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Oldies but goldies: R. Keys, Cubic convolution interpolation for digital image processing, 1980. Introduces bicubic interpolation, the most frequently used image interpolation method.

Gabriel Peyré

102,325 subscribers

37,829 views • 1 year ago •via X (Twitter)

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😀 ToonCrafter: Generative Cartoon Interpolation 🔆 Can interpolate two cartoon images by leveraging the pre-trained image-to-video diffusion priors. Model on 🤗👇

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Gradio

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Oldies but goldies: J Nelder, R Mead, A simplex method for function minimization, 1965. Nelder-Mead is a popular derivative-free optimization method that performs a heuristic greedy search.

Oldies but goldies: J Nelder, R Mead, A simplex method for function minimization, 1965. Nelder-Mead is a popular derivative-free optimization method that performs a heuristic greedy search.

Gabriel Peyré

38,796 views • 2 years ago

The Stable Video Diffusion model just dropped 🔥 The new model supports: – Text-to-Video – Image-to-Video – 14 or 25 frames at 576 x 1024 – Multi-View Generation – Frame Interpolation – 3D Scene Understanding – Camera Control via LoRA Paper: Code: SVD model: SVD-XT model:

The Stable Video Diffusion model just dropped 🔥 The new model supports: – Text-to-Video – Image-to-Video – 14 or 25 frames at 576 x 1024 – Multi-View Generation – Frame Interpolation – 3D Scene Understanding – Camera Control via LoRA Paper: Code: SVD model: SVD-XT model:

Dreaming Tulpa 🥓👑

457,795 views • 2 years ago

Oldies but goldies: Y Nesterov, A method for solving a convex programming problem with convergence rate O(1/k^2). Improves the 1/k rate of vanilla gradient descent to 1/k^2.

Oldies but goldies: Y Nesterov, A method for solving a convex programming problem with convergence rate O(1/k^2). Improves the 1/k rate of vanilla gradient descent to 1/k^2.

Gabriel Peyré

47,883 views • 1 year ago

3. Modify image Use this prompt to modify the image you have chosen: "Modify image [1] with seed [1470033597]: add a parrot on her shoulder" Dall-E 3 identifies the image and makes the changes for you! Tips and tricks: - You can generate as many variations as you like. - It's also possible to remove an element from the image using the same method. - Sometimes the image isn't 100% identical, but still looks very similar.

3. Modify image Use this prompt to modify the image you have chosen: "Modify image [1] with seed [1470033597]: add a parrot on her shoulder" Dall-E 3 identifies the image and makes the changes for you! Tips and tricks: - You can generate as many variations as you like. - It's also possible to remove an element from the image using the same method. - Sometimes the image isn't 100% identical, but still looks very similar.

Paul Couvert

44,073 views • 2 years ago

🎥 Comparing AI video models: Image to video • Gen-3 • Kling AI 1.5 • Hailuo MiniMax • Luma Dream Machine I used a Midjourney image in each model 4 times with no text prompt. This type of image is difficult for the AI to separate the subject in the front from the people behind her - they tend to move the group as if they are a single unit. But the results were interesting, as you can see! I chose my favorite results, below.

🎥 Comparing AI video models: Image to video • Gen-3 • Kling AI 1.5 • Hailuo MiniMax • Luma Dream Machine I used a Midjourney image in each model 4 times with no text prompt. This type of image is difficult for the AI to separate the subject in the front from the people behind her - they tend to move the group as if they are a single unit. But the results were interesting, as you can see! I chose my favorite results, below.

Heather Cooper

41,378 views • 1 year ago

We recently discovered the ultimate workflow to restore old footage to something close to digital high-quality footage. The method works better than we thought, and it utilizes OpenAI GPT-Image 2 + seedance2.0 2.0 + Topaz Labs Astra…. See the step-by-step workflow in 🧵 Shoutout to Adam Nieri from our team for the real footage + test!

We recently discovered the ultimate workflow to restore old footage to something close to digital high-quality footage. The method works better than we thought, and it utilizes OpenAI GPT-Image 2 + seedance2.0 2.0 + Topaz Labs Astra…. See the step-by-step workflow in 🧵 Shoutout to Adam Nieri from our team for the real footage + test!

Curious Refuge

35,729 views • 3 months ago

$🪽 Hermes just got more creative! —— Risomorphism-1911 — production-grade ASCII rendering pipeline 🎨 Hermes-native ASCII art engine. 4 presets, --scale 1–16, video→animated eikon pipeline for your Herm TUI, quality-gated verdicts, pure-Python backend. Shipped, tested, gallery-stocked. Ready for operator deployment. 🧵 --- What it is Risomorphism-1911 is the ASCII rendering foundation for Hermes ops. Still images, video, animated eikons — all from a single deterministic pipeline. No external binaries. No guesswork. Quality enforced at every step. --- Capabilities - 4 presets: stroke-clarity (high-contrast poster), d30-dense (180-glyph block mode), braille-detail (4× effective resolution), eikon-motion (video pipeline) - Integer scaling: --scale N (1–16) on base 48×24 grid; intermediate grids adapt automatically - Quality gates: automatic verdict — high-contrast (production-safe), low-contrast-garble-risk (auto-reject), braille-dominant (resolution boost detected) - Video pipeline: frame extraction → motion-phase detection → optional motion-compensated interpolation (48 fps) → embedded HTML5 player (no HTTP/CORS) - Edge-aware processing: Laplacian-weighted downsampling + CLAHE preserves structural edges even at scale-16 densities - Pure-Python runtime: Pillow + NumPy only; ffmpeg optional for interpolation step --- CLI surface ascii-pipeline presets # list 4 presets ascii-pipeline diagnose file.txt # quality verdict ascii-pipeline render-preview image.jpg # quick PNG ascii-pipeline render-image \ --input image.jpg \ --preset d30-dense \ --scale 4 \ --out out.txt \ --preview-out out.png \ --diagnostics-out out.json ascii-pipeline build-eikon-from-video \ --video owl.mp4 \ --fps 48 \ --states 3 \ --id owl-smooth --- Scale strategy - Base: 48×24 (Herm avatar) - Scale 1–4: deployable, fast - Scale 8: showcase-ready - Scale 16: poster-sized, heavy, edge-aware mandatory All paths share the same preset pipeline; intermediate grids scale transparently. --- Tech stack - Python 3.11+, Pillow ≥10.0, NumPy ≥1.26 - Zero runtime binary deps - 11-test suite, 100% green - MIT license - Skill documented in SKILL.md with operator guidance --- Gallery (16 panels) - Cosmic pyramid stroke-clarity 192×96 — bold poster contrast - Cosmic pyramid D30 dense 192×96 — 180-glyph atmospheric - Owl animated eikon 48 fps — motion phases, smooth interpolation - Avatar fallback 48×24 — compact, deployable, legible All final-generation assets only. Clean tree: ~47 MB. No intermediates. --- This is the ASCII rendering baseline Hermes ops can rely on. Deterministic. Quality-gated. Production-ready.$

🪽 Hermes just got more creative! —— Risomorphism-1911 — production-grade ASCII rendering pipeline 🎨 Hermes-native ASCII art engine. 4 presets, --scale 1–16, video→animated eikon pipeline for your Herm TUI, quality-gated verdicts, pure-Python backend. Shipped, tested, gallery-stocked. Ready for operator deployment. 🧵 --- What it is Risomorphism-1911 is the ASCII rendering foundation for Hermes ops. Still images, video, animated eikons — all from a single deterministic pipeline. No external binaries. No guesswork. Quality enforced at every step. --- Capabilities - 4 presets: stroke-clarity (high-contrast poster), d30-dense (180-glyph block mode), braille-detail (4× effective resolution), eikon-motion (video pipeline) - Integer scaling: --scale N (1–16) on base 48×24 grid; intermediate grids adapt automatically - Quality gates: automatic verdict — high-contrast (production-safe), low-contrast-garble-risk (auto-reject), braille-dominant (resolution boost detected) - Video pipeline: frame extraction → motion-phase detection → optional motion-compensated interpolation (48 fps) → embedded HTML5 player (no HTTP/CORS) - Edge-aware processing: Laplacian-weighted downsampling + CLAHE preserves structural edges even at scale-16 densities - Pure-Python runtime: Pillow + NumPy only; ffmpeg optional for interpolation step --- CLI surface ascii-pipeline presets # list 4 presets ascii-pipeline diagnose file.txt # quality verdict ascii-pipeline render-preview image.jpg # quick PNG ascii-pipeline render-image \ --input image.jpg \ --preset d30-dense \ --scale 4 \ --out out.txt \ --preview-out out.png \ --diagnostics-out out.json ascii-pipeline build-eikon-from-video \ --video owl.mp4 \ --fps 48 \ --states 3 \ --id owl-smooth --- Scale strategy - Base: 48×24 (Herm avatar) - Scale 1–4: deployable, fast - Scale 8: showcase-ready - Scale 16: poster-sized, heavy, edge-aware mandatory All paths share the same preset pipeline; intermediate grids scale transparently. --- Tech stack - Python 3.11+, Pillow ≥10.0, NumPy ≥1.26 - Zero runtime binary deps - 11-test suite, 100% green - MIT license - Skill documented in SKILL.md with operator guidance --- Gallery (16 panels) - Cosmic pyramid stroke-clarity 192×96 — bold poster contrast - Cosmic pyramid D30 dense 192×96 — 180-glyph atmospheric - Owl animated eikon 48 fps — motion phases, smooth interpolation - Avatar fallback 48×24 — compact, deployable, legible All final-generation assets only. Clean tree: ~47 MB. No intermediates. --- This is the ASCII rendering baseline Hermes ops can rely on. Deterministic. Quality-gated. Production-ready.

Ousia Research (οὐσία)

14,879 views • 4 months ago

MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers paper page: Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However, the computational demands of diffusion-based methods are substantial, often necessitating large-scale paired datasets for training, and therefore challenging the deployment in practical applications. This study addresses this challenge by breaking down the text-based video editing process into two separate stages. In the first stage, we leverage an existing text-to-image diffusion model to simultaneously edit a few keyframes without additional fine-tuning. In the second stage, we introduce an efficient model called MaskINT, which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the keyframes, benefiting from structural guidance provided by intermediate frames. Our comprehensive set of experiments illustrates the efficacy and efficiency of MaskINT when compared to other diffusion-based methodologies. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain.

MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers paper page: Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However, the computational demands of diffusion-based methods are substantial, often necessitating large-scale paired datasets for training, and therefore challenging the deployment in practical applications. This study addresses this challenge by breaking down the text-based video editing process into two separate stages. In the first stage, we leverage an existing text-to-image diffusion model to simultaneously edit a few keyframes without additional fine-tuning. In the second stage, we introduce an efficient model called MaskINT, which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the keyframes, benefiting from structural guidance provided by intermediate frames. Our comprehensive set of experiments illustrates the efficacy and efficiency of MaskINT when compared to other diffusion-based methodologies. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain.

AK

25,460 views • 2 years ago

If you're using match cuts in your video, the invideo editor + agent gives you the fastest, most seamless experience to do it. Here's what we did to create this in ~40 minutes: ~Picked an image with a circle in the centre. Used that image as a reference to generate other images with circular subjects (but you could always just use your own footage). ~Uploaded the audio track I wanted to use into the invideo timeline, and asked the agent to detect all beats and place markers there. ~Asked the agent to then place all my generated images against those markers. ~Used rulers in the editor to maintain clip/subject alignment.

If you're using match cuts in your video, the invideo editor + agent gives you the fastest, most seamless experience to do it. Here's what we did to create this in ~40 minutes: ~Picked an image with a circle in the centre. Used that image as a reference to generate other images with circular subjects (but you could always just use your own footage). ~Uploaded the audio track I wanted to use into the invideo timeline, and asked the agent to detect all beats and place markers there. ~Asked the agent to then place all my generated images against those markers. ~Used rulers in the editor to maintain clip/subject alignment.

Invideo

11,077 views • 18 days ago

NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions paper page: present a novel type of neural fields that uses general radial bases for signal representation. State-of-the-art neural fields typically rely on grid-based representations for storing local neural features and N-dimensional linear kernels for interpolating features at continuous query points. The spatial positions of their neural features are fixed on grid nodes and cannot well adapt to target signals. Our method instead builds upon general radial bases with flexible kernel position and shape, which have higher spatial adaptivity and can more closely fit target signals. To further improve the channel-wise capacity of radial basis functions, we propose to compose them with multi-frequency sinusoid functions. This technique extends a radial basis to multiple Fourier radial bases of different frequency bands without requiring extra parameters, facilitating the representation of details. Moreover, by marrying adaptive radial bases with grid-based ones, our hybrid combination inherits both adaptivity and interpolation smoothness. We carefully designed weighting schemes to let radial bases adapt to different types of signals effectively. Our experiments on 2D image and 3D signed distance field representation demonstrate the higher accuracy and compactness of our method than prior arts. When applied to neural radiance field reconstruction, our method achieves state-of-the-art rendering quality, with small model size and comparable training speed.

NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions paper page: present a novel type of neural fields that uses general radial bases for signal representation. State-of-the-art neural fields typically rely on grid-based representations for storing local neural features and N-dimensional linear kernels for interpolating features at continuous query points. The spatial positions of their neural features are fixed on grid nodes and cannot well adapt to target signals. Our method instead builds upon general radial bases with flexible kernel position and shape, which have higher spatial adaptivity and can more closely fit target signals. To further improve the channel-wise capacity of radial basis functions, we propose to compose them with multi-frequency sinusoid functions. This technique extends a radial basis to multiple Fourier radial bases of different frequency bands without requiring extra parameters, facilitating the representation of details. Moreover, by marrying adaptive radial bases with grid-based ones, our hybrid combination inherits both adaptivity and interpolation smoothness. We carefully designed weighting schemes to let radial bases adapt to different types of signals effectively. Our experiments on 2D image and 3D signed distance field representation demonstrate the higher accuracy and compactness of our method than prior arts. When applied to neural radiance field reconstruction, our method achieves state-of-the-art rendering quality, with small model size and comparable training speed.

AK

194,523 views • 3 years ago

Even if you shut off white balance, most of the pictures I see aren't selling how orange the sky is, because there's no frame of reference. Here's a 6500k display with an image we all recognize, for a more relative perspective. The color science isn't perfect, but close enough.

Even if you shut off white balance, most of the pictures I see aren't selling how orange the sky is, because there's no frame of reference. Here's a 6500k display with an image we all recognize, for a more relative perspective. The color science isn't perfect, but close enough.

Chris Vranos

2,850,762 views • 3 years ago

MiniMax H3 Instead of sharing the prompts for each of these videos, I thought it would be more useful to share how I created that prompts. All of the videos were generated with text-to-video. First, find an image with the kind of scene, composition and mood you want to recreate. I used a few YouTube playlist thumbnails as references but Pinterest is also a great place to find inspiration. You can even use your own old or nostalgic photographs. Then upload the image to ChatGPT and ask it to describe the scene. The description it gives you can essentially become your text-to-video prompt. From there, you can generate completely new scenes with a similar composition, atmosphere and cinematic language. You can of course use the reference image directly with image-to-video or as a first frame. But if the original image isn't yours, I prefer using it only as visual inspiration and recreating the scene through text-to-video. This is the prompt I use with ChatGPT: "Describe the scene in this image in English, focusing primarily on what is happening, the characters, their actions and body language, the setting and the overall atmosphere. Also briefly describe the composition, framing, camera angle, approximate lens choice, lighting, color palette and cinematic aesthetic. Keep it concise and scene-focused rather than overly technical."

MiniMax H3 Instead of sharing the prompts for each of these videos, I thought it would be more useful to share how I created that prompts. All of the videos were generated with text-to-video. First, find an image with the kind of scene, composition and mood you want to recreate. I used a few YouTube playlist thumbnails as references but Pinterest is also a great place to find inspiration. You can even use your own old or nostalgic photographs. Then upload the image to ChatGPT and ask it to describe the scene. The description it gives you can essentially become your text-to-video prompt. From there, you can generate completely new scenes with a similar composition, atmosphere and cinematic language. You can of course use the reference image directly with image-to-video or as a first frame. But if the original image isn't yours, I prefer using it only as visual inspiration and recreating the scene through text-to-video. This is the prompt I use with ChatGPT: "Describe the scene in this image in English, focusing primarily on what is happening, the characters, their actions and body language, the setting and the overall atmosphere. Also briefly describe the composition, framing, camera angle, approximate lens choice, lighting, color palette and cinematic aesthetic. Keep it concise and scene-focused rather than overly technical."

Kōda

55,398 views • 1 month ago

Q. you secretly save members meme 🐕 this is my wallpaper 🌰 ohyul has the most photos. i used to have a lot of goofy shots 🐈‍⬛ it would be really fun if those photos could be made public 🌰 but he's been on top of managing his image so it’s less fun now 😭😭😭😭😭

Q. you secretly save members meme 🐕 this is my wallpaper 🌰 ohyul has the most photos. i used to have a lot of goofy shots 🐈‍⬛ it would be really fun if those photos could be made public 🌰 but he's been on top of managing his image so it’s less fun now 😭😭😭😭😭

☆

51,247 views • 3 months ago

Made with GPT Image 2 + Seedance 2.0 by Yapper Prompt: Aurban city street, consistent young adult male same from reference Image (short dark hair, casual clothing), full-body framing, fixed side camera, slight gradual zoom-in, smooth interpolation, high character consistency Timeline Breakdown (15 seconds total) 0.0s – 1.5s | Scene 1 — Introduction young man standing still holding skateboard vertically at his side, relaxed posture, neutral expression, quiet city street background, no motion 1.5s – 3.0s | Scene 2 — Setup he lowers skateboard to ground and steps onto it, slight crouch, arms relaxed for balance, subtle anticipation 3.0s – 5.0s | Scene 3 — Rolling he rides forward smoothly, knees slightly bent, body leaning forward, light motion lines, slight background blur begins 5.0s – 6.5s | Scene 4 — Trick Preparation he crouches deeper, back foot pressing tail, front foot ready, arms widen, tension builds before jump 6.5s – 8.0s | Scene 5 — Ollie Jump he pops the board and jumps, skateboard rises with him, mid-air suspension, knees bent, strong motion lines 8.0s – 9.5s | Scene 6 — Landing he lands cleanly and continues riding, now wearing open casual jacket, expression more confident 9.5s – 11.5s | Scene 7 — Speed Increase he rides faster, leaning slightly, stronger motion blur, dynamic lines emphasize speed 11.5s – 13.5s | Scene 8 — Kickflip he performs kickflip, skateboard flips beneath him mid-air, controlled posture, high energy motion 13.5s – 15.0s | Scene 9 — Final Pose he lands and stabilizes, standing confidently on skateboard, now wearing full tracksuit jacket, relaxed stance, motion settles into still frame Motion & Style Controls motion strength: low (0–3s) medium (3–9s) high (9–13.5s) ease-out (final 1.5s) transitions: smooth interpolation between scenes camera: fixed side tracking + slight zoom-in across full 15s pacing: gradual acceleration then clean slowdown

Made with GPT Image 2 + Seedance 2.0 by Yapper Prompt: Aurban city street, consistent young adult male same from reference Image (short dark hair, casual clothing), full-body framing, fixed side camera, slight gradual zoom-in, smooth interpolation, high character consistency Timeline Breakdown (15 seconds total) 0.0s – 1.5s | Scene 1 — Introduction young man standing still holding skateboard vertically at his side, relaxed posture, neutral expression, quiet city street background, no motion 1.5s – 3.0s | Scene 2 — Setup he lowers skateboard to ground and steps onto it, slight crouch, arms relaxed for balance, subtle anticipation 3.0s – 5.0s | Scene 3 — Rolling he rides forward smoothly, knees slightly bent, body leaning forward, light motion lines, slight background blur begins 5.0s – 6.5s | Scene 4 — Trick Preparation he crouches deeper, back foot pressing tail, front foot ready, arms widen, tension builds before jump 6.5s – 8.0s | Scene 5 — Ollie Jump he pops the board and jumps, skateboard rises with him, mid-air suspension, knees bent, strong motion lines 8.0s – 9.5s | Scene 6 — Landing he lands cleanly and continues riding, now wearing open casual jacket, expression more confident 9.5s – 11.5s | Scene 7 — Speed Increase he rides faster, leaning slightly, stronger motion blur, dynamic lines emphasize speed 11.5s – 13.5s | Scene 8 — Kickflip he performs kickflip, skateboard flips beneath him mid-air, controlled posture, high energy motion 13.5s – 15.0s | Scene 9 — Final Pose he lands and stabilizes, standing confidently on skateboard, now wearing full tracksuit jacket, relaxed stance, motion settles into still frame Motion & Style Controls motion strength: low (0–3s) medium (3–9s) high (9–13.5s) ease-out (final 1.5s) transitions: smooth interpolation between scenes camera: fixed side tracking + slight zoom-in across full 15s pacing: gradual acceleration then clean slowdown

Zar⭕on

12,089 views • 4 months ago

So, today we have fast SDF sculpting + real-time AI in Unbound Loop. Old news🥱 Coming up next: -quad-view generation from sculpted geometry -image tweaks via nano🍌 -tripo HD & Low-Poly 3D generation There's more in the upcoming release, but these three deserve a closer look: Quad-View Generation Most platforms offer some version of this, but Loop has a key advantage, your sculpted model is the reference. That means less guessing from the generator. Though it’s not 100% foolproof, like any AI I guess? (I should stop stating the obvious every time). Image Tweaks via Chat Select any generated image and ask for fixes or changes in real time. Works great on unintended quad-view hallucinations, but also handy for quick iterations. Swapping colors, tweaking details, removing elements. Tripo 3D generator Especially the low-poly model, it consistently delivered fantastic game-ready topology when we tried it with our real-time AI output. And it's super fast.

So, today we have fast SDF sculpting + real-time AI in Unbound Loop. Old news🥱 Coming up next: -quad-view generation from sculpted geometry -image tweaks via nano🍌 -tripo HD & Low-Poly 3D generation There's more in the upcoming release, but these three deserve a closer look: Quad-View Generation Most platforms offer some version of this, but Loop has a key advantage, your sculpted model is the reference. That means less guessing from the generator. Though it’s not 100% foolproof, like any AI I guess? (I should stop stating the obvious every time). Image Tweaks via Chat Select any generated image and ask for fixes or changes in real time. Works great on unintended quad-view hallucinations, but also handy for quick iterations. Swapping colors, tweaking details, removing elements. Tripo 3D generator Especially the low-poly model, it consistently delivered fantastic game-ready topology when we tried it with our real-time AI output. And it's super fast.

Andrea Intg.

15,132 views • 1 month ago

5-Axis CNC machining of turbocharger impellers sits in the same league as aerospace manufacturing, precision engineering pushed to physical limits. These machines run with positional accuracy in the 1-5 micron range, spindle speeds of 20,000-40,000 RPM, and continuous 5-axis interpolation to carve complex blade geometry without collision, vibration, or thermal drift during cutting of nickel alloys and titanium. A production-grade 5-axis system for impellers typically costs $300,000 to $3 million+ The real constraint is not motion, but stability, suppressing chatter, tool deflection, and microscopic surface defects that would quietly destroy efficiency at 100,000+ RPM operating cycles. Without this class of machining, modern turbocharged engines, high-efficiency industrial compressors, and compact propulsion systems simply would not exist.

5-Axis CNC machining of turbocharger impellers sits in the same league as aerospace manufacturing, precision engineering pushed to physical limits. These machines run with positional accuracy in the 1-5 micron range, spindle speeds of 20,000-40,000 RPM, and continuous 5-axis interpolation to carve complex blade geometry without collision, vibration, or thermal drift during cutting of nickel alloys and titanium. A production-grade 5-axis system for impellers typically costs $300,000 to $3 million+ The real constraint is not motion, but stability, suppressing chatter, tool deflection, and microscopic surface defects that would quietly destroy efficiency at 100,000+ RPM operating cycles. Without this class of machining, modern turbocharged engines, high-efficiency industrial compressors, and compact propulsion systems simply would not exist.

Ammanichanda

48,750 views • 3 months ago

Is Google taking initial steps to enhance Street View? For some reason, Street View seems stuck in technology that feels outdated. I wonder if we'll see such improvements on the product side. Also, note how much better it performs in all aspects compared to Zip-NeRF in their presented material. It offers more details and fewer artifacts. Great work! "LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering" Contributions: • We propose a novel LOD representation for 3DGS which, unlike previous methods [27, 28, 17], does not recompute the list of used Gaussians at each frame. This allows for acceleration and compaction, enabling the rendering of large-scale scenes even on mobile devices. • We design a strategy to automatically select optimal hyperparameters for splitting LODs, whereas most other methods require manual tuning of hyperparameters for each 3D scene. • To further accelerate rendering, we split the scene into chunks and pre-compute sets of active Gaussians per chunk. • Finally, we introduce a novel opacity interpolation scheme to produce visually pleasing rendering and eliminate artifacts when transitioning between chunks.

Is Google taking initial steps to enhance Street View? For some reason, Street View seems stuck in technology that feels outdated. I wonder if we'll see such improvements on the product side. Also, note how much better it performs in all aspects compared to Zip-NeRF in their presented material. It offers more details and fewer artifacts. Great work! "LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering" Contributions: • We propose a novel LOD representation for 3DGS which, unlike previous methods [27, 28, 17], does not recompute the list of used Gaussians at each frame. This allows for acceleration and compaction, enabling the rendering of large-scale scenes even on mobile devices. • We design a strategy to automatically select optimal hyperparameters for splitting LODs, whereas most other methods require manual tuning of hyperparameters for each 3D scene. • To further accelerate rendering, we split the scene into chunks and pre-compute sets of active Gaussians per chunk. • Finally, we introduce a novel opacity interpolation scheme to produce visually pleasing rendering and eliminate artifacts when transitioning between chunks.

MrNeRF

62,564 views • 1 year ago

Playboi Carti ex girlfriend Gio went live to expose him for being Gay😬 , says its not an image and be acts like that in real life but what hurt here the most was when she caught him in a room shirtless with 5 other guys 😬 “I had to let yall know” “Yall kept asking” “Gay”

Playboi Carti ex girlfriend Gio went live to expose him for being Gay😬 , says its not an image and be acts like that in real life but what hurt here the most was when she caught him in a room shirtless with 5 other guys 😬 “I had to let yall know” “Yall kept asking” “Gay”

Fendi

89,623 views • 5 months ago

Playboi Carti ex girlfriend Gio went live to expose him for being Gay😬 , says its not an image and be acts like that in real life but what hurt here the most was when she caught him in a room shirtless with 5 other guys 😬 “I had to let yall know” “Yall kept asking” “Gay”

Playboi Carti ex girlfriend Gio went live to expose him for being Gay😬 , says its not an image and be acts like that in real life but what hurt here the most was when she caught him in a room shirtless with 5 other guys 😬 “I had to let yall know” “Yall kept asking” “Gay”

Fendi

12,717 views • 4 months ago