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random model find; might be the best 3D multi-person pose estimation I have ever seen; it's Apache-2.0 Multi-HMR: Regressing Whole-Body Human Meshes for Multiple Persons in a Single Shot; ECCV2024

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SkalskiP

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31,150 просмотров • 1 год назад •via X (Twitter)

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Meta dropped not just SAM-3 yesterday but also SAM-3D (for objects and humans)🤯 SAM 3D Body is a promptable model for recovering full-body 3D human meshes from a single image. Seems to be the new SOTA
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Meta dropped not just SAM-3 yesterday but also SAM-3D (for objects and humans)🤯 SAM 3D Body is a promptable model for recovering full-body 3D human meshes from a single image. Seems to be the new SOTA

Niels Rogge

125,157 просмотров • 8 месяцев назад

I found a mind-blowing Seedance 2.0 hack no one is talking about. Just upload the first and last frame of your story and prompt: "Show me what happens in between. USE MULTIPLE CAMERA ANGLES" It will generate the entire multi-shot video for you 🤯 10 examples 👇
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I found a mind-blowing Seedance 2.0 hack no one is talking about. Just upload the first and last frame of your story and prompt: "Show me what happens in between. USE MULTIPLE CAMERA ANGLES" It will generate the entire multi-shot video for you 🤯 10 examples 👇

Framer 🇱🇹

134,080 просмотров • 4 месяцев назад

hi all, excited to join! i'm building an expressive mini "shoggoth" robot which will eventually be hooked up to gpt4o realtime voice. i'm currently working on the low-level policies, which are trained in a mujoco simulation with RL. to delay working on raw-pixels for now, i trained a pose-estimation model using deeplabcut and triangulate the position in 3d space using the stereo cameras. eventually, i'll use gpt4o's tool calling capabilities to activate several of these policies (closed and open loop) based on the dialog flow! captions: manual actuation of the tentacle / 3d pose estimation / target design
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hi all, excited to join! i'm building an expressive mini "shoggoth" robot which will eventually be hooked up to gpt4o realtime voice. i'm currently working on the low-level policies, which are trained in a mujoco simulation with RL. to delay working on raw-pixels for now, i trained a pose-estimation model using deeplabcut and triangulate the position in 3d space using the stereo cameras. eventually, i'll use gpt4o's tool calling capabilities to activate several of these policies (closed and open loop) based on the dialog flow! captions: manual actuation of the tentacle / 3d pose estimation / target design

Matthieu LC

45,587 просмотров • 1 год назад

As an AI consultant who tests models daily, I find Kling 3.0 to be the first model that truly and precisely understands multi‑person dialogue. Its native audio can clearly and unambiguously reference specific characters in complex scenes, eliminating any confusion between characters and voices, making narrative logic reliable for the first time.
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As an AI consultant who tests models daily, I find Kling 3.0 to be the first model that truly and precisely understands multi‑person dialogue. Its native audio can clearly and unambiguously reference specific characters in complex scenes, eliminating any confusion between characters and voices, making narrative logic reliable for the first time.

SARAH

98,063 просмотров • 6 месяцев назад

It's time Kling AI 3.0 just dropped and it is amazing! I never say this but this one is a game changer! The quality of output is like nothing we have never seen before, truly SOTA. Ill let the output do the talking here in this 15 second raw multi-shot. 🔊🎧
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It's time Kling AI 3.0 just dropped and it is amazing! I never say this but this one is a game changer! The quality of output is like nothing we have never seen before, truly SOTA. Ill let the output do the talking here in this 15 second raw multi-shot. 🔊🎧

Stevie Mac

56,380 просмотров • 6 месяцев назад

NVIDIA DROPPED A MOTION DIFFUSION MODEL FOR HUMANOID ROBOTS trained on 700 hours of mocap data kimodo generates high-quality 3D human and robot motions from text prompts you control it with: → full-body pose keyframes → end-effector positions/rotations → 2D paths and waypoints works on human skeletons and unitree G1 robot plug the outputs directly into mujoco or retarget to other robots using GMR has a web-based interactive demo with a timeline editor. runs locally needs ~17GB VRAM to run inference open source under apache 2.0
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NVIDIA DROPPED A MOTION DIFFUSION MODEL FOR HUMANOID ROBOTS trained on 700 hours of mocap data kimodo generates high-quality 3D human and robot motions from text prompts you control it with: → full-body pose keyframes → end-effector positions/rotations → 2D paths and waypoints works on human skeletons and unitree G1 robot plug the outputs directly into mujoco or retarget to other robots using GMR has a web-based interactive demo with a timeline editor. runs locally needs ~17GB VRAM to run inference open source under apache 2.0

Vaishnavi

17,572 просмотров • 3 месяцев назад

Human3R: Everyone Everywhere All at Once Note: I recorded the video from the interactive demo on their project page (linked in the comment below). Abstract (excerpt): Human3R jointly recovers global multi-person SMPL-X bodies ("everyone"), dense 3D scenes ("everywhere"), and camera trajectories in a single forward pass ("all-at-once"). Our method builds upon the 4D online reconstruction model CUT3R and uses parameter-efficient visual prompt tuning to preserve CUT3R's rich spatiotemporal priors while enabling direct readout of multiple SMPL-X bodies. Human3R is a unified model that eliminates heavy dependencies and iterative refinement. After being trained on the relatively small-scale synthetic dataset BEDLAM for just one day on one GPU, it achieves superior performance with remarkable efficiency: it reconstructs multiple humans in a one-shot manner, along with 3D scenes, in one stage, at real-time speed (15 FPS) with a low memory footprint (8 GB).
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Human3R: Everyone Everywhere All at Once Note: I recorded the video from the interactive demo on their project page (linked in the comment below). Abstract (excerpt): Human3R jointly recovers global multi-person SMPL-X bodies ("everyone"), dense 3D scenes ("everywhere"), and camera trajectories in a single forward pass ("all-at-once"). Our method builds upon the 4D online reconstruction model CUT3R and uses parameter-efficient visual prompt tuning to preserve CUT3R's rich spatiotemporal priors while enabling direct readout of multiple SMPL-X bodies. Human3R is a unified model that eliminates heavy dependencies and iterative refinement. After being trained on the relatively small-scale synthetic dataset BEDLAM for just one day on one GPU, it achieves superior performance with remarkable efficiency: it reconstructs multiple humans in a one-shot manner, along with 3D scenes, in one stage, at real-time speed (15 FPS) with a low memory footprint (8 GB).

MrNeRF

35,783 просмотров • 10 месяцев назад

If you typically stream with a 3D model, I highly recommend that you pose your model when you’re not doing full body mocap instead of letting it stay in the stiff generic pose! It makes a huge difference in how your energy is conveyed (ᗒ⩊ᗕ)⸝ި ʕᦏ⌎ I’m only saying this because I noticed this a lot with my own 3D kids, but here’s a comparison showcase: ← default pose & default pendulum physics in Warudo → custom poses & slightly adjusted pendulum physics If you find it hard to pose within Warudo by using bone offsets or none of the existing poses in Warudo vibe with you, you can make your own like me! I made my custom poses in “VRM Posing Desktop” (this is the BEST vrm posing app I’ve ever used in the past 3 years) and exported them as Unity anims then dropped them into Warudo’s animation folder! You can then make a simple blueprint in Warudo to toggle between poses and make your model look more alive! It should fit especially well for just chatting streams 🙂‍↕️✨
0:10

If you typically stream with a 3D model, I highly recommend that you pose your model when you’re not doing full body mocap instead of letting it stay in the stiff generic pose! It makes a huge difference in how your energy is conveyed (ᗒ⩊ᗕ)⸝ި ʕᦏ⌎ I’m only saying this because I noticed this a lot with my own 3D kids, but here’s a comparison showcase: ← default pose & default pendulum physics in Warudo → custom poses & slightly adjusted pendulum physics If you find it hard to pose within Warudo by using bone offsets or none of the existing poses in Warudo vibe with you, you can make your own like me! I made my custom poses in “VRM Posing Desktop” (this is the BEST vrm posing app I’ve ever used in the past 3 years) and exported them as Unity anims then dropped them into Warudo’s animation folder! You can then make a simple blueprint in Warudo to toggle between poses and make your model look more alive! It should fit especially well for just chatting streams 🙂‍↕️✨

𝗞𝗔𝗥𝗜𝗛𝗔 🌘🍀 3D Artist ☻

15,700 просмотров • 5 месяцев назад

Seedance 2 just dropped. This was my first test and it changed everything. Perfect lip sync. Realistic movement. Multi-shot support. Natural voice completely human, zero robotic tone. And the SFX? It nailed the sound effects automatically. Every detail synced perfectly. This is a game-changer. From now on, automation isn't theory it's real. And yes, it's happening on Crafft. With a high-fidelity model like this + the right prompting rules and skills, you can generate endless variations that work every single time. Crafft agents will handle the whole process. One model, infinite outputs. You can try Seedance 2 on CapCut right now. RT + comment "PROMPT" and I'll send you the exact setup (follow so I can DM)
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Seedance 2 just dropped. This was my first test and it changed everything. Perfect lip sync. Realistic movement. Multi-shot support. Natural voice completely human, zero robotic tone. And the SFX? It nailed the sound effects automatically. Every detail synced perfectly. This is a game-changer. From now on, automation isn't theory it's real. And yes, it's happening on Crafft. With a high-fidelity model like this + the right prompting rules and skills, you can generate endless variations that work every single time. Crafft agents will handle the whole process. One model, infinite outputs. You can try Seedance 2 on CapCut right now. RT + comment "PROMPT" and I'll send you the exact setup (follow so I can DM)

Ahmad

31,486 просмотров • 5 месяцев назад

Codex update 0.105.0 is out! Despite the fairly pedestrian changelog, this one's a doosie. It's a laundry list of quality of life improvements across the board. - Wispr Voice dictation (hold space to talk) - Theme picker - Codex can prevent sleep on Linux & Windows (I just know there's a joke in there) - Customize Plan Mode reasoning - Many other fixes/updates There's also a complete overhaul to subagents: - New names for readability - Visual display overhaul (way cleaner) - Allow for multi-layered subagent depth (max_depth) - Custom multi-agent role definitions (custom subagents) - /agents now shows both agent names, agent roles, and "dead agents" for auditibility This is the largest single update of Codex I've ever seen! Absolutely massive if you love to use multi-agents. To turn on Voice Transcription, enable: [features] voice_transcription = true Does not work on Linux yet. Well done OpenAI Developers 👏
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Codex update 0.105.0 is out! Despite the fairly pedestrian changelog, this one's a doosie. It's a laundry list of quality of life improvements across the board. - Wispr Voice dictation (hold space to talk) - Theme picker - Codex can prevent sleep on Linux & Windows (I just know there's a joke in there) - Customize Plan Mode reasoning - Many other fixes/updates There's also a complete overhaul to subagents: - New names for readability - Visual display overhaul (way cleaner) - Allow for multi-layered subagent depth (max_depth) - Custom multi-agent role definitions (custom subagents) - /agents now shows both agent names, agent roles, and "dead agents" for auditibility This is the largest single update of Codex I've ever seen! Absolutely massive if you love to use multi-agents. To turn on Voice Transcription, enable: [features] voice_transcription = true Does not work on Linux yet. Well done OpenAI Developers 👏

am.will

115,392 просмотров • 5 месяцев назад

Before the week ends, let's acknowledge one of the most INSANE week ever for open AI, with 25+ notable open-weight drops across every modality: 🧠 LLMs → NVIDIA Nemotron 3 Ultra: 550B hybrid Mamba-MoE, only 55B active, 1M context, MMLU 89.1. NVFP4 variant claims ~5x throughput on Blackwell. First openly-weighted 550B hybrid Mamba-Transformer, closing the gap with frontier closed models. → Google Gemma 4 12B: fully open dense any-to-any (text/image/audio/video), 256k context, encoder-free, 140+ languages, AIME 2026 at 77.5. Shipped with a 23-checkpoint QAT wave (mobile ONNX + MLX). Most deployable model of the week. → StepFun Step-3.7-Flash: 198B sparse MoE VLM, ~11B active, SWE-Bench PRO 56.3. Apache 2.0. → Liquid AI LFM2.5-8B-A1B: edge MoE, just 1.5B active, 128k ctx, MATH500 88.8, MLX-ready. Best on-device option this week. → JetBrains Mellum2-12B-A2.5B-Thinking: their first open MoE, near-Qwen3-14B coding at 2.5B active. Apache 2.0. 🎨 Image gen (the surprise of the week) → Ideogram 4: their FIRST-EVER open weights. 9.3B flow-matching DiT trained from scratch. #2 overall behind GPT Image 2, top open-weight model on Design Arena + LMArena. Strongest open checkpoint for text-rich images, full stop. It has taste. Still can't believe this is open weights. 🔊 Audio & Speech (a breakout week for open TTS, 4 labs shipped) → Boson Higgs Audio v3 4B: 102 languages, 21 emotions, singing/whispering/shouting, sub-second TTFA. → RedNote dots.tts: the only fully continuous (no codec) open TTS pipeline, Apache 2.0. → Google Magenta RealTime 2: real-time music gen, <200ms latency, text+audio+MIDI. multimodalart ported it to PyTorch within hours with live ZeroGPU demos. → NVIDIA Nemotron-3.5 ASR: 600M streaming, 17x more concurrent streams vs Parakeet RNNT 1.1B. 👁️ Vision & VLMs → PaddleOCR-VL-1.6: SOTA document parsing at 1B params, Apache 2.0. → Baidu NAVA: 6.3B joint audio-video gen, best-in-class A/V sync, Apache 2.0. 🎬 Video, 3D & World Models → NVIDIA Cosmos3-Super: 64B omnimodal world model coupling action trajectories with video+audio gen, for Physical AI. → JD JoyAI-Echo: up to 5-min multi-shot text-to-video on LTX-2.3. → ByteDance Bernini-R + VAST TripoSplat (single-image-to-3D Gaussian splats, MIT).
0:14

Before the week ends, let's acknowledge one of the most INSANE week ever for open AI, with 25+ notable open-weight drops across every modality: 🧠 LLMs → NVIDIA Nemotron 3 Ultra: 550B hybrid Mamba-MoE, only 55B active, 1M context, MMLU 89.1. NVFP4 variant claims ~5x throughput on Blackwell. First openly-weighted 550B hybrid Mamba-Transformer, closing the gap with frontier closed models. → Google Gemma 4 12B: fully open dense any-to-any (text/image/audio/video), 256k context, encoder-free, 140+ languages, AIME 2026 at 77.5. Shipped with a 23-checkpoint QAT wave (mobile ONNX + MLX). Most deployable model of the week. → StepFun Step-3.7-Flash: 198B sparse MoE VLM, ~11B active, SWE-Bench PRO 56.3. Apache 2.0. → Liquid AI LFM2.5-8B-A1B: edge MoE, just 1.5B active, 128k ctx, MATH500 88.8, MLX-ready. Best on-device option this week. → JetBrains Mellum2-12B-A2.5B-Thinking: their first open MoE, near-Qwen3-14B coding at 2.5B active. Apache 2.0. 🎨 Image gen (the surprise of the week) → Ideogram 4: their FIRST-EVER open weights. 9.3B flow-matching DiT trained from scratch. #2 overall behind GPT Image 2, top open-weight model on Design Arena + LMArena. Strongest open checkpoint for text-rich images, full stop. It has taste. Still can't believe this is open weights. 🔊 Audio & Speech (a breakout week for open TTS, 4 labs shipped) → Boson Higgs Audio v3 4B: 102 languages, 21 emotions, singing/whispering/shouting, sub-second TTFA. → RedNote dots.tts: the only fully continuous (no codec) open TTS pipeline, Apache 2.0. → Google Magenta RealTime 2: real-time music gen, <200ms latency, text+audio+MIDI. multimodalart ported it to PyTorch within hours with live ZeroGPU demos. → NVIDIA Nemotron-3.5 ASR: 600M streaming, 17x more concurrent streams vs Parakeet RNNT 1.1B. 👁️ Vision & VLMs → PaddleOCR-VL-1.6: SOTA document parsing at 1B params, Apache 2.0. → Baidu NAVA: 6.3B joint audio-video gen, best-in-class A/V sync, Apache 2.0. 🎬 Video, 3D & World Models → NVIDIA Cosmos3-Super: 64B omnimodal world model coupling action trajectories with video+audio gen, for Physical AI. → JD JoyAI-Echo: up to 5-min multi-shot text-to-video on LTX-2.3. → ByteDance Bernini-R + VAST TripoSplat (single-image-to-3D Gaussian splats, MIT).

Victor M

540,629 просмотров • 2 месяцев назад

It's the final 30 seconds of the game...how does anyone have the composure to find a world class pass like this?? We might not have won today but I reckon his 'no pressure' clutch mentality will be immense for us in years to come, just like the last game.
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It's the final 30 seconds of the game...how does anyone have the composure to find a world class pass like this?? We might not have won today but I reckon his 'no pressure' clutch mentality will be immense for us in years to come, just like the last game.

Cityzen_Sameer

662,075 просмотров • 7 месяцев назад

Hato might have the fastest jockey I have ever seen in football Bailey is QUICK but even with his faints, he couldn’t get past his jockey &amp; only did when Hato hesitated for the tackle That is genuinely a crazy physical trait to be able to move like that
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Hato might have the fastest jockey I have ever seen in football Bailey is QUICK but even with his faints, he couldn’t get past his jockey &amp; only did when Hato hesitated for the tackle That is genuinely a crazy physical trait to be able to move like that

Vᴅʏᴋ

171,409 просмотров • 5 месяцев назад

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.
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Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

MrNeRF

52,849 просмотров • 1 год назад

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!
0:51

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!

@levelsio

119,210 просмотров • 1 год назад

😈 While we are waiting for Seedance 2.0, Kling 3.0 is still an amazing option. Especially Omni mode when working with character. Just generate multiple facial expressions and provide them as reference images. Then make a multi-shot prompt and you will have amazing character consistency. Prompt: shot 1: the devil talking while turning his head giving a side eye. shot 2: extreme close up of the devils eye. he winks. shot 3: extreme close up of the mouth while he turns agry. shot 4: extreme close up of his ear. small hairs are showing. shot 5. the devil sticks out his tongue while saying a licking sound shot 6: zoom out and show the devil standing in hell. Only a silhuette is visible. Link to Spaces below
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😈 While we are waiting for Seedance 2.0, Kling 3.0 is still an amazing option. Especially Omni mode when working with character. Just generate multiple facial expressions and provide them as reference images. Then make a multi-shot prompt and you will have amazing character consistency. Prompt: shot 1: the devil talking while turning his head giving a side eye. shot 2: extreme close up of the devils eye. he winks. shot 3: extreme close up of the mouth while he turns agry. shot 4: extreme close up of his ear. small hairs are showing. shot 5. the devil sticks out his tongue while saying a licking sound shot 6: zoom out and show the devil standing in hell. Only a silhuette is visible. Link to Spaces below

Martin LeBlanc

34,681 просмотров • 5 месяцев назад

✦ 3D Model ⋮ showcase ✦ ── .⟢ for Alettie 💖 I’ve had the wonderful opportunity to work on the cutest magical girl design ₊‧°𐐪♡𐑂°‧₊ It was a project full of new challenges for me, and I had to most fun trying to figure out how to best express everything in 3D! My favourite parts have got to be the layered skirt physics and the flounce modelling!! There are still many things I want to showcase for this model (details, mahou props✨, wings animation🪽) so I’ll be posting about them thru the week, I hope y’all won’t get sick of me 🥹 #by_KARIHA
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✦ 3D Model ⋮ showcase ✦ ── .⟢ for Alettie 💖 I’ve had the wonderful opportunity to work on the cutest magical girl design ₊‧°𐐪♡𐑂°‧₊ It was a project full of new challenges for me, and I had to most fun trying to figure out how to best express everything in 3D! My favourite parts have got to be the layered skirt physics and the flounce modelling!! There are still many things I want to showcase for this model (details, mahou props✨, wings animation🪽) so I’ll be posting about them thru the week, I hope y’all won’t get sick of me 🥹 #by_KARIHA

𝗞𝗔𝗥𝗜𝗛𝗔 🌘🍀 3D Artist ☻

32,235 просмотров • 1 месяц назад

Seedance 2.0 - Cinematic Summoning VFX Prompt This prompt generates a multi-cut VFX showcase with a clear progression. It’s designed for realistic 3D with grounded physics, natural character motion and readable, production-style VFX. You don’t need to define the summon, but you can specify what is being summoned if you want control. If you leave it open, the model decides based on the character sheet. You can find gpt image 2 character sheet prompt in the quoted post. Also, this isn’t limited to summoning. You can reuse the same VFX structure for anything, just swap the effect logic. Just change the parts about summoning. Prompt in the replies 👇
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Seedance 2.0 - Cinematic Summoning VFX Prompt This prompt generates a multi-cut VFX showcase with a clear progression. It’s designed for realistic 3D with grounded physics, natural character motion and readable, production-style VFX. You don’t need to define the summon, but you can specify what is being summoned if you want control. If you leave it open, the model decides based on the character sheet. You can find gpt image 2 character sheet prompt in the quoted post. Also, this isn’t limited to summoning. You can reuse the same VFX structure for anything, just swap the effect logic. Just change the parts about summoning. Prompt in the replies 👇

Kōda

28,400 просмотров • 3 месяцев назад

Hip airplanes might be the single best exercise for runners. They've been in my routine for over a year now. I've been using them as a mobility drill. Bodyweight, slow, pushing the end range before a run. When my hip starts acting up, I hit a few reps of this goat mobility drill and usually find immediate relief after a few sets. One movement. Multiple benefits: → Foot splay → Lower leg rotation → Hip stability → Hip strength in multiple planes of motion Make this a staple in your exercise routine. Doesn't matter if you do it before or after your run or during your leg day routine.
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Hip airplanes might be the single best exercise for runners. They've been in my routine for over a year now. I've been using them as a mobility drill. Bodyweight, slow, pushing the end range before a run. When my hip starts acting up, I hit a few reps of this goat mobility drill and usually find immediate relief after a few sets. One movement. Multiple benefits: → Foot splay → Lower leg rotation → Hip stability → Hip strength in multiple planes of motion Make this a staple in your exercise routine. Doesn't matter if you do it before or after your run or during your leg day routine.

Tim Roozendaal

75,573 просмотров • 3 месяцев назад