Density via dist-field & gradient
β Alleviating the instability
More: https://bit.ly/3Bte8LC
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π₯AND/OR: Composable Diffusion Modelsπ₯
πNovel neural compositional generation via Composable Diffusion Models
ππ’π π‘π₯π’π π‘ππ¬:
β DM as energy-based models
β Connecting diffusion models
β Conjunction & negation, on top of DM
β Zero-shot combinatorial generalization
More: https://bit.ly/3PYv1Cs
πNovel neural compositional generation via Composable Diffusion Models
ππ’π π‘π₯π’π π‘ππ¬:
β DM as energy-based models
β Connecting diffusion models
β Conjunction & negation, on top of DM
β Zero-shot combinatorial generalization
More: https://bit.ly/3PYv1Cs
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π₯ MobileNeRF is out -> Pure Fire! π₯
πMobileNeRF is out: the mobile evolution of NeRF via textured polygons.
ππ’π π‘π₯π’π π‘ππ¬:
β Same quality, 10x faster than SNeRG
β Memory-- by storing surface textures
β Integrated GPUs: less memory/power
β Suitable for browser & viewer is HTML
More: https://bit.ly/3PUKPWy
πMobileNeRF is out: the mobile evolution of NeRF via textured polygons.
ππ’π π‘π₯π’π π‘ππ¬:
β Same quality, 10x faster than SNeRG
β Memory-- by storing surface textures
β Integrated GPUs: less memory/power
β Suitable for browser & viewer is HTML
More: https://bit.ly/3PUKPWy
π₯25π5
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π§£NeRF for Outdoor Scene Relightingπ§£
πNeRF-OSR: the first neural radiance fields approach for outdoor scene relighting
ππ’π π‘π₯π’π π‘ππ¬:
β NeRF-method for outdoor relighting
β Simultaneous illumination/viewpoint
β Control over shading, shadow, albedo
β Self-Supervised training from outdoor
β Dataset: 3240 viewpoints, 110+ times
More: https://bit.ly/3vBiH2G
πNeRF-OSR: the first neural radiance fields approach for outdoor scene relighting
ππ’π π‘π₯π’π π‘ππ¬:
β NeRF-method for outdoor relighting
β Simultaneous illumination/viewpoint
β Control over shading, shadow, albedo
β Self-Supervised training from outdoor
β Dataset: 3240 viewpoints, 110+ times
More: https://bit.ly/3vBiH2G
π₯5π3β€1
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π©βπ¦° Real-Time Neural Hair π©βπ¦°
πAccurate hair geometry & appearance from multi-pics
ππ’π π‘π₯π’π π‘ππ¬:
β Bonn, CMU and Reality Labs
β Photorealistic Real-Time render
β HQ strand geometry/appearance
β Novel scalp texture description
β Intuitive manipulation of 3D hair
More: https://bit.ly/3vBiH2G
πAccurate hair geometry & appearance from multi-pics
ππ’π π‘π₯π’π π‘ππ¬:
β Bonn, CMU and Reality Labs
β Photorealistic Real-Time render
β HQ strand geometry/appearance
β Novel scalp texture description
β Intuitive manipulation of 3D hair
More: https://bit.ly/3vBiH2G
β€8π6
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π #VR by NASA - 1985 π
πQ: is #VR the technology that developed least in the last 40 years? π€
Let's talk: https://bit.ly/3JxDZ7i
πQ: is #VR the technology that developed least in the last 40 years? π€
Let's talk: https://bit.ly/3JxDZ7i
π€―7π€©2π1
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π₯ MinVIS, a new SOTA is out π₯
π#Nvidia miniVIS: no video-based architectures nor training proceduresπ€―
ππ’π π‘π₯π’π π‘ππ¬:
β Video architecture/train not required
β MinVIS outperforms the previous SOTA
β Occluded VIS (OVIS): >10% improvement
β 1% of labeled frames >> fully-supervised
More: https://bit.ly/3pcYzk1
π#Nvidia miniVIS: no video-based architectures nor training proceduresπ€―
ππ’π π‘π₯π’π π‘ππ¬:
β Video architecture/train not required
β MinVIS outperforms the previous SOTA
β Occluded VIS (OVIS): >10% improvement
β 1% of labeled frames >> fully-supervised
More: https://bit.ly/3pcYzk1
π₯12
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π₯π₯MultiNeRF: three NeRFs are out!π₯π₯
πGoogle opens the code of three #cvpr2022 papers: Mip-NeRF 360, Ref-NeRF, RawNeRF
ππ’π π‘π₯π’π π‘ππ¬:
β Paper_1: Mip-NeRF 360
β Paper_2: Ref-NeRF
β Paper_3: NeRF in the Dark
More: https://bit.ly/3QjpRRc
πGoogle opens the code of three #cvpr2022 papers: Mip-NeRF 360, Ref-NeRF, RawNeRF
ππ’π π‘π₯π’π π‘ππ¬:
β Paper_1: Mip-NeRF 360
β Paper_2: Ref-NeRF
β Paper_3: NeRF in the Dark
More: https://bit.ly/3QjpRRc
π13β€4π€―4
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βοΈLocoProp: Neural Layers CompositionβοΈ
πGoogle AI unveils LocoProp: novel neural paradigm for modular composition of layers.
ππ’π π‘π₯π’π π‘ππ¬:
β Backprop++ via Local Loss Optimization
β Layer-based w-reg, target output, loss
β Multiple local update via first-order opt.
β Superior performance and efficiency
More: https://bit.ly/3Q40YJn
πGoogle AI unveils LocoProp: novel neural paradigm for modular composition of layers.
ππ’π π‘π₯π’π π‘ππ¬:
β Backprop++ via Local Loss Optimization
β Layer-based w-reg, target output, loss
β Multiple local update via first-order opt.
β Superior performance and efficiency
More: https://bit.ly/3Q40YJn
π₯13
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π₯PCVOS: clip-wise mask VOSπ₯
πPCVOS: new semi-supervised video object segmentation method
ππ’π π‘π₯π’π π‘ππ¬:
β Reformulating semi-supervised VOS
β Novel per-clip inference perspective
β Clip-wise operation on intra-clip
β PCVOS: model for per-clip inference
β New SOTA on multiple benchmarks
More: https://bit.ly/3vJtmbz
πPCVOS: new semi-supervised video object segmentation method
ππ’π π‘π₯π’π π‘ππ¬:
β Reformulating semi-supervised VOS
β Novel per-clip inference perspective
β Clip-wise operation on intra-clip
β PCVOS: model for per-clip inference
β New SOTA on multiple benchmarks
More: https://bit.ly/3vJtmbz
π10π2β€1π€©1
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π World-Object Detection via ViT π
πGoogle unveils OWL-ViT: open-vocabulary detector based on ViTs π€―
ππ’π π‘π₯π’π π‘ππ¬:
β ViTs for Open-World Localization
β Img-level to open-vocabulary detection
β SOTA one-shot (img.cond.) detection
More: https://bit.ly/3Sy3jOj
πGoogle unveils OWL-ViT: open-vocabulary detector based on ViTs π€―
ππ’π π‘π₯π’π π‘ππ¬:
β ViTs for Open-World Localization
β Img-level to open-vocabulary detection
β SOTA one-shot (img.cond.) detection
More: https://bit.ly/3Sy3jOj
π€―12π3
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πΉπΉ Learning Piano in #AR πΉπΉ
πPianoVision (on #META #Quest2) accelerates the piano learning via Passthrough #AR & hand tracking
ππ’π π‘π₯π’π π‘ππ¬:
β Sheet Insight to learn sight-read
β MIDI keyboard connectivity
β Air piano for no physical pianos
β Multiplayer Music Instruction
β PianoVision Music Hall in #VR
More: https://bit.ly/3zYvwGX
πPianoVision (on #META #Quest2) accelerates the piano learning via Passthrough #AR & hand tracking
ππ’π π‘π₯π’π π‘ππ¬:
β Sheet Insight to learn sight-read
β MIDI keyboard connectivity
β Air piano for no physical pianos
β Multiplayer Music Instruction
β PianoVision Music Hall in #VR
More: https://bit.ly/3zYvwGX
β€15π€―6π1
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π§EPro-PnP: Persp-n-Points Detectionπ§
πEPro-PnP: probabilistic PnP layer for general e2e pose estimation
ππ’π π‘π₯π’π π‘ππ¬:
β Probabilistic PnP for general e2e pose
β Top-tier in 6DoF by inserting into CDPN
β Deformable accurate detection
β 2D-3D corresp. learned from scratch
More: https://bit.ly/3BNPXYr
πEPro-PnP: probabilistic PnP layer for general e2e pose estimation
ππ’π π‘π₯π’π π‘ππ¬:
β Probabilistic PnP for general e2e pose
β Top-tier in 6DoF by inserting into CDPN
β Deformable accurate detection
β 2D-3D corresp. learned from scratch
More: https://bit.ly/3BNPXYr
π11
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π₯#NVIDIA wins SIGGRAPH's Best Paperπ₯
πInstant #NeRF awarded as a best paper at SIGGRAPH 2022!
ππ’π π‘π₯π’π π‘ππ¬:
β Speed-up of several orders of magnitude
β HQ neural primitives in a matter of secs
β Render in tens of milliseconds at 1080p
β Source code and resources available!
More: https://bit.ly/3Qt8c9D
πInstant #NeRF awarded as a best paper at SIGGRAPH 2022!
ππ’π π‘π₯π’π π‘ππ¬:
β Speed-up of several orders of magnitude
β HQ neural primitives in a matter of secs
β Render in tens of milliseconds at 1080p
β Source code and resources available!
More: https://bit.ly/3Qt8c9D
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