Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Amiga OpenGL remote rendering. OpenGL commands get serialised on Amiga, sent over a TCP socket to a remote rendering server. That is Amiga 68k GLQuake (running in winuae) rendering on my PC RTX3080 GPU :)

Claude Schwarz

5,202 subscribers

41,029 views • 8 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

my PC is about to explode, 5k+ dummies rendering in browser, mostly playable after a ton of optimizations

my PC is about to explode, 5k+ dummies rendering in browser, mostly playable after a ton of optimizations

BEN.ZZZ

19,614 views • 1 year ago

FASTER for #Amiga OCS/ECS: switched over to single pass (rather than 4 pass) rendering for car and scenery bobs, giving a noticeable increase in performance. Further evidence for any existing doubters out there that this is definitely not a lazy port from the ST! 😄

FASTER for #Amiga OCS/ECS: switched over to single pass (rather than 4 pass) rendering for car and scenery bobs, giving a noticeable increase in performance. Further evidence for any existing doubters out there that this is definitely not a lazy port from the ST! 😄

Jonathan Thomas @atariste.bsky.social

11,176 views • 1 year ago

BOMBSHELL REPORT: ⚠️ CCP whistleblowers reveal CHINA has the technology to CUT OFF POWER TO SHIPS rendering the controls useless.. Remote towing technology was reportedly used in 2017 on the Hudson River in a direct attempt to harm a CCP whistleblower..

BOMBSHELL REPORT: ⚠️ CCP whistleblowers reveal CHINA has the technology to CUT OFF POWER TO SHIPS rendering the controls useless.. Remote towing technology was reportedly used in 2017 on the Hudson River in a direct attempt to harm a CCP whistleblower..

Chuck Callesto

6,048,430 views • 2 years ago

FASTER for #Amiga OCS/ECS: I never imagined saying this in a million years, but I seem to be struggling to get the performance I need out of the blitter, and I don't even have all the stuff on screen yet! Anybody know a faster way of rendering bobs than the cookie cutter method?

FASTER for #Amiga OCS/ECS: I never imagined saying this in a million years, but I seem to be struggling to get the performance I need out of the blitter, and I don't even have all the stuff on screen yet! Anybody know a faster way of rendering bobs than the cookie cutter method?

Jonathan Thomas @atariste.bsky.social

13,897 views • 1 year ago

Any tips on how not to over rendering hair highlights?? I have a bad habit on doing that and hard to ignore it by renoizon

Any tips on how not to over rendering hair highlights?? I have a bad habit on doing that and hard to ignore it by renoizon

DrFonts©

340,822 views • 4 months ago

Linework is NOW LIVE on the Asset Store! A practical toolkit for rendering outlines, highlights and fills in Unity 🖍️ Get Linework here >

Linework is NOW LIVE on the Asset Store! A practical toolkit for rendering outlines, highlights and fills in Unity 🖍️ Get Linework here >

Alex Ameye

30,110 views • 2 years ago

Half of my brain that loves a good rendering - “wow this is sick” The other half that lives in reality - “where the fuck are we going to park?”

Half of my brain that loves a good rendering - “wow this is sick” The other half that lives in reality - “where the fuck are we going to park?”

Big Cat

4,521,657 views • 2 years ago

VideoRF: Rendering Dynamic Radiance Fields as 2D Feature Video Streams paper page: Neural Radiance Fields (NeRFs) excel in photorealistically rendering static scenes. However, rendering dynamic, long-duration radiance fields on ubiquitous devices remains challenging, due to data storage and computational constraints. In this paper, we introduce VideoRF, the first approach to enable real-time streaming and rendering of dynamic radiance fields on mobile platforms. At the core is a serialized 2D feature image stream representing the 4D radiance field all in one. We introduce a tailored training scheme directly applied to this 2D domain to impose the temporal and spatial redundancy of the feature image stream. By leveraging the redundancy, we show that the feature image stream can be efficiently compressed by 2D video codecs, which allows us to exploit video hardware accelerators to achieve real-time decoding. On the other hand, based on the feature image stream, we propose a novel rendering pipeline for VideoRF, which has specialized space mappings to query radiance properties efficiently. Paired with a deferred shading model, VideoRF has the capability of real-time rendering on mobile devices thanks to its efficiency. We have developed a real-time interactive player that enables online streaming and rendering of dynamic scenes, offering a seamless and immersive free-viewpoint experience across a range of devices, from desktops to mobile phones.

VideoRF: Rendering Dynamic Radiance Fields as 2D Feature Video Streams paper page: Neural Radiance Fields (NeRFs) excel in photorealistically rendering static scenes. However, rendering dynamic, long-duration radiance fields on ubiquitous devices remains challenging, due to data storage and computational constraints. In this paper, we introduce VideoRF, the first approach to enable real-time streaming and rendering of dynamic radiance fields on mobile platforms. At the core is a serialized 2D feature image stream representing the 4D radiance field all in one. We introduce a tailored training scheme directly applied to this 2D domain to impose the temporal and spatial redundancy of the feature image stream. By leveraging the redundancy, we show that the feature image stream can be efficiently compressed by 2D video codecs, which allows us to exploit video hardware accelerators to achieve real-time decoding. On the other hand, based on the feature image stream, we propose a novel rendering pipeline for VideoRF, which has specialized space mappings to query radiance properties efficiently. Paired with a deferred shading model, VideoRF has the capability of real-time rendering on mobile devices thanks to its efficiency. We have developed a real-time interactive player that enables online streaming and rendering of dynamic scenes, offering a seamless and immersive free-viewpoint experience across a range of devices, from desktops to mobile phones.

AK

38,686 views • 2 years ago

The AAA norm is to release half broken on PC. Dragon Age: The Veilguard bucks this trend offering a PC-tailored user experience that scales well above the consoles. You will spend your time staring at its slick hair rendering and not at a frame-time graph.

The AAA norm is to release half broken on PC. Dragon Age: The Veilguard bucks this trend offering a PC-tailored user experience that scales well above the consoles. You will spend your time staring at its slick hair rendering and not at a frame-time graph.

Alexander Battaglia

352,023 views • 1 year ago

Here's a work-in-progress look at our updated "Prioritize Performance" mode running on PS5. The team has addressed rendering issues from the Open Beta Test and is making steady improvements for launch. #MHWilds

Here's a work-in-progress look at our updated "Prioritize Performance" mode running on PS5. The team has addressed rendering issues from the Open Beta Test and is making steady improvements for launch. #MHWilds

Monster Hunter

320,494 views • 1 year ago

I've compiled some performance tips gleaned from working on my 1 million GPU AI steering agents project. I want to see Twitter compression destroy this video. 🚀 GPGPU: N-Body - O(n^2) - simulations are easily processed in parallel. GPUs are really good at parallel processing = 🫶. 🌐 Spatial hashing: Accelerate local group search via sorting into an unbounded grid (int3 cell -> 1D hash/array index). ⏩ Asynchronous compute: Once the data is on the GPU, both the simulation and rendering are handled there. There's no need to synchronize the physics step to visual update frames with decoupled rendering (see below). 🔄 Batched/staggered update cycle: Spread updates over multiple dispatches/frames. 🎭 Decoupled simulation-rendering: Simulation step can be much lower than visual updates. Use interpolation and/or simple velocity integration. In my case I'm passing the smoothed-out position and velocity of the boid 'particles' to VFX Graph for rendering. With these techniques you can get 100,000 boids even mobile/smartphones. 📱 Please don't mind the emojis, I like to flavour and stylize my text. #gamedev #indiedev

I've compiled some performance tips gleaned from working on my 1 million GPU AI steering agents project. I want to see Twitter compression destroy this video. 🚀 GPGPU: N-Body - O(n^2) - simulations are easily processed in parallel. GPUs are really good at parallel processing = 🫶. 🌐 Spatial hashing: Accelerate local group search via sorting into an unbounded grid (int3 cell -> 1D hash/array index). ⏩ Asynchronous compute: Once the data is on the GPU, both the simulation and rendering are handled there. There's no need to synchronize the physics step to visual update frames with decoupled rendering (see below). 🔄 Batched/staggered update cycle: Spread updates over multiple dispatches/frames. 🎭 Decoupled simulation-rendering: Simulation step can be much lower than visual updates. Use interpolation and/or simple velocity integration. In my case I'm passing the smoothed-out position and velocity of the boid 'particles' to VFX Graph for rendering. With these techniques you can get 100,000 boids even mobile/smartphones. 📱 Please don't mind the emojis, I like to flavour and stylize my text. #gamedev #indiedev

Mirza Beig

13,296 views • 2 years ago

Never did I expect this to happen! (BLM) Sean Barrett joined me to discuss his legendary work on software rendering! Before there were GPUs, two extraordinary minds led the frontier of rendering on CPUs. When John Carmack worked on Quake, (BLM) Sean Barrett championed rendering technology at famed Looking Glass Studios which resulted in groundbreaking Thief: The Dark Project. Join us for a 5h deep dive in a world of incredible optimizations, counting cycles and ridiculous tricks! (BLM) Sean Barrett, it was an honor! Thank you so much for your time and openness! Loved that discussion, the only regret is that my brain was fried after 5h and couldn't handle anymore! Thank you!

Never did I expect this to happen! (BLM) Sean Barrett joined me to discuss his legendary work on software rendering! Before there were GPUs, two extraordinary minds led the frontier of rendering on CPUs. When John Carmack worked on Quake, (BLM) Sean Barrett championed rendering technology at famed Looking Glass Studios which resulted in groundbreaking Thief: The Dark Project. Join us for a 5h deep dive in a world of incredible optimizations, counting cycles and ridiculous tricks! (BLM) Sean Barrett, it was an honor! Thank you so much for your time and openness! Loved that discussion, the only regret is that my brain was fried after 5h and couldn't handle anymore! Thank you!

Łukasz | Wookash Podcast

23,958 views • 1 year ago

Get ready to have your mind blown in my next show with remote viewer #001, Joe McMoneagle. In this particular session, Joe remote views 1,000,000 B.C. and goes on to describe a pyramid on Mars that is larger than Giza, with remnants of human-like beings. #shawnryanshow

Get ready to have your mind blown in my next show with remote viewer #001, Joe McMoneagle. In this particular session, Joe remote views 1,000,000 B.C. and goes on to describe a pyramid on Mars that is larger than Giza, with remnants of human-like beings. #shawnryanshow

Shawn Ryan

73,807 views • 2 years ago

With the latest fixes in ds4 I can now get it to build and iterate on a little TUI Tetris game just fine. Pretty damn cool (yes, it made some rendering errors)

With the latest fixes in ds4 I can now get it to build and iterate on a little TUI Tetris game just fine. Pretty damn cool (yes, it made some rendering errors)

Armin Ronacher ⇌

55,442 views • 4 months ago

SharedNeRF: Leveraging Photorealistic and View-dependent Rendering for Real-time and Remote Collaboration Abstract (excerpt): When collaborators are remote, coordinating the sharing of views of their physical environment becomes challenging. Video-conferencing tools often do not provide the desired viewpoints for a remote viewer. While RGB-D cameras offer 3D views, they lack the necessary fidelity. We introduce SharedNeRF, designed to enhance synchronous remote collaboration by leveraging the photorealistic and view-dependent nature of Neural Radiance Field (NeRF). The system complements the higher visual quality of the NeRF rendering with the instantaneity of a point cloud and combines them through carefully accommodating the dynamic elements within the shared space, such as hand gestures and moving objects. The system employs a head-mounted camera for data collection, creating a volumetric task space on the fly and updating it as the task space changes.

SharedNeRF: Leveraging Photorealistic and View-dependent Rendering for Real-time and Remote Collaboration Abstract (excerpt): When collaborators are remote, coordinating the sharing of views of their physical environment becomes challenging. Video-conferencing tools often do not provide the desired viewpoints for a remote viewer. While RGB-D cameras offer 3D views, they lack the necessary fidelity. We introduce SharedNeRF, designed to enhance synchronous remote collaboration by leveraging the photorealistic and view-dependent nature of Neural Radiance Field (NeRF). The system complements the higher visual quality of the NeRF rendering with the instantaneity of a point cloud and combines them through carefully accommodating the dynamic elements within the shared space, such as hand gestures and moving objects. The system employs a head-mounted camera for data collection, creating a volumetric task space on the fly and updating it as the task space changes.

MrNeRF

10,615 views • 1 year ago

You can do some pretty fun realtime VFX once the depth output is just another (Type)GPU buffer :D I basically yoinked my old 3D fire sim (that is actually a fluid sim but do not tell anyone) and plugged it into the depth pipeline together with hand tracking Everything here is built from scratch in TypeGPU, so inference, simulation and rendering can share GPU resources and interleave passes directly. That’s 2 NN inferences, a full 3D fluid sim, relighting and rendering in one command encoder, still under 16 ms with no CPU sync in between

You can do some pretty fun realtime VFX once the depth output is just another (Type)GPU buffer :D I basically yoinked my old 3D fire sim (that is actually a fluid sim but do not tell anyone) and plugged it into the depth pipeline together with hand tracking Everything here is built from scratch in TypeGPU, so inference, simulation and rendering can share GPU resources and interleave passes directly. That’s 2 NN inferences, a full 3D fluid sim, relighting and rendering in one command encoder, still under 16 ms with no CPU sync in between

Konrad Reczko

26,866 views • 18 days ago