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Depth video workflows have been getting a lot of attention lately, so I tested one myself. Combined with Seedance 2.0, it produced more natural motion-transfer results than using Kling Motion Control directly. Why use a depth video? 1. It removes the original character and scene details, reducing copyright and...

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underwood

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15,968 просмотров • 11 дней назад •via X (Twitter)

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🤯 Found another great use case for Depth today! I tried recreating this fashion transformation video with my own character, but the original character kept showing up. 🌟 So I switched the reference video to Depth, and it worked way better! Same motion and timing, but now I can use my own character and completely new outfits! Quick workflow in the comments Full Depth workflow in the quoted post 👇
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🤯 Found another great use case for Depth today! I tried recreating this fashion transformation video with my own character, but the original character kept showing up. 🌟 So I switched the reference video to Depth, and it worked way better! Same motion and timing, but now I can use my own character and completely new outfits! Quick workflow in the comments Full Depth workflow in the quoted post 👇

Larus Canus

114,295 просмотров • 23 дней назад

🤯 Depth videos perfectly solve false flags on reference videos — and can perfectly recreate both martial arts and dance! Turned a Guan Dao martial arts clip into a Depth motion reference, swapped the fighter for a market auntie, and moved the scene to a T-junction inside a local market 🤣 🌟 Workflow: 1. Pick a reference clip under 15 seconds 2. Convert it to Depth with Depth Anything V2 3. Generate a new character + scene 4. Feed everything into Seedance with the prompt below 🌟 Depth video conversion: You can build a local Depth converter with Codex — prompt in the comments. This goes way beyond trending dances. Martial arts, polearms, and other complex movements can all be transferred cleanly with Depth! Workflow + Prompt below 👇
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🤯 Depth videos perfectly solve false flags on reference videos — and can perfectly recreate both martial arts and dance! Turned a Guan Dao martial arts clip into a Depth motion reference, swapped the fighter for a market auntie, and moved the scene to a T-junction inside a local market 🤣 🌟 Workflow: 1. Pick a reference clip under 15 seconds 2. Convert it to Depth with Depth Anything V2 3. Generate a new character + scene 4. Feed everything into Seedance with the prompt below 🌟 Depth video conversion: You can build a local Depth converter with Codex — prompt in the comments. This goes way beyond trending dances. Martial arts, polearms, and other complex movements can all be transferred cleanly with Depth! Workflow + Prompt below 👇

Larus Canus

50,001 просмотров • 24 дней назад

New fal workflow: video restyling with Video Depth Anything and Seedance 2.0 - Restyle the first frame into your target look - Extract consistent depth across the source video - Use that depth to guide Seedance 2.0 through the full sequence - Preserve the original motion and composition while transforming the visual style A simple workflow for turning existing footage into stylized video.
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New fal workflow: video restyling with Video Depth Anything and Seedance 2.0 - Restyle the first frame into your target look - Extract consistent depth across the source video - Use that depth to guide Seedance 2.0 through the full sequence - Preserve the original motion and composition while transforming the visual style A simple workflow for turning existing footage into stylized video.

fal

10,909 просмотров • 26 дней назад

🤯 I didn’t expect Depth to track rooftop parkour this cleanly! Used a fast climbing clip as the motion reference, then swapped in a student with twin tails and a backpack on a coastal school rooftop. The running path, wall contact, jumps, landings, and full-body continuity all hold together surprisingly well. 🌟 Workflow: 1. Pick a reference clip under 15 seconds 2. Convert it to Depth with Depth Anything V2 3. Generate a new character + scene 4. Feed everything into Seedance with the prompt below 🌟 Depth video conversion: You can build a local Depth converter with Codex — prompt in the comments. Depth opens up a lot more possibilities for parkour, climbing, martial arts, dance, and other complex full-body motion. Workflow + Prompt below 👇
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🤯 I didn’t expect Depth to track rooftop parkour this cleanly! Used a fast climbing clip as the motion reference, then swapped in a student with twin tails and a backpack on a coastal school rooftop. The running path, wall contact, jumps, landings, and full-body continuity all hold together surprisingly well. 🌟 Workflow: 1. Pick a reference clip under 15 seconds 2. Convert it to Depth with Depth Anything V2 3. Generate a new character + scene 4. Feed everything into Seedance with the prompt below 🌟 Depth video conversion: You can build a local Depth converter with Codex — prompt in the comments. Depth opens up a lot more possibilities for parkour, climbing, martial arts, dance, and other complex full-body motion. Workflow + Prompt below 👇

Larus Canus

24,613 просмотров • 20 дней назад

Gemini Omni's motion control is f*cking cracked i just figured out how to turn 1 reference video into 50+ AI videos with the exact same movements... you have a video of someone eating, dancing, using a product, doing whatever complex motion you need. you feed it to Gemini Omni and it recreates that exact motion with a completely new AI character in literally one prompt i've tested this against Kling motion control and it's not even close. Kling falls apart the moment you try anything complex. eating scenes look weird, hand movements get mangled, anything multi-step breaks down completely. Gemini Omni handles all of it if you're still using kling motion control or paying creators to split test your videos, this replaces that entire workflow here's the thing though. you can't just prompt this out of the box. if you try to do motion transfer with default prompting you're going to get errors or the motion won't transfer properly. there's a specific prompting method that makes it work every time so i packaged up the whole system.. here's what you're getting: > full step by step video breakdown > how to find the best reference videos to use > the exact prompting system that allows for motion control transfer so you never get errors > the workflow for batching this out at scale (1 video → 50+) RT + reply "MOTION" and i'll send it over (must follow so i can dm)
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Gemini Omni's motion control is f*cking cracked i just figured out how to turn 1 reference video into 50+ AI videos with the exact same movements... you have a video of someone eating, dancing, using a product, doing whatever complex motion you need. you feed it to Gemini Omni and it recreates that exact motion with a completely new AI character in literally one prompt i've tested this against Kling motion control and it's not even close. Kling falls apart the moment you try anything complex. eating scenes look weird, hand movements get mangled, anything multi-step breaks down completely. Gemini Omni handles all of it if you're still using kling motion control or paying creators to split test your videos, this replaces that entire workflow here's the thing though. you can't just prompt this out of the box. if you try to do motion transfer with default prompting you're going to get errors or the motion won't transfer properly. there's a specific prompting method that makes it work every time so i packaged up the whole system.. here's what you're getting: > full step by step video breakdown > how to find the best reference videos to use > the exact prompting system that allows for motion control transfer so you never get errors > the workflow for batching this out at scale (1 video → 50+) RT + reply "MOTION" and i'll send it over (must follow so i can dm)

Miko

59,125 просмотров • 1 месяц назад

🔥 AI video creation is finally evolving beyond basic motion prompts. Try out Depth Map Control right here: Historically, the hardest part of the process has been maintaining scene consistency: • camera movement • object placement • spatial relationships Thanks to PixVerse Depth Map Control, you can now grab depth data from a reference video and merge it with a fresh image to explore highly directed video generation. The final outcome? Much smoother camera tracking. Highly cohesive visual scenes. It is a powerful workflow that hands creators actual authority over how their AI visuals shift and flow over time. 🎬
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🔥 AI video creation is finally evolving beyond basic motion prompts. Try out Depth Map Control right here: Historically, the hardest part of the process has been maintaining scene consistency: • camera movement • object placement • spatial relationships Thanks to PixVerse Depth Map Control, you can now grab depth data from a reference video and merge it with a fresh image to explore highly directed video generation. The final outcome? Much smoother camera tracking. Highly cohesive visual scenes. It is a powerful workflow that hands creators actual authority over how their AI visuals shift and flow over time. 🎬

Elara Quinn

12,202 просмотров • 12 дней назад

Kling 2.6 Motion Capture is so good. It's a huge leap forward. We've been able to do motion capture with AI for a while, but the quality bump finally made this approach useful. Here are the steps: - Get a reference video - record it or get a stock video - Grab the start frame of that video - Edit it using Nano Banana Pro. Ask to replace character and background. - Select the Kling 2.6 Motion Capture model in the video generator on Freepik (now Magnific) - Upload reference video + set the edited start frame - Video prompt can be simple e.g. "Gandalf dancing"
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Kling 2.6 Motion Capture is so good. It's a huge leap forward. We've been able to do motion capture with AI for a while, but the quality bump finally made this approach useful. Here are the steps: - Get a reference video - record it or get a stock video - Grab the start frame of that video - Edit it using Nano Banana Pro. Ask to replace character and background. - Select the Kling 2.6 Motion Capture model in the video generator on Freepik (now Magnific) - Upload reference video + set the edited start frame - Video prompt can be simple e.g. "Gandalf dancing"

Martin LeBlanc

35,262 просмотров • 7 месяцев назад

One trick we discovered for avoiding realistic face moderation issues in Seedance 2.0 is using character turnaround sheets (front / side / back views). The first video is one of our experiment results — and it runs successfully. We’ve now integrated character turnarounds directly into our workflow + canvas system: 1. If your artwork was generated on our site, you can drag the image into the canvas directly from the Assets tab 2. Click the “Character Turnaround” button above the image to automatically generate a 3-view turnaround sheet 3. Create a new video node and use the turnaround sheet directly with Seedance 2.0 inside the workflow I’ve shared the workflow link in the comments if you want to explore the exact prompts, setup, and workflow details.
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One trick we discovered for avoiding realistic face moderation issues in Seedance 2.0 is using character turnaround sheets (front / side / back views). The first video is one of our experiment results — and it runs successfully. We’ve now integrated character turnarounds directly into our workflow + canvas system: 1. If your artwork was generated on our site, you can drag the image into the canvas directly from the Assets tab 2. Click the “Character Turnaround” button above the image to automatically generate a 3-view turnaround sheet 3. Create a new video node and use the turnaround sheet directly with Seedance 2.0 inside the workflow I’ve shared the workflow link in the comments if you want to explore the exact prompts, setup, and workflow details.

underwood

32,883 просмотров • 2 месяцев назад

Combining ChatGPT Images 2.0 and Seedance 2.0 together. Character sheets are just perfect for using as a reference for your videos. Can really help with consistency. From this character sheet here, I am able to reimagine the same character in different situations. 1. Runner
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Combining ChatGPT Images 2.0 and Seedance 2.0 together. Character sheets are just perfect for using as a reference for your videos. Can really help with consistency. From this character sheet here, I am able to reimagine the same character in different situations. 1. Runner

Jerrod Lew

70,651 просмотров • 3 месяцев назад

I handed a dance video over to AI... Try this exact workflow yourself: I used PixVerse Depth Map Control to seamlessly map the original choreography onto a completely fresh character. The visual outcome is incredibly fascinating 👀 Instead of generating a scene from absolute zero, this specific setup helps you lock down crucial details from your base clip: • movement • body position • spatial structure Your source footage dictates the precise action, while a single reference picture establishes the new aesthetic. It is a brilliant way to level up your AI video experiments — granting you far more precision over how your animations move and shift.
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I handed a dance video over to AI... Try this exact workflow yourself: I used PixVerse Depth Map Control to seamlessly map the original choreography onto a completely fresh character. The visual outcome is incredibly fascinating 👀 Instead of generating a scene from absolute zero, this specific setup helps you lock down crucial details from your base clip: • movement • body position • spatial structure Your source footage dictates the precise action, while a single reference picture establishes the new aesthetic. It is a brilliant way to level up your AI video experiments — granting you far more precision over how your animations move and shift.

LX™

59,128 просмотров • 13 дней назад

I became a Midjourney Niji 7 character. Went to MJ, made a character, used it as a reference in Higgsfield Cinema Studio, animated it, and then I used Motion Control to insert myself into the footage. Absolutely crazy for 5 minutes of work!
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I became a Midjourney Niji 7 character. Went to MJ, made a character, used it as a reference in Higgsfield Cinema Studio, animated it, and then I used Motion Control to insert myself into the footage. Absolutely crazy for 5 minutes of work!

Alex Patrascu

46,567 просмотров • 6 месяцев назад

Kling 2.6 Motion Control is absolutely insane 🤯 Take any reference video and transfer the exact motion onto an AI character: full-body sync, facial expressions, hand gestures, everything. All with just a few clicks. Perfect for e-comm brands and agencies creating AI video ads that don't look like AI. Here's the problem: AI-generated video ads still look robotic. The movements are stiff, the expressions are flat. Your audience clocks it as AI instantly and keeps scrolling. Kling 2.6 Motion Control fixes it: → Start with any reference clip (stock footage, existing UGC, motion reference) → Upload to Kling → Map the exact movement onto any AI character → Full-body motion, hand gestures, facial expressions—all transferred → Generate up to 30 seconds of video No stiff AI movements, no uncanny valley, no instant "skip this ad" reaction. What this unlocks: - Use one winning UGC motion → swap in different AI creators - Pull reference clips from anywhere → generate branded variations - Create dynamic AI video ads with real human movement - Test multiple "creators" without filming anyone new I recorded a quick walkthrough showing how to do this step-by-step. Want access? > Comment "KLING" > Like this post And I'll send it over (must be following so I can DM)
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Kling 2.6 Motion Control is absolutely insane 🤯 Take any reference video and transfer the exact motion onto an AI character: full-body sync, facial expressions, hand gestures, everything. All with just a few clicks. Perfect for e-comm brands and agencies creating AI video ads that don't look like AI. Here's the problem: AI-generated video ads still look robotic. The movements are stiff, the expressions are flat. Your audience clocks it as AI instantly and keeps scrolling. Kling 2.6 Motion Control fixes it: → Start with any reference clip (stock footage, existing UGC, motion reference) → Upload to Kling → Map the exact movement onto any AI character → Full-body motion, hand gestures, facial expressions—all transferred → Generate up to 30 seconds of video No stiff AI movements, no uncanny valley, no instant "skip this ad" reaction. What this unlocks: - Use one winning UGC motion → swap in different AI creators - Pull reference clips from anywhere → generate branded variations - Create dynamic AI video ads with real human movement - Test multiple "creators" without filming anyone new I recorded a quick walkthrough showing how to do this step-by-step. Want access? > Comment "KLING" > Like this post And I'll send it over (must be following so I can DM)

Mike Futia

25,179 просмотров • 6 месяцев назад

Turn a simple idea into a storyboard, then generate the video with Seedance 2.0. This ComfyUI workflow uses LLMs to structure prompts into storyboard-ready scenes that define how the video should play out. The storyboard can be paired with reference images and sent directly into Seedance for video generation. To try this workflow, link below 👇
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Turn a simple idea into a storyboard, then generate the video with Seedance 2.0. This ComfyUI workflow uses LLMs to structure prompts into storyboard-ready scenes that define how the video should play out. The storyboard can be paired with reference images and sent directly into Seedance for video generation. To try this workflow, link below 👇

ComfyUI

33,901 просмотров • 2 месяцев назад

MIMO: Advanced Video-to-Video Model by Alibaba This is insane! With this v2v model, we can take a video of ourselves and use it to animate any character we create, all while keeping the original background from the video. It's like we're putting ourselves into any other new worlds! 6 Example:
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MIMO: Advanced Video-to-Video Model by Alibaba This is insane! With this v2v model, we can take a video of ourselves and use it to animate any character we create, all while keeping the original background from the video. It's like we're putting ourselves into any other new worlds! 6 Example:

el.cine

118,771 просмотров • 1 год назад

For the facial animations in my game, I use a technique that yields results similar to L.A. Noire. To keep it simple: I first generate a facial animation video using AI like LTX 2. Then, I create a video depth map from that animation and project it onto a face mask. Using vertex displacement, the depth map dynamically deforms the face as the character speaks. The result looks good in standalone VR. I actually created this workflow long time ago:
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For the facial animations in my game, I use a technique that yields results similar to L.A. Noire. To keep it simple: I first generate a facial animation video using AI like LTX 2. Then, I create a video depth map from that animation and project it onto a face mask. Using vertex displacement, the depth map dynamically deforms the face as the character speaks. The result looks good in standalone VR. I actually created this workflow long time ago:

Alex

48,296 просмотров • 5 месяцев назад

📖THE STEP MOST CREATORS SKIP IS WHY THEIR AI ANIMATION LOOKS INCONSISTENT Consistency across clips doesn't come from prompting — it comes from the reference image. The pipeline, step by step: ▪ Start with ChatGPT Image 2 — generate a full character design sheet first, not just a single frame. Multiple angles, expressions, and outfit variations in one image keeps the character consistent across every scene ▪ Build a storyboard inside ChatGPT Image 2 as well — define each shot, camera angle, action, and mood before touching Seedance at all. This is the step most people skip and it's the reason clips look disconnected ▪ Define a color palette and lighting mood early — golden afternoon light, soft warm tones, dramatic shadows. Lock those values and repeat them across every prompt ▪ Take each storyboard frame into Seedance 2.0 as the reference image — one frame becomes one clip ▪ Write the Seedance prompt around the character action, not the scene description. The scene is already in the image. The prompt handles motion, camera behavior, and timing ▪ Keep clip duration between 4-6 seconds per shot — shorter clips give more control over pacing and reduce motion drift on character faces ▪ Match camera movement type across consecutive clips — if one shot dollies in, the next should hold or pull back, not dolly again The consistency across these frames comes from the character design sheet, not from luck. Seedance reads the reference image and the prompt together — if the reference is detailed enough, the output stays on-model. This video was created by ALOKXMEHTA 📥 tomorrow: the exact ChatGPT Image 2 prompt structure used to generate a multi-angle character design sheet like this one 🔖One article covers the entire workflow — it is pinned below, do not scroll past it.
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📖THE STEP MOST CREATORS SKIP IS WHY THEIR AI ANIMATION LOOKS INCONSISTENT Consistency across clips doesn't come from prompting — it comes from the reference image. The pipeline, step by step: ▪ Start with ChatGPT Image 2 — generate a full character design sheet first, not just a single frame. Multiple angles, expressions, and outfit variations in one image keeps the character consistent across every scene ▪ Build a storyboard inside ChatGPT Image 2 as well — define each shot, camera angle, action, and mood before touching Seedance at all. This is the step most people skip and it's the reason clips look disconnected ▪ Define a color palette and lighting mood early — golden afternoon light, soft warm tones, dramatic shadows. Lock those values and repeat them across every prompt ▪ Take each storyboard frame into Seedance 2.0 as the reference image — one frame becomes one clip ▪ Write the Seedance prompt around the character action, not the scene description. The scene is already in the image. The prompt handles motion, camera behavior, and timing ▪ Keep clip duration between 4-6 seconds per shot — shorter clips give more control over pacing and reduce motion drift on character faces ▪ Match camera movement type across consecutive clips — if one shot dollies in, the next should hold or pull back, not dolly again The consistency across these frames comes from the character design sheet, not from luck. Seedance reads the reference image and the prompt together — if the reference is detailed enough, the output stays on-model. This video was created by ALOKXMEHTA 📥 tomorrow: the exact ChatGPT Image 2 prompt structure used to generate a multi-angle character design sheet like this one 🔖One article covers the entire workflow — it is pinned below, do not scroll past it.

Zentrix⌚️

14,015 просмотров • 1 месяц назад

Create a short film like this in just 1 minute with GPT Image 2.0 + Seedance 2.0. GPT Image 2.0 can naturally combine multiple photos into one single image, while Seedance 2.0 can use that image as a reference to automatically separate the scenes, generate a coherent video sequence, and add suitable background music. This workflow greatly improves the overall creative efficiency. When using this method, simply provide the merged image as a reference for Seedance 2.0 and briefly describe each scene with a simple prompt. This can significantly increase the success rate of the final video. All of the above was created on GPT Image Prompt: Seedance Prompt:
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Create a short film like this in just 1 minute with GPT Image 2.0 + Seedance 2.0. GPT Image 2.0 can naturally combine multiple photos into one single image, while Seedance 2.0 can use that image as a reference to automatically separate the scenes, generate a coherent video sequence, and add suitable background music. This workflow greatly improves the overall creative efficiency. When using this method, simply provide the merged image as a reference for Seedance 2.0 and briefly describe each scene with a simple prompt. This can significantly increase the success rate of the final video. All of the above was created on GPT Image Prompt: Seedance Prompt:

Midjourney Sref and prompt Library

40,572 просмотров • 3 месяцев назад

🇨🇳 Another great Chinese Model, OmniHuman-1.5 from ByteDance Turns 1 image plus a voice track into expressive avatar video by pairing a System 1 and System 2 inspired planner with a Diffusion Transformer, Produces coherent motion for over 1 minute with moving camera and multi character scenes. Most avatar models move to the beat of the audio but miss meaning, so gestures feel generic and emotions feel shallow. The fix here is a Multimodal LLM planner that listens to the speech and drafts a structured plan describing intent, emotions, beats, and high level actions, which gives the motion engine clear semantic targets instead of only rhythm. The motion engine is a Multimodal Diffusion Transformer that fuses the plan with audio, the single reference image, and optional text prompts, then synthesizes continuous body, face, and head motion that matches both words and tone. A key trick is a Pseudo Last Frame, a synthetic target that summarizes the next expected state, which stabilizes fusion across modalities and keeps motion consistent over long spans. From just 1 image and speech, the system outputs speaking avatars with synchronized lips, context aware gestures, and continuous camera movement, and it also supports multi character interactions without manual choreography. Reported results show strong lip sync accuracy, high video quality, natural motion, and close match to text prompts, and the same setup works on nonhuman characters too.
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🇨🇳 Another great Chinese Model, OmniHuman-1.5 from ByteDance Turns 1 image plus a voice track into expressive avatar video by pairing a System 1 and System 2 inspired planner with a Diffusion Transformer, Produces coherent motion for over 1 minute with moving camera and multi character scenes. Most avatar models move to the beat of the audio but miss meaning, so gestures feel generic and emotions feel shallow. The fix here is a Multimodal LLM planner that listens to the speech and drafts a structured plan describing intent, emotions, beats, and high level actions, which gives the motion engine clear semantic targets instead of only rhythm. The motion engine is a Multimodal Diffusion Transformer that fuses the plan with audio, the single reference image, and optional text prompts, then synthesizes continuous body, face, and head motion that matches both words and tone. A key trick is a Pseudo Last Frame, a synthetic target that summarizes the next expected state, which stabilizes fusion across modalities and keeps motion consistent over long spans. From just 1 image and speech, the system outputs speaking avatars with synchronized lips, context aware gestures, and continuous camera movement, and it also supports multi character interactions without manual choreography. Reported results show strong lip sync accuracy, high video quality, natural motion, and close match to text prompts, and the same setup works on nonhuman characters too.

Rohan Paul

63,859 просмотров • 11 месяцев назад

this effect is all over tiktok right now and nobody's explaining how to actually do it properly... the 3d balloon character thing. where someone turns into a shiny inflatable version of themselves that still moves and talks. looks pretty smooth in feeds. the workflow is stupid simple once you see it. step 1: take any photo. drop it into an image gen tool (nano banana pro). prompt it with something like "make the person in the photo a plastic blow up balloon character with a shiny surface. keep the face details as 3d balloon details including the person in the background. don't change background" that's it for the image. don't overcomplicate the prompt. shorter = more consistent results. (learned this after wasting like 2 hours trying to get "perfect" prompts that kept giving me garbage) step 2: take that balloon image + your original video and drop both into kling motion control. prompt: "turn the motion and detailed mouth movement of the video to the setting of the image" that's literally it. kling maps the motion from the real video onto the balloon character. mouth moves. head turns. expressions transfer. the whole thing renders in a few minutes. the result looks like a $500 custom animation and costs you maybe $0.30 in kling credits. people are getting 500k+ views with these because the scroll-stop factor is insane. nobody expects to see a shiny inflatable version of someone giving a real speech or doing a product review. the play here is obvious btw. run this for client content (mix with the hook and real body, check the results yourself) or use it on your own faceless channels as a hook pattern before the algo catches up...
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this effect is all over tiktok right now and nobody's explaining how to actually do it properly... the 3d balloon character thing. where someone turns into a shiny inflatable version of themselves that still moves and talks. looks pretty smooth in feeds. the workflow is stupid simple once you see it. step 1: take any photo. drop it into an image gen tool (nano banana pro). prompt it with something like "make the person in the photo a plastic blow up balloon character with a shiny surface. keep the face details as 3d balloon details including the person in the background. don't change background" that's it for the image. don't overcomplicate the prompt. shorter = more consistent results. (learned this after wasting like 2 hours trying to get "perfect" prompts that kept giving me garbage) step 2: take that balloon image + your original video and drop both into kling motion control. prompt: "turn the motion and detailed mouth movement of the video to the setting of the image" that's literally it. kling maps the motion from the real video onto the balloon character. mouth moves. head turns. expressions transfer. the whole thing renders in a few minutes. the result looks like a $500 custom animation and costs you maybe $0.30 in kling credits. people are getting 500k+ views with these because the scroll-stop factor is insane. nobody expects to see a shiny inflatable version of someone giving a real speech or doing a product review. the play here is obvious btw. run this for client content (mix with the hook and real body, check the results yourself) or use it on your own faceless channels as a hook pattern before the algo catches up...

KNOX

25,773 просмотров • 6 месяцев назад

I didn’t expect Grok’s video model to improve this much. Today, when I was generating a video with Seedance 2.0, I found the results weren’t very good. So I tried using the exact same source material and prompt in Grok instead. To my surprise, Grok actually produced better results than Seedance 2.0. I’ve put the prompt and source material in the comments, so if you’re interested, you can give it a try too. The left side shows the video generated by Grok, and the right side shows the video generated by Seedance 2.0.
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I didn’t expect Grok’s video model to improve this much. Today, when I was generating a video with Seedance 2.0, I found the results weren’t very good. So I tried using the exact same source material and prompt in Grok instead. To my surprise, Grok actually produced better results than Seedance 2.0. I’ve put the prompt and source material in the comments, so if you’re interested, you can give it a try too. The left side shows the video generated by Grok, and the right side shows the video generated by Seedance 2.0.

underwood

34,518 просмотров • 4 месяцев назад