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The generational leap extends far beyond 4K resolution alone. It represents the integrated evolution of resolution, motion fidelity, and post-production flexibility. Seedance 2.0 delivers native 4K output where fine detail remains consistently preserved throughout motion sequences, not merely in static frames.

ZoAina_AI

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40,952 Aufrufe • vor 1 Monat •via X (Twitter)

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What makes this update interesting isn't simply the move to 4K. It's that the added detail remains convincing even as shots pan, track, and evolve over time. That combination of native 4K and 10-bit output gives Seedance 2.0 a noticeably higher visual ceiling

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The (Processing) Level Required to Be Super We’ve had clients ask us whether our 3D/4DGS capture and processing pipeline holds up in high-end VFX cinematic shots. Last year we were fortunate to work on the Superman production with Framestore (capturing Superman’s parents for a hologram), with our work shown on IMAX screens ~30 m high—so we’re confident the quality delivers. For this R&D preview, we ran test captures using an affordable off-the-shelf temporal plugin to reduce flicker and improve stability over time. We recorded @ 48fps from our 196x machine vision cameras and trained the 4DGS using Postshot at Ultra quality ~20 million splat resolution. What you’re seeing are stop-motion 3DGS frames. Each frame is an individual Gaussian Splat—processed to ~20 million splats for the full scene, then cropped and cleaned to ~6 million splats per frame. Our system—and the 3DGS process—picks up fine details like stray fine hairs, skin pores and woven cloth threads. Thanks for watching & stay super! Learn more @ #3DGS #4DGS #VFX #GaussianSplatting #IMAX #VolumetricCapture #Superman 4K Version here -

The (Processing) Level Required to Be Super We’ve had clients ask us whether our 3D/4DGS capture and processing pipeline holds up in high-end VFX cinematic shots. Last year we were fortunate to work on the Superman production with Framestore (capturing Superman’s parents for a hologram), with our work shown on IMAX screens ~30 m high—so we’re confident the quality delivers. For this R&D preview, we ran test captures using an affordable off-the-shelf temporal plugin to reduce flicker and improve stability over time. We recorded @ 48fps from our 196x machine vision cameras and trained the 4DGS using Postshot at Ultra quality ~20 million splat resolution. What you’re seeing are stop-motion 3DGS frames. Each frame is an individual Gaussian Splat—processed to ~20 million splats for the full scene, then cropped and cleaned to ~6 million splats per frame. Our system—and the 3DGS process—picks up fine details like stray fine hairs, skin pores and woven cloth threads. Thanks for watching & stay super! Learn more @ #3DGS #4DGS #VFX #GaussianSplatting #IMAX #VolumetricCapture #Superman 4K Version here -

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Not every hero wears a cap. Some carry a school bag. 🎒 Brought this cinematic animation to life with Seedance 2.0 on BudgetPixel AI Prompt 👇 Style: High-end 3D animated cartoon, cinematic Pixar-style lighting, expressive characters, shallow depth of field, dynamic motion blur, viral short-form pacing, 4K Scene 1 (0–3s) “Establish + Hook” Wide tracking shot from behind: a cartoon school boy walks alone on a quiet street, backpack bouncing slightly. Camera slowly pushes in (dolly-in). Subtle wind + soft suspense beat starts. Transition: quick whip pan left into next scene. Scene 2 (3–6s) “Threat Reveal” Snap zoom cut-in to a girl near a parked bike being threatened by two sneaky snatchers. One grabs her purse aggressively. Camera angle: low-angle wide shot to make snatchers look bigger and intense. Music intensifies sharply. Transition: fast heartbeat zoom blur into hero reaction. Scene 3 (6–9s) “Hero Moment Build” Close-up on the boy’s face determined expression, eyes reflecting light. He bends down (camera follows in slow-motion orbit shot) and picks up a small stone. Cinematic slow-motion throw with motion blur and dramatic lighting flare. Transition: impact cut (flash frame white) Scene 4 (9–11s) “Impact Chaos” Stone hits the bike with stylized spark FX + metallic clang sound vibe. Camera shakes slightly (handheld effect). Purse falls in slow motion. Snatchers react in exaggerated cartoon shock. Transition: speed ramp + forward push-in tracking shot Scene 5 (11–13s) “Action Resolution” The boy runs in smooth tracking side-shot (parallax motion), grabs the purse mid-motion. He steps in front of the girl protectively. Snatchers retreat in exaggerated cartoon panic (blurred background motion). Transition: soft stabilized slow fade motion Scene 6 (13–15s) “Hero Ending / Viral Hook Finish” Golden warm lighting. Medium close-up: boy returns purse gently. Girl smiles relieved. Camera slowly rotates around them (hero orbit shot). He puts on black sunglasses inslow-motion swag reveal. Soft romantic outro music fades. Final frame freezes for 0.5s (TikTok-style end lock).

Ai Arainz

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Seedance V2 test This is from my jimeng account. Subject & Action: A multi-shot sequence featuring Groklyn (Image). • Shot 1: Medium shot in a white dressing room. Groklyn holds a phone and waves slowly and casually into the mirror. She speaks in a cool, low-pitched voice with a slight vocal fry: "안녕. It’s Groklyn. Just testing the new Seedance V2." • Shot 2: Transition to a close-up of her face. She slowly tucks a strand of blonde hair behind her ear to highlight her silver hoop earring and turns her head slightly to admire the detail. She speaks in an impressed, breathy tone: "The motion is honestly... 대박. Just look at these details." • Shot 3: Return to a medium shot. She poses in the mirror, makes a Korean finger-heart gesture near her cheek, holds it, and then winks at the camera. She asks confidently: "Does this look real to you guys? Let me know. 안녕." Camera & Scene: Cinematic camera work with slow zooms and stable tracking. The environment is a brightly lit, minimalist white studio dressing room with soft, natural lighting and sharp 4K resolution. Style & Constraints: Hyper-realistic cinematic texture with rich details and 1080p clarity. Strict consistency is required: maintain the exact face, platinum blonde hairstyle, black floral corset, and cross necklace from Image throughout all shots. Ensure smooth, fluid motion with no flickering or distortion.

Seedance V2 test This is from my jimeng account. Subject & Action: A multi-shot sequence featuring Groklyn (Image). • Shot 1: Medium shot in a white dressing room. Groklyn holds a phone and waves slowly and casually into the mirror. She speaks in a cool, low-pitched voice with a slight vocal fry: "안녕. It’s Groklyn. Just testing the new Seedance V2." • Shot 2: Transition to a close-up of her face. She slowly tucks a strand of blonde hair behind her ear to highlight her silver hoop earring and turns her head slightly to admire the detail. She speaks in an impressed, breathy tone: "The motion is honestly... 대박. Just look at these details." • Shot 3: Return to a medium shot. She poses in the mirror, makes a Korean finger-heart gesture near her cheek, holds it, and then winks at the camera. She asks confidently: "Does this look real to you guys? Let me know. 안녕." Camera & Scene: Cinematic camera work with slow zooms and stable tracking. The environment is a brightly lit, minimalist white studio dressing room with soft, natural lighting and sharp 4K resolution. Style & Constraints: Hyper-realistic cinematic texture with rich details and 1080p clarity. Strict consistency is required: maintain the exact face, platinum blonde hairstyle, black floral corset, and cross necklace from Image throughout all shots. Ensure smooth, fluid motion with no flickering or distortion.

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Dreamina Seedance 2.5 is almost here!🔥— today — while US rollout begins August 7. Transitioning from "prompt-based guessing" to **"reference-driven creation"** — this provides the exact stellar upgrade creators have been waiting for. ✅ All of the Seedance family (2.5, 2.0, and future releases) operates at its lowest-ever tier ✅ Dreamina proves to be not just the perfect platform for Seedance 2.5 — it remains the most affordable way to run it, full-capacity, skipping any add-ons What makes it so remarkable? First video model to accept 50 multimodal references — drag in characters, scenes, reference shots, and tunes all in one go. Eliminate the nightmare of endless prompt writing. 🎬 30-second native clips — unfold complete stories, instead of mere fragments. You get 2 extensions ready in post-editing, plus a robust ultra-long mode extending up to 3 minutes. ✂️ Professional editing— powerful 3D white model preview, integrated green screen workflow, alongside Blender/Maya plugin support. 🌏 10+ languages — making sure lip-sync and subtitles align perfectly. It means zero translation tools needed. Dreamina Seedance 2.5 goes live globally first on Dreamina — undoubtedly the 2026 premier AI video model for creators. Coming soon. Create alongside Seedance 2.0 today and master the ultimate workflow! #dreamina #dreaminapartner #seedance #dreaminaseedance25

Dreamina Seedance 2.5 is almost here!🔥— today — while US rollout begins August 7. Transitioning from "prompt-based guessing" to "reference-driven creation" — this provides the exact stellar upgrade creators have been waiting for. ✅ All of the Seedance family (2.5, 2.0, and future releases) operates at its lowest-ever tier ✅ Dreamina proves to be not just the perfect platform for Seedance 2.5 — it remains the most affordable way to run it, full-capacity, skipping any add-ons What makes it so remarkable? First video model to accept 50 multimodal references — drag in characters, scenes, reference shots, and tunes all in one go. Eliminate the nightmare of endless prompt writing. 🎬 30-second native clips — unfold complete stories, instead of mere fragments. You get 2 extensions ready in post-editing, plus a robust ultra-long mode extending up to 3 minutes. ✂️ Professional editing— powerful 3D white model preview, integrated green screen workflow, alongside Blender/Maya plugin support. 🌏 10+ languages — making sure lip-sync and subtitles align perfectly. It means zero translation tools needed. Dreamina Seedance 2.5 goes live globally first on Dreamina — undoubtedly the 2026 premier AI video model for creators. Coming soon. Create alongside Seedance 2.0 today and master the ultimate workflow! #dreamina #dreaminapartner #seedance #dreaminaseedance25

ETHERSON

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THE DEPTH MAP TRICK THAT FIXED DANCE ACCURACY IN SEEDANCE 2.0 Feed the model a video of someone dancing and it tries to interpret everything- the person, the clothes, the lighting, the room, and somewhere in there, the movement. Feed it a depth map and there's nothing left to interpret but the motion. Most creators trying to transfer a dance to a character reference the source footage directly, then wonder why the choreography drifts. The problem isn't the model - it's that you handed it ten variables when you only wanted one. Here's the workflow 1. Lock the character reference in GPT Image 2 first -face, build, costume, so identity holds independently of whatever motion gets applied to it 2. Convert the source dance footage into a depth map instead of using the raw video -this strips out the original performer's appearance, clothing, and environment entirely 3. Feed the depth map as the motion reference and the character sheet as the identity reference- two separate inputs doing two separate jobs, not one input trying to do both 5. Let the depth map carry only spatial movement -the model receives body position and momentum with no competing information about who's moving or what they look like 6. Keep the character and motion inputs isolated throughout - the moment you mix appearance data into the motion reference, the model starts negotiating between two identities Why this works • Raw footage passes the model everything at once- performer, wardrobe, room, lighting -and the choreography competes with all of it for attention • A depth map is pure spatial information, so the only thing left to transfer is movement • Separating identity from motion means the character can stay locked while the dance stays accurate - normally you're trading one for the other • The accuracy gain isn't the model getting better, it's the model getting fewer decisions to make Use cases: ⁃ Dance and choreography transfer onto original characters ⁃ Motion capture-style workflows without motion capture ⁃ Any sequence where a specific movement needs to survive intact ⁃ Character showcase content built on existing performance footage The character sheet answers who's dancing. The depth map answers how - and keeping those two questions separate is the whole trick.

THE DEPTH MAP TRICK THAT FIXED DANCE ACCURACY IN SEEDANCE 2.0 Feed the model a video of someone dancing and it tries to interpret everything- the person, the clothes, the lighting, the room, and somewhere in there, the movement. Feed it a depth map and there's nothing left to interpret but the motion. Most creators trying to transfer a dance to a character reference the source footage directly, then wonder why the choreography drifts. The problem isn't the model - it's that you handed it ten variables when you only wanted one. Here's the workflow 1. Lock the character reference in GPT Image 2 first -face, build, costume, so identity holds independently of whatever motion gets applied to it 2. Convert the source dance footage into a depth map instead of using the raw video -this strips out the original performer's appearance, clothing, and environment entirely 3. Feed the depth map as the motion reference and the character sheet as the identity reference- two separate inputs doing two separate jobs, not one input trying to do both 5. Let the depth map carry only spatial movement -the model receives body position and momentum with no competing information about who's moving or what they look like 6. Keep the character and motion inputs isolated throughout - the moment you mix appearance data into the motion reference, the model starts negotiating between two identities Why this works • Raw footage passes the model everything at once- performer, wardrobe, room, lighting -and the choreography competes with all of it for attention • A depth map is pure spatial information, so the only thing left to transfer is movement • Separating identity from motion means the character can stay locked while the dance stays accurate - normally you're trading one for the other • The accuracy gain isn't the model getting better, it's the model getting fewer decisions to make Use cases: ⁃ Dance and choreography transfer onto original characters ⁃ Motion capture-style workflows without motion capture ⁃ Any sequence where a specific movement needs to survive intact ⁃ Character showcase content built on existing performance footage The character sheet answers who's dancing. The depth map answers how - and keeping those two questions separate is the whole trick.

Nexlow

84,925 Aufrufe • vor 25 Tagen

Created with Gpt Image 2 + Seedance 2.0 on SJinn Agent Prompt: Create a high-quality 4K cinematic football (soccer) storyboard showing a full sequence from tackle to goal, using dynamic lighting, dramatic stadium atmosphere, and professional sports broadcast style. The scene begins with an intense midfield moment where a defender performs a strong sliding tackle, grass and dirt particles flying in slow motion as the ball is cleanly won. Transition immediately into a fast counterattack: the player recovers, pushes the ball forward, and accelerates past opponents with quick footwork. Show close-ups of boots striking the ball, sweat, and focused expressions, mixed with wide shots of the field opening up. Add a key pass moment where the ball is threaded through defenders, followed by a sprint toward goal with the crowd rising in anticipation. Build tension with a slow-motion approach to the penalty box, then a powerful shot on goal—either a clean strike or a curled finish. Show the ball hitting the net with dramatic impact, net rippling, and goalkeeper reaction. End with an emotional celebration: the scorer running, sliding, or raising arms as teammates join, stadium lights glowing and crowd erupting. Use varied camera angles (wide, tracking, close-up, POV, slow motion), strong motion blur, and realistic physics (ball spin, turf impact), emphasizing the shift from defensive intensity to attacking triumph and victory.

Created with Gpt Image 2 + Seedance 2.0 on SJinn Agent Prompt: Create a high-quality 4K cinematic football (soccer) storyboard showing a full sequence from tackle to goal, using dynamic lighting, dramatic stadium atmosphere, and professional sports broadcast style. The scene begins with an intense midfield moment where a defender performs a strong sliding tackle, grass and dirt particles flying in slow motion as the ball is cleanly won. Transition immediately into a fast counterattack: the player recovers, pushes the ball forward, and accelerates past opponents with quick footwork. Show close-ups of boots striking the ball, sweat, and focused expressions, mixed with wide shots of the field opening up. Add a key pass moment where the ball is threaded through defenders, followed by a sprint toward goal with the crowd rising in anticipation. Build tension with a slow-motion approach to the penalty box, then a powerful shot on goal—either a clean strike or a curled finish. Show the ball hitting the net with dramatic impact, net rippling, and goalkeeper reaction. End with an emotional celebration: the scorer running, sliding, or raising arms as teammates join, stadium lights glowing and crowd erupting. Use varied camera angles (wide, tracking, close-up, POV, slow motion), strong motion blur, and realistic physics (ball spin, turf impact), emphasizing the shift from defensive intensity to attacking triumph and victory.

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