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Famous YouTuber Badal beautifully explained the difference in compression rate of YouTube and IMAX 3D. In the last scene of ‘Rama’ Tease there was a deep ditch which went unnoticed because of YouTube compression but felt scary in 3D. The Previous adaptation of Ramayana has defamed the 3D to...

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KBP Reviews

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32,854 просмотров • 5 месяцев назад •via X (Twitter)

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First Interview of Namit Malhotra from the creator meet is out, Famous YouTuber JD shared his experience from Ramayana event. Namit Malhotra answered the VFX Criticism and explained difference in experience between YouTube and 3D IMAX. Namit also requested that “We are making it for people, VFX work, CGI, colour grading, Music, effects everything is WIP, Please have Faith on us”
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First Interview of Namit Malhotra from the creator meet is out, Famous YouTuber JD shared his experience from Ramayana event. Namit Malhotra answered the VFX Criticism and explained difference in experience between YouTube and 3D IMAX. Namit also requested that “We are making it for people, VFX work, CGI, colour grading, Music, effects everything is WIP, Please have Faith on us”

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#Ramayana 3D will be different from traditional 3D movies : MOC Ramayana 3D will offer you depth for immersive experience instead of creatures popping out of screen, Makers want you to be in the World of Ramayana
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#Ramayana 3D will be different from traditional 3D movies : MOC Ramayana 3D will offer you depth for immersive experience instead of creatures popping out of screen, Makers want you to be in the World of Ramayana

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The timeless story of Rama and Ravana, now experienced in 3D. 🏹 Our truth. Our history. Our Ramayana. Watch the Ramayana 3D Trailer on the ARR Immersive App for Apple Vision Pro, an immersive presentation on a scale not attempted before for an Indian film. Experience the epic with enhanced depth, detail and presence. The World Of Ramayana #Ramayana #ARRImmersive #AppleVisionPro
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The timeless story of Rama and Ravana, now experienced in 3D. 🏹 Our truth. Our history. Our Ramayana. Watch the Ramayana 3D Trailer on the ARR Immersive App for Apple Vision Pro, an immersive presentation on a scale not attempted before for an Indian film. Experience the epic with enhanced depth, detail and presence. The World Of Ramayana #Ramayana #ARRImmersive #AppleVisionPro

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The battle of good and evil is about to begin. RAMAYANA: RISE OF A LEGEND - the most immersive 3D cinematic experience ever - exclusively in theatres, IMAX and Premium Large Formats worldwide November 5.
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The battle of good and evil is about to begin. RAMAYANA: RISE OF A LEGEND - the most immersive 3D cinematic experience ever - exclusively in theatres, IMAX and Premium Large Formats worldwide November 5.

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The power of #blender 3d modeling and texture paint in one place. Watch the full video on my YouTube channel #b3d
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The power of #blender 3d modeling and texture paint in one place. Watch the full video on my YouTube channel #b3d

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Creators were shocked after watching ‘Rama’ glimpse in 3D, A creator who earlier used to criticise Rama Glimpse was shocked after watching it in IMAX 3D. He said it is completely different teaser and has never seen anything like this before, and almost every creator is sounding same. Namit and Team has a far more Greater Vision beyond Ramayana, they want to create a Universe of our Indian History.
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Creators were shocked after watching ‘Rama’ glimpse in 3D, A creator who earlier used to criticise Rama Glimpse was shocked after watching it in IMAX 3D. He said it is completely different teaser and has never seen anything like this before, and almost every creator is sounding same. Namit and Team has a far more Greater Vision beyond Ramayana, they want to create a Universe of our Indian History.

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DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior paper page: present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.
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DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior paper page: present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.

AK

161,530 просмотров • 3 лет назад

have been having a SUPER weird time with youtube compression while working on my short film - any youtube experts able to weigh in here? the attached video has some examples but the text summary is: - uploaded 20 versions of short film - shot on fx3/6, plan was to upload in prores422hq 4K - versions 1-4 were slightly outdated versions of the edit - v5 was finally ready to post but the compression quality on it nosedived compared to v2,v3,v4 - haven't been able to recreate the higher compression quality of v2-4 in ANY uploads since - on PC, v4 looks good 👍 in medium shadows such as in the intro scene, but blocky 🚨 in the dark corridor - on Mac, v4 looks good 👍 in the medium shadows AND good 👍 in the dark corridor - on PC, v5-v20 looks bad 🚨 in any shadows at all, and blocky 🚨 in the dark corridor - on Mac, v5-v20 looks bad 🚨 in the shadows and blocky (and flashes weirdly) 🚨 in the dark corridor - no matter what settings I try, I can't seem to recreate the compression of v2, v3, v4 - part of me wonders if it's because they were uploaded close to the posting of an actual published video which might mean youtube gave us favourable compression for a few vids before reverting? I really don't know tbh - note: the codec youtube is giving us for ALL the versions when displayed on windows are the same av1. on all mac versions it's vp9. doesn't seem to be caused by a codec difference - have tried uploading in 8k, different colour space, adding sharpen, h264 instead of prores 422hq, prores4444, etc etc. nothing has brought back the compression quality of v2-4, they all suffer from same problem - reuploading the source v4 file resulted in worse quality the second time seemingly? any ideas here would be super appreciated, this compression flashing and glitching through the shadows has been the bane of my existence all week lol. TeamYouTube would love if you could lend a hand here too
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have been having a SUPER weird time with youtube compression while working on my short film - any youtube experts able to weigh in here? the attached video has some examples but the text summary is: - uploaded 20 versions of short film - shot on fx3/6, plan was to upload in prores422hq 4K - versions 1-4 were slightly outdated versions of the edit - v5 was finally ready to post but the compression quality on it nosedived compared to v2,v3,v4 - haven't been able to recreate the higher compression quality of v2-4 in ANY uploads since - on PC, v4 looks good 👍 in medium shadows such as in the intro scene, but blocky 🚨 in the dark corridor - on Mac, v4 looks good 👍 in the medium shadows AND good 👍 in the dark corridor - on PC, v5-v20 looks bad 🚨 in any shadows at all, and blocky 🚨 in the dark corridor - on Mac, v5-v20 looks bad 🚨 in the shadows and blocky (and flashes weirdly) 🚨 in the dark corridor - no matter what settings I try, I can't seem to recreate the compression of v2, v3, v4 - part of me wonders if it's because they were uploaded close to the posting of an actual published video which might mean youtube gave us favourable compression for a few vids before reverting? I really don't know tbh - note: the codec youtube is giving us for ALL the versions when displayed on windows are the same av1. on all mac versions it's vp9. doesn't seem to be caused by a codec difference - have tried uploading in 8k, different colour space, adding sharpen, h264 instead of prores 422hq, prores4444, etc etc. nothing has brought back the compression quality of v2-4, they all suffer from same problem - reuploading the source v4 file resulted in worse quality the second time seemingly? any ideas here would be super appreciated, this compression flashing and glitching through the shadows has been the bane of my existence all week lol. TeamYouTube would love if you could lend a hand here too

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Titan After Earth. One of the last movies to do that 2D characters in a 3D environment thing that was popular in the 90s
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Yes, AI is here and 3D artists have to adapt. Here's one way I'm integrating AI with my 3D work, as a set extension. The BG is generated by Kling 3.0 and brought into my 3D scene so I can fully control the timing of the lights in the FG, which is 3D. Seedance 2.0 next!!

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I was in charge of making the plague mask of Rikku リックVEXPO26✗ 's model! you may think it looks 3d, it's because it is FUNCTIONALLY 3D! I basically have a copy of the mask, and glued them together with varying weights to make it look 3D. 1/3 #Live2D
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I was in charge of making the plague mask of Rikku リックVEXPO26✗ 's model! you may think it looks 3d, it's because it is FUNCTIONALLY 3D! I basically have a copy of the mask, and glued them together with varying weights to make it look 3D. 1/3 #Live2D

hamsterCrumbs | Live2D モデラー

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LLaVA-3D A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness Recent advancements in Large Multimodal Models (LMMs) have greatly enhanced their proficiency in 2D visual understanding tasks, enabling them to effectively process and understand images and videos. However, the development of LMMs with 3D-awareness for 3D scene understanding has been hindered by the lack of large-scale 3D vision-language datasets and powerful 3D encoders. In this paper, we introduce a simple yet effective framework called LLaVA-3D. Leveraging the strong 2D understanding priors from LLaVA, our LLaVA-3D efficiently adapts LLaVA for 3D scene understanding without compromising 2D understanding capabilities. To achieve this, we employ a simple yet effective representation, 3D Patch, which connects 2D CLIP patch features with their corresponding positions in 3D space. By integrating the 3D Patches into 2D LMMs and employing joint 2D and 3D vision-language instruction tuning, we establish a unified architecture for both 2D image understanding and 3D scene understanding. Experimental results show that LLaVA-3D converges 3.5x faster than existing 3D LMMs when trained on 3D vision-language datasets. Moreover, LLaVA-3D not only achieves state-of-the-art performance across various 3D tasks but also maintains comparable 2D image understanding and vision-language conversation capabilities with LLaVA.
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The challenge of creating this scene was from a 3D environment to a 2D painted environment. We used the character to bait-and-switch out the backgrounds. #2d #3d #animation

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Early in the development of BW1, there was a huge test map that demonstrated the 3D capabilities of the new engine. A big spiraling mountain with some cliffs and rivers around.

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