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Viewport in → 4K out Left: Raw motion rig blocking the pose Right: Seedance 2.0 turning that exact motion into a stylized 4K warrior Cape sway, landing weight, punch follow-through — all carried straight from the reference. Feed it clean motion. The model handles the rest.

PixVerse

28,749 subscribers

24,859 次观看 • 1 个月前 •via X (Twitter)

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I built a Claude skill that makes motion-style animation. > Paste a reference image > It generates frames with Nano Banana 2 > and animates the video with Seedance 2.0 Everything runs in Claude through the Arcads MCP Comment "Motion", and I'll send you the skill

I built a Claude skill that makes motion-style animation. > Paste a reference image > It generates frames with Nano Banana 2 > and animates the video with Seedance 2.0 Everything runs in Claude through the Arcads MCP Comment "Motion", and I'll send you the skill

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Another look at Seedance 2.0 4K on TapNow. Fast cuts, close contact, and a field full of motion. The model keeps the image sharp without losing the feel of the play. Click the link below for more details.

Another look at Seedance 2.0 4K on TapNow. Fast cuts, close contact, and a field full of motion. The model keeps the image sharp without losing the feel of the play. Click the link below for more details.

TapNow

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🇬🇭ENTERTAINMENT: IRON BOY at Rapperholic 2025!! A poet in motion, turning pain into rhythm and truth into melody. Every line carried weight, every note spoke to the soul. #KobbyKyeiNews

🇬🇭ENTERTAINMENT: IRON BOY at Rapperholic 2025!! A poet in motion, turning pain into rhythm and truth into melody. Every line carried weight, every note spoke to the soul. #KobbyKyeiNews

#BoysLivesMatter

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I tested Seedance 2.0 vs Kling 3.0. The winner was obvious. Same prompt, same reference image, one scene: a boy rescuing his dog from an oncoming car. Seedance 2.0 came out ahead across the board: · Better character consistency — the boy's face and expression stayed recognizable through fast, dynamic motion. · More coherent physics — when the boy dives to save the dog, he actually lands on the other side of the road. In Kling 3.0, they just drop in place, which breaks the logic of the scene. · More natural motion — fluid movements without the exaggerated, theatrical quality that plagues most AI video. · Richer cinematic language — varied shot compositions including close-ups, camera transitions, and framing shifts that actively reinforce emotion and narrative tension. The gap suggests Seedance 2.0's training leans heavier into film language, not just motion synthesis.

I tested Seedance 2.0 vs Kling 3.0. The winner was obvious. Same prompt, same reference image, one scene: a boy rescuing his dog from an oncoming car. Seedance 2.0 came out ahead across the board: · Better character consistency — the boy's face and expression stayed recognizable through fast, dynamic motion. · More coherent physics — when the boy dives to save the dog, he actually lands on the other side of the road. In Kling 3.0, they just drop in place, which breaks the logic of the scene. · More natural motion — fluid movements without the exaggerated, theatrical quality that plagues most AI video. · Richer cinematic language — varied shot compositions including close-ups, camera transitions, and framing shifts that actively reinforce emotion and narrative tension. The gap suggests Seedance 2.0's training leans heavier into film language, not just motion synthesis.

Latte

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Seedance Playblast 2 Render Demo: VFX artist Doug Hogan built a workflow in ComfyUI that takes a raw Playblast and turns it into a finished shot using Seedance 2.0 - no traditional rendering pipeline. → Playblast = motion ground truth → Nanobanana Pro multi-angle grids = asset identity → Gemini node = auto-generated structured prompt → Seedance 2.0 ties it all together Raw CG animation → finished shot in minutes instead of hours. Click the link below for the workflow👇

Seedance Playblast 2 Render Demo: VFX artist Doug Hogan built a workflow in ComfyUI that takes a raw Playblast and turns it into a finished shot using Seedance 2.0 - no traditional rendering pipeline. → Playblast = motion ground truth → Nanobanana Pro multi-angle grids = asset identity → Gemini node = auto-generated structured prompt → Seedance 2.0 ties it all together Raw CG animation → finished shot in minutes instead of hours. Click the link below for the workflow👇

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AI video is moving way too fast. This entire sequence was generated using just the Seedance 2.0 x ChatGPT combo. 🎬 Anime Fight Previz (testing choreography & camera beats) ✨ Fashion Lookbook Motion (turning static fits into runway clips) I'm dropping the exact prompts in the comments below. 👇

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This entire ad? AI-generated. No camera, no studio, no actors. Putting Seedance 2.0 through a rigorous commercial test to see how it handles luxury product consistency, macro skin details, and 3D motion. Full prompt and storyboard details in the comments

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FATHELA ESQ

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I’ve stood on global stages, I’ve carried battles. I’ve held office. But through it all, I’ve remained one thing: A woman in motion. But motion means nothing if it only serves the self; the test of our time is greater. So here’s my birthday wish ….

I’ve stood on global stages, I’ve carried battles. I’ve held office. But through it all, I’ve remained one thing: A woman in motion. But motion means nothing if it only serves the self; the test of our time is greater. So here’s my birthday wish ….

Lisa Hanna™️

11,719 次观看 • 11 个月前

MotionGPT: Human Motion as a Foreign Language paper page: Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.

MotionGPT: Human Motion as a Foreign Language paper page: Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.

AK

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Kling 2.6 Motion Control is LIVE in Hedra. Video editing as you know it? Over. Swap characters in any scene while keeping the lighting, depth, and motion perfectly locked in. Forget After Effects or manual tracing. Just drop a clip and an image, Hedra handles the rest. All inside Hedra. Try it now.

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One simple watch photo → Pollo AI Mobile App. GPT Image 2 turned it into a flagship-worthy ad — crisp, clean, premium. Then Seedance 2.0 4K brought it to life: ultra-sharp clarity, cinematic motion, dangerously professional. What’s the difference between this and a real storefront ad? Honestly? Not much. If your products are drowning on Amazon, this is your wake-up call.

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0xTria

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Video Agent takes you from a single prompt to a finished video Today it got better. Seedance 2.0 is now built in Your Digital Twin in cinematic shots with real motion, the kind that used to take a production team RT + comment “VideoAgent” and we’ll DM 100 credits (must follow)

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HeyGen

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I put Seedance 2.0 in native 4k to the test and.... wow. Every water droplet, every blade of grass, every strand of hair has perfect detail. I usually generate in 720p and assumed the increase in resolution wouldn't be that noticeable but have been blown away by: -Increased prompt adherence -Improved motion interpolation (no intra-frame artifacts) -Complex camera moves -Cinematic colour grading A personal favourite is the handheld vlog style camera movement seen in this clip. It's almost impossible to do with traditional stop-motion filmmaking. I think this Is where AI shines best - where it allows you to do things you just couldn't do with traditional techniques. Watch the 4k film below and let me know what you think! You can try seedance 4k for yourself with: BytePlus Lumina

Soul Motion labs

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Seedance 2.0 vs Gemini Omni, tested under the same conditions 👀 I was working on a new storyboard. After generating it with Seedance 2.0, I gave the exact same prompt + storyboard reference + character reference to Gemini Omni as well. Result: Gemini Omni surprised me with style quality and got closer than I expected in prompt adherence. But Seedance still feels ahead for storyboard execution, motion energy, camera language and environmental interaction. Gemini looks good. Seedance feels directed. You can see them in the video and the storyboard sheet is shown at the end. Which one do you think is more useful for this kind of work?

Seedance 2.0 vs Gemini Omni, tested under the same conditions 👀 I was working on a new storyboard. After generating it with Seedance 2.0, I gave the exact same prompt + storyboard reference + character reference to Gemini Omni as well. Result: Gemini Omni surprised me with style quality and got closer than I expected in prompt adherence. But Seedance still feels ahead for storyboard execution, motion energy, camera language and environmental interaction. Gemini looks good. Seedance feels directed. You can see them in the video and the storyboard sheet is shown at the end. Which one do you think is more useful for this kind of work?

Kōda

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Cash Counter : watch 40 bills riffle through a counting machine. the gaps between notes follow an ease-in-out curve, so it starts slow, blurs through the middle, and eases out at the end, just like a real counter. designed in Paper, built in React + motion

Cash Counter : watch 40 bills riffle through a counting machine. the gaps between notes follow an ease-in-out curve, so it starts slow, blurs through the middle, and eases out at the end, just like a real counter. designed in Paper, built in React + motion

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Introducing the Illustrated Transformer in 3D 🚀 Fly through LLaMA like never before. See every tensor and operation in motion. Click any component to reveal the exact lines of code that run it. A new way to learn and teach LLMs. Try it out in the link below 👇

Introducing the Illustrated Transformer in 3D 🚀 Fly through LLaMA like never before. See every tensor and operation in motion. Click any component to reveal the exact lines of code that run it. A new way to learn and teach LLMs. Try it out in the link below 👇

alphaXiv

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$Example-based Motion Synthesis via Generative Motion Matching paper page: present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual artifacts, and tend to fail on large and complex skeletons, GenMM inherits the training-free nature and the superior quality of the well-known Motion Matching method. GenMM can synthesize a high-quality motion within a fraction of a second, even with highly complex and large skeletal structures. At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches. In addition to diverse motion generation, we show the versatility of our generative framework by extending it to a number of scenarios that are not possible with motion matching alone, including motion completion, key frame-guided generation, infinite looping, and motion reassembly.$

Example-based Motion Synthesis via Generative Motion Matching paper page: present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual artifacts, and tend to fail on large and complex skeletons, GenMM inherits the training-free nature and the superior quality of the well-known Motion Matching method. GenMM can synthesize a high-quality motion within a fraction of a second, even with highly complex and large skeletal structures. At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches. In addition to diverse motion generation, we show the versatility of our generative framework by extending it to a number of scenarios that are not possible with motion matching alone, including motion completion, key frame-guided generation, infinite looping, and motion reassembly.

AK

93,729 次观看 • 3 年前

We just tested what might be the most accurate motion transfer workflow we've seen so far. It's a little more involved than a standard workflow, but the results are pretty impressive. Here's the basic workflow: 1. Start with your reference footage. Create character and environment references. 2. Convert the reference footage into a depth map (with Depth-Anything tool on Fal: 3. (Optional) Layer on additional guidance like colorized character masks (with Meta's Segment Anything: and or skeleton poses (with 4. Feed the depth map into Seedance 2.0 Omni as your motion reference along with your character and environment references. 5. Generate your final video. After testing several variations, the depth map workflow consistently produced the cleanest and most accurate motion transfer. If you're trying to recreate specific performances or complex movement, this is one of the closest AI workflows we've seen to achieving production-quality results.

We just tested what might be the most accurate motion transfer workflow we've seen so far. It's a little more involved than a standard workflow, but the results are pretty impressive. Here's the basic workflow: 1. Start with your reference footage. Create character and environment references. 2. Convert the reference footage into a depth map (with Depth-Anything tool on Fal: 3. (Optional) Layer on additional guidance like colorized character masks (with Meta's Segment Anything: and or skeleton poses (with 4. Feed the depth map into Seedance 2.0 Omni as your motion reference along with your character and environment references. 5. Generate your final video. After testing several variations, the depth map workflow consistently produced the cleanest and most accurate motion transfer. If you're trying to recreate specific performances or complex movement, this is one of the closest AI workflows we've seen to achieving production-quality results.

Curious Refuge

29,932 次观看 • 16 天前

Seedance 2.0 is now in HeyGen Everyone's using Seedance to generate cinematic video with any character. We just made that character you. Introducing Avatar Shots - your likeness, consistent across scenes. Dynamic motion. Multiple avatars in the same shot. Same face. Way more range. Only available through business email verification for all regions except US and Japan. RT + comment "SEEDANCE" for 100 HeyGen credits (must follow)

Joshua Xu

600,580 次观看 • 4 个月前