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Day 2: Kling IMAGE O1 is Officially Here! Input anything. Understand everything. Generate any vision. Superb Consistency, Precise Modification, Powerful Stylization, Max Creativity — IMAGE O1 brings it all! This update revamps the entire process from generation to editing, empowering maximum productivity with a seamless experience! 1 year of...

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Kling AI

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154,154 views • 7 months ago •via X (Twitter)

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We’re excited to announce the release and open-source of HunyuanImage 3.0 — the largest and most powerful open-source text-to-image model to date, with over 80 billion total parameters, of which 13 billion are activated per token during inference.The effect is completely comparable to the industry’s flagship closed-source model.🚀🚀🚀 HunyuanImage 3.0 originates from our internally developed native multimodal large language model, with fine-tuning and post-training focused on text-to-image generation. This unique foundation gives the model a powerful set of capabilities: ✅Reason with world knowledge ✅Understand complex, thousand-word prompts ✅Generate precise text within images Different from traditional DiT architecture image generation models, HunyuanImage 3.0’s MoE architecture uses a Transfusion-based approach to deeply couple Diffusion and LLM training for a single, powerful system. Built on Hunyuan-A13B, HunyuanImage 3.0 was trained on a massive dataset: 5 billion image-text pairs, video frames, interleaved image-text data, and 6 trillion tokens of text corpora. This hybrid training across multimodal generation, understanding, and LLM capabilities allows the model to seamlessly integrate multiple tasks. Whether you're an illustrator, designer, or creator, this is built to slash your workflow from hours to minutes. HunyuanImage 3.0 can generate intricate text, detailed comics, expressive emojis, and lively, engaging illustrations for educational content. The current release focuses solely on text-to-image generation and future updates will include image-to-image, image editing, multi-turn interaction, and more. 👉🏻Try it now: 🔗GitHub: 🤗Hugging Face:

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I finished a 2 minute 30 second short film using only AI tools inside invideo. I just structured storytelling, precise prompting, and relentless iteration and documented the entire process step by step. I used... - Seedance 1.5 Pro for video generation (Best overall) - Kling 2.6 and Kling 3 for advanced motion scenes - Nano Banana Pro for scene generation and editing - ChatGPT for crafting highly controlled cinematic prompts - Image-to-video methodology to maintain continuity If you’re serious about AI creative video creation, this might change how you approach it. Like, REPOST, and comment "Guide" and I'll DM you the complete step by step guide how to generated it. Watch the complete video here... . .
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I finished a 2 minute 30 second short film using only AI tools inside invideo. I just structured storytelling, precise prompting, and relentless iteration and documented the entire process step by step. I used... - Seedance 1.5 Pro for video generation (Best overall) - Kling 2.6 and Kling 3 for advanced motion scenes - Nano Banana Pro for scene generation and editing - ChatGPT for crafting highly controlled cinematic prompts - Image-to-video methodology to maintain continuity If you’re serious about AI creative video creation, this might change how you approach it. Like, REPOST, and comment "Guide" and I'll DM you the complete step by step guide how to generated it. Watch the complete video here... . .

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Hey everyone! To make my pieces more findable, I’ve made a 2024 catalog of all my 1/1 pieces that have not been collected yet. I’m hoping this will be an inviting way for people to check out what I have available. Each image is titled and directly linked for anyone curious about the details of the photo/would like to purchase the piece. If you place any bids, you can choose a 1/1 from this catalogue per bid (just shoot me a dm)- bid winners/buy it now are also eligible! Please feel free to contact me with any questions. To see the catalog, follow the 🔗⬇️! Happy Bidding/Winning!
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Hey everyone! To make my pieces more findable, I’ve made a 2024 catalog of all my 1/1 pieces that have not been collected yet. I’m hoping this will be an inviting way for people to check out what I have available. Each image is titled and directly linked for anyone curious about the details of the photo/would like to purchase the piece. If you place any bids, you can choose a 1/1 from this catalogue per bid (just shoot me a dm)- bid winners/buy it now are also eligible! Please feel free to contact me with any questions. To see the catalog, follow the 🔗⬇️! Happy Bidding/Winning!

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BARRY 🍯 - Meet Barry, a bear hopelessly addicted to store bought honey. Once content with wild beehives, his taste buds now crave the processed sweetness from supermarket shelves. To get his fix, he repeatedly robs convenience stores. Arrested time and again, he somehow keeps getting released, only to strike again. Miraculously, the charges never stick. I guess being a bear has its perks. My end goal here was to use Kling 3.0 for the entire cinematic sequence, including lipsyncing and dialogue to see how it holds up. The video model has improved a great deal and this was a lot of fun to put together. Enjoy! I love telling all sorts of stories, even silly ones like this. P.S I'm starring in my own short film, I'm the one who is in prison with the bear. Tools For Creation: Image Generation: Nano Banana Pro Image To Video: Kling AI 3.0 Lipsync And Dialogue: Kling 3.0 Music: Elevenlabs & Suno Sound Effects: Artlist Video Editing: Capcut
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BARRY 🍯 - Meet Barry, a bear hopelessly addicted to store bought honey. Once content with wild beehives, his taste buds now crave the processed sweetness from supermarket shelves. To get his fix, he repeatedly robs convenience stores. Arrested time and again, he somehow keeps getting released, only to strike again. Miraculously, the charges never stick. I guess being a bear has its perks. My end goal here was to use Kling 3.0 for the entire cinematic sequence, including lipsyncing and dialogue to see how it holds up. The video model has improved a great deal and this was a lot of fun to put together. Enjoy! I love telling all sorts of stories, even silly ones like this. P.S I'm starring in my own short film, I'm the one who is in prison with the bear. Tools For Creation: Image Generation: Nano Banana Pro Image To Video: Kling AI 3.0 Lipsync And Dialogue: Kling 3.0 Music: Elevenlabs & Suno Sound Effects: Artlist Video Editing: Capcut

Travis Davids

11,125 views • 5 months ago

Cinematic city transitions. Perfect cuts. Zero traditional video editing. Everything you’re watching is AI. Tools: - Kling 2.5 Turbo - Nano Banana Pro - ChatGPT 4 STEP TUTORIAL: 1️⃣: Design your concept Brainstorm with ChatGPT the type of “vibe” you want your images/video to capture. I landed on “Cinematic Urban Melancholy” for my overall Visual Theme. 2️⃣: Generate Images with Nano Banana 🍌 Use ChatGPT to generate nano-banana-ready prompts for each of the 5 images in your setting. 3️⃣: Generate AI Transitions Navigate to Kling and select AI Video Generator Upload your start frame (image 1) + end frame (image 2) Prompt for a “seamless cinematic transition between scenes”. You’ll need to iterate on your prompt until the result matches your vision. Be specific about motion, style, and mood. 4️⃣: Polish Import everything into your editor (I use CapCut). Add speed / slowdown. And most importantly, add sound design. Music and SFX sell the visuals. Bookmark this and try it out yourself! If you want the exact prompts I'm using to generate these transitions: Follow, RT, & comment 'PROMPTS' below. I'll dm them to you (must be following so i can message)
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Cinematic city transitions. Perfect cuts. Zero traditional video editing. Everything you’re watching is AI. Tools: - Kling 2.5 Turbo - Nano Banana Pro - ChatGPT 4 STEP TUTORIAL: 1️⃣: Design your concept Brainstorm with ChatGPT the type of “vibe” you want your images/video to capture. I landed on “Cinematic Urban Melancholy” for my overall Visual Theme. 2️⃣: Generate Images with Nano Banana 🍌 Use ChatGPT to generate nano-banana-ready prompts for each of the 5 images in your setting. 3️⃣: Generate AI Transitions Navigate to Kling and select AI Video Generator Upload your start frame (image 1) + end frame (image 2) Prompt for a “seamless cinematic transition between scenes”. You’ll need to iterate on your prompt until the result matches your vision. Be specific about motion, style, and mood. 4️⃣: Polish Import everything into your editor (I use CapCut). Add speed / slowdown. And most importantly, add sound design. Music and SFX sell the visuals. Bookmark this and try it out yourself! If you want the exact prompts I'm using to generate these transitions: Follow, RT, & comment 'PROMPTS' below. I'll dm them to you (must be following so i can message)

David Roberts

230,390 views • 6 months ago

How to Bring Emojis to Life with AI (Yes, you read that right) 1. Choose the Emoji Wisely Prefer detailed emojis 👨‍🎤 The Man Singer emoji is a good example of this. Face, hair, outfit, mic... The more detailed, the better. 2. Prepare Your Original Image We'll be doing multi-enhancements, so the initial image shouldn't be too large. (I'll explain more about this shortly) I started with a 512x512px image. The smaller the image you start with, the more detail-oriented the enhancer tends to be. I usually use Figma for design and animations. However, you can use any other tool like Canva or Photoshop. Or just download it from somewhere. 3. Upload Your Image Go to Magnific's website and simply drag and drop your image there. 4. Setup the Parameters This is the fun part. You can experiment here to find your own settings or follow the method I used. I start with high creativity and low resemblance values. Depending on how far you want to deviate from the original image, you can increase this contrast. First Enhancing Creativity: 6 / Resemblance: -3 All Parameters Optimized for: Standard Creativity: 6 HDR: 0 (not a fan) Resemblance: -3 Engine: Automatic 5. The Prompt 🪄 Since Magnific uses Stable Diffusion's technology, it uses the same syntax for prompts. But you don't need to write very detailed prompts. Just state what you want to see. The trick here is to use prompt weights. You need to specify the features you want to emphasize like this: Bowie, Portrait photo of a male singer with blue hair, (detailed eyes:1.2), (detailed hair:1.2), (detailed fabric:1.1), (detailed mic:1.1), (realistic skin:1.5), ultra-realistic, 8k You can change the weight values between 1.0 - 2.0 6. Hit that Rainbow Button After a short wait, you'll have a 1024x1024px image. If you like the result, download the image for further enhancements. 7. Reenhancing The system supports up to approximately 4400 x 4400 px. This means we can enhance it two more times. 1x → 512x512px 2x → 1024x1024px 4x → 2048x2048px 8x → 4096x4096px Now, upload the image you downloaded again. This time, we bring the values closer to each other. Second Enhancing Creativity: 4 / Resemblance: -2 Third Enhancing Creativity: 2 / Resemblance: -1 The other values can remain the same. 8. Share what you create! That's it. Now you know everything I know. If you have any questions, don't hesitate to ask. And please share your creations with us. Happy Sunday everyone!
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How to Bring Emojis to Life with AI (Yes, you read that right) 1. Choose the Emoji Wisely Prefer detailed emojis 👨‍🎤 The Man Singer emoji is a good example of this. Face, hair, outfit, mic... The more detailed, the better. 2. Prepare Your Original Image We'll be doing multi-enhancements, so the initial image shouldn't be too large. (I'll explain more about this shortly) I started with a 512x512px image. The smaller the image you start with, the more detail-oriented the enhancer tends to be. I usually use Figma for design and animations. However, you can use any other tool like Canva or Photoshop. Or just download it from somewhere. 3. Upload Your Image Go to Magnific's website and simply drag and drop your image there. 4. Setup the Parameters This is the fun part. You can experiment here to find your own settings or follow the method I used. I start with high creativity and low resemblance values. Depending on how far you want to deviate from the original image, you can increase this contrast. First Enhancing Creativity: 6 / Resemblance: -3 All Parameters Optimized for: Standard Creativity: 6 HDR: 0 (not a fan) Resemblance: -3 Engine: Automatic 5. The Prompt 🪄 Since Magnific uses Stable Diffusion's technology, it uses the same syntax for prompts. But you don't need to write very detailed prompts. Just state what you want to see. The trick here is to use prompt weights. You need to specify the features you want to emphasize like this: Bowie, Portrait photo of a male singer with blue hair, (detailed eyes:1.2), (detailed hair:1.2), (detailed fabric:1.1), (detailed mic:1.1), (realistic skin:1.5), ultra-realistic, 8k You can change the weight values between 1.0 - 2.0 6. Hit that Rainbow Button After a short wait, you'll have a 1024x1024px image. If you like the result, download the image for further enhancements. 7. Reenhancing The system supports up to approximately 4400 x 4400 px. This means we can enhance it two more times. 1x → 512x512px 2x → 1024x1024px 4x → 2048x2048px 8x → 4096x4096px Now, upload the image you downloaded again. This time, we bring the values closer to each other. Second Enhancing Creativity: 4 / Resemblance: -2 Third Enhancing Creativity: 2 / Resemblance: -1 The other values can remain the same. 8. Share what you create! That's it. Now you know everything I know. If you have any questions, don't hesitate to ask. And please share your creations with us. Happy Sunday everyone!

Dogan Ural

225,681 views • 2 years ago

[CLIP] by Hand ✍️ The CLIP (Contrastive Language–Image Pre-training) model, a groundbreaking work by OpenAI, redefines the intersection of computer vision and natural language processing. It is the basis of all the multi-modal foundation models we see today. How does CLIP work? Goal: 🟨 Learn a shared embedding space for text and image [1] Given ↳ A mini batch of 3 text-image pairs ↳ OpenAI used 400 million text-image pairs to train its original CLIP model. Process 1st pair: "big table" [2] 🟪 Text → 2 Vectors (3D) ↳ Look up word embedding vectors using word2vec. [3] 🟩 Image → 2 Vectors (4D) ↳ Divide the image into two patches. ↳ Flatten each patch [4] Process other pairs ↳ Repeat [2]-[3] [5] 🟪 Text Encoder & 🟩 Image Encoder ↳ Encode input vectors into feature vectors ↳ Here, both encoders are simple one layer perceptron (linear + ReLU) ↳ In practice, the encoders are usually transformer models. [6] 🟪 🟩 Mean Pooling: 2 → 1 vector ↳ Average 2 feature vectors into a single vector by averaging across the columns ↳ The goal is to have one vector to represent each image or text [7] 🟪 🟩 -> 🟨 Projection ↳ Note that the text and image feature vectors from the encoders have different dimensions (3D vs. 4D). ↳ Use a linear layer to project image and text vectors to a 2D shared embedding space. 🏋️ Contrastive Pre-training 🏋️ [8] Prepare for MatMul ↳ Copy text vectors (T1,T2,T3) ↳ Copy the transpose of image vectors (I1,I2,I3) ↳ They are all in the 2D shared embedding space. [9] 🟦 MatMul ↳ Multiply T and I matrices. ↳ This is equivalent to taking dot product between every pair of image and text vectors. ↳ The purpose is to use dot product to estimate the similarity between a pair of image-text. [10] 🟦 Softmax: e^x ↳ Raise e to the power of the number in each cell ↳ To simplify hand calculation, we approximate e^□ with 3^□. [11] 🟦 Softmax: ∑ ↳ Sum each row for 🟩 image→🟪 text ↳ Sum each column for 🟪 text→ 🟩 image [12] 🟦 Softmax: 1 / sum ↳ Divide each element by the column sum to obtain a similarity matrix for 🟪 text→🟩 image ↳ Divide each element by the row sum to obtain a similarity matrix for 🟩 image→🟪 text [13] 🟥 Loss Gradients ↳ The "Targets" for the similarity matrices are Identity Matrices. ↳ Why? If I and T come from the same pair (i=j), we want the highest value, which is 1, and 0 otherwise. ↳ Apply the simple equation of [Similarity - Target] to compute gradients of for both directions. ↳ Why so simple? Because when Softmax and Cross-Entropy Loss are used together, the math magically works out that way. ↳ These gradients kick off the backpropagation process to update weights and biases of the encoders and projection layers (red borders).
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[CLIP] by Hand ✍️ The CLIP (Contrastive Language–Image Pre-training) model, a groundbreaking work by OpenAI, redefines the intersection of computer vision and natural language processing. It is the basis of all the multi-modal foundation models we see today. How does CLIP work? Goal: 🟨 Learn a shared embedding space for text and image [1] Given ↳ A mini batch of 3 text-image pairs ↳ OpenAI used 400 million text-image pairs to train its original CLIP model. Process 1st pair: "big table" [2] 🟪 Text → 2 Vectors (3D) ↳ Look up word embedding vectors using word2vec. [3] 🟩 Image → 2 Vectors (4D) ↳ Divide the image into two patches. ↳ Flatten each patch [4] Process other pairs ↳ Repeat [2]-[3] [5] 🟪 Text Encoder & 🟩 Image Encoder ↳ Encode input vectors into feature vectors ↳ Here, both encoders are simple one layer perceptron (linear + ReLU) ↳ In practice, the encoders are usually transformer models. [6] 🟪 🟩 Mean Pooling: 2 → 1 vector ↳ Average 2 feature vectors into a single vector by averaging across the columns ↳ The goal is to have one vector to represent each image or text [7] 🟪 🟩 -> 🟨 Projection ↳ Note that the text and image feature vectors from the encoders have different dimensions (3D vs. 4D). ↳ Use a linear layer to project image and text vectors to a 2D shared embedding space. 🏋️ Contrastive Pre-training 🏋️ [8] Prepare for MatMul ↳ Copy text vectors (T1,T2,T3) ↳ Copy the transpose of image vectors (I1,I2,I3) ↳ They are all in the 2D shared embedding space. [9] 🟦 MatMul ↳ Multiply T and I matrices. ↳ This is equivalent to taking dot product between every pair of image and text vectors. ↳ The purpose is to use dot product to estimate the similarity between a pair of image-text. [10] 🟦 Softmax: e^x ↳ Raise e to the power of the number in each cell ↳ To simplify hand calculation, we approximate e^□ with 3^□. [11] 🟦 Softmax: ∑ ↳ Sum each row for 🟩 image→🟪 text ↳ Sum each column for 🟪 text→ 🟩 image [12] 🟦 Softmax: 1 / sum ↳ Divide each element by the column sum to obtain a similarity matrix for 🟪 text→🟩 image ↳ Divide each element by the row sum to obtain a similarity matrix for 🟩 image→🟪 text [13] 🟥 Loss Gradients ↳ The "Targets" for the similarity matrices are Identity Matrices. ↳ Why? If I and T come from the same pair (i=j), we want the highest value, which is 1, and 0 otherwise. ↳ Apply the simple equation of [Similarity - Target] to compute gradients of for both directions. ↳ Why so simple? Because when Softmax and Cross-Entropy Loss are used together, the math magically works out that way. ↳ These gradients kick off the backpropagation process to update weights and biases of the encoders and projection layers (red borders).

Tom Yeh

67,834 views • 2 years ago

we’re happy to share the latest software update for r1. this is an over-the-air (OTA) update which will automatically be downloaded and installed to your device. try these prompts with r1 to see the new UI in action: search with image and text: “who is taylor swift?” “what is the summer solstice?” “search for examples of impressionism” image search: “get me images of monarch butterflies” long form text response: “give me a low-carb recipe for banana pancakes” “can you make me a 3-day upper-body workout plan” *different prompts will trigger different AI models so a picture is not guaranteed all the time; r1 makes the final call 🐰 *dm us your favorite use cases
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we’re happy to share the latest software update for r1. this is an over-the-air (OTA) update which will automatically be downloaded and installed to your device. try these prompts with r1 to see the new UI in action: search with image and text: “who is taylor swift?” “what is the summer solstice?” “search for examples of impressionism” image search: “get me images of monarch butterflies” long form text response: “give me a low-carb recipe for banana pancakes” “can you make me a 3-day upper-body workout plan” *different prompts will trigger different AI models so a picture is not guaranteed all the time; r1 makes the final call 🐰 *dm us your favorite use cases

rabbit inc.

48,170 views • 2 years ago

Make money with OpenClaw/ Clawdbot in 7 days. This OpenClaw Made $113,73 Autonomously and Now I’m Scared. You only need ChatGPT + a laptop + 1 hour/day to make $5,000/month. I’ve made the exact step-by-step guide. Normally priced at $200, but it’s free for 24 hours. To get it: 1. Comment "OpenClaw", I will send you in DM. 2. Like and Retweet this post. Note: You Must Follow me Himanshu Kumar, I can DM you
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Make money with OpenClaw/ Clawdbot in 7 days. This OpenClaw Made $113,73 Autonomously and Now I’m Scared. You only need ChatGPT + a laptop + 1 hour/day to make $5,000/month. I’ve made the exact step-by-step guide. Normally priced at $200, but it’s free for 24 hours. To get it: 1. Comment "OpenClaw", I will send you in DM. 2. Like and Retweet this post. Note: You Must Follow me Himanshu Kumar, I can DM you

Himanshu Kumar

155,870 views • 4 months ago

It's finally here..... The most powerful cinematic tool in the world, Big Motion x Seedance 2.0 Get access right now or wait for 1 year when it officially launches lol We are giving away INSTANT ACCESS credits to 30 lucky winners. - Like and repost this post - Comment/quote with a Seedance 2.0 video idea We will pick the winners in 6 hours. Cinematic video, Hollywood animation, consistent characters, studio-level VFX... All just one prompt away. Find the tool link below 👇
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It's finally here..... The most powerful cinematic tool in the world, Big Motion x Seedance 2.0 Get access right now or wait for 1 year when it officially launches lol We are giving away INSTANT ACCESS credits to 30 lucky winners. - Like and repost this post - Comment/quote with a Seedance 2.0 video idea We will pick the winners in 6 hours. Cinematic video, Hollywood animation, consistent characters, studio-level VFX... All just one prompt away. Find the tool link below 👇

BigMotion.ai

179,729 views • 5 months ago

Make money with OpenClaw/ Clawdbot in 7 days. My OpenClaw Made $113,73 Autonomously and Now I’m Scared. You only need ChatGPT + a laptop + 1 hour/day to make $5,000/month. I’ve prepared the exact step-by-step guide. Normally priced at $200, but it’s free for 24 hours. To get it: 1. Comment "money", I will send you in DM. 2. Like and Retweet this post. Note: You Must Follow me Himanshu Kumar, I can DM you
3:18

Make money with OpenClaw/ Clawdbot in 7 days. My OpenClaw Made $113,73 Autonomously and Now I’m Scared. You only need ChatGPT + a laptop + 1 hour/day to make $5,000/month. I’ve prepared the exact step-by-step guide. Normally priced at $200, but it’s free for 24 hours. To get it: 1. Comment "money", I will send you in DM. 2. Like and Retweet this post. Note: You Must Follow me Himanshu Kumar, I can DM you

Himanshu Kumar

176,529 views • 5 months ago

RunwayML is not limited to 4sec! Here's how you can bypass the limitations 👇 Step 1: Generate your video with Runway Step 2: Get ffmpeg Step 3: Grab the last frame of your video using this command ffmpeg -sseof -3 -i [input_file_name.mp4] -update 1 -q:v 1 [output_file_name.jpg] Step 4 : Use the image output as the input for your next RunwayML generation Step 5: Stich them together in your favorite editing software Step 6: This is the most important step! Like, RT and follow for more good stuff, thanks! ♥️🤗 It's not perfect, but it's a start, I added 5 clips in total in the video below, just so you can see how it evolves over time. With a lot of generations eventually it can look decent for 2-3 clips. Voice by ElevenLabs Music and SFX by Epidemic Sound Sound mixing with Ableton
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RunwayML is not limited to 4sec! Here's how you can bypass the limitations 👇 Step 1: Generate your video with Runway Step 2: Get ffmpeg Step 3: Grab the last frame of your video using this command ffmpeg -sseof -3 -i [input_file_name.mp4] -update 1 -q:v 1 [output_file_name.jpg] Step 4 : Use the image output as the input for your next RunwayML generation Step 5: Stich them together in your favorite editing software Step 6: This is the most important step! Like, RT and follow for more good stuff, thanks! ♥️🤗 It's not perfect, but it's a start, I added 5 clips in total in the video below, just so you can see how it evolves over time. With a lot of generations eventually it can look decent for 2-3 clips. Voice by ElevenLabs Music and SFX by Epidemic Sound Sound mixing with Ableton

Alex Patrascu

535,642 views • 3 years ago

Ok finally dug into Meta's new Movie Gen paper. Text-to-video is cool and all but, to me the precise editing feature is the game changer. I mean just look at these results 🤯 It can handle complex VFX tasks like replacing environments, doing set extensions, swapping characters, removing items, adding particle effects with realistic lighting interaction. The coolest bit to me is how they trained this model, because paired before/after vfx editing datasets are super scarce. TL;DR They taught it video editing through a clever three-stage process: 1. Started with image editing data, treating it like single-frame video edits. 2. Created synthetic video editing tasks by animating still image edits and using AI models (like SAM and DINO) for object segmentation. 3. The model generated edited videos, and then learned to reconstruct the originals from the edited version Meta calls this "video editing via backtranslation" and the results speak for themselves.
0:23

Ok finally dug into Meta's new Movie Gen paper. Text-to-video is cool and all but, to me the precise editing feature is the game changer. I mean just look at these results 🤯 It can handle complex VFX tasks like replacing environments, doing set extensions, swapping characters, removing items, adding particle effects with realistic lighting interaction. The coolest bit to me is how they trained this model, because paired before/after vfx editing datasets are super scarce. TL;DR They taught it video editing through a clever three-stage process: 1. Started with image editing data, treating it like single-frame video edits. 2. Created synthetic video editing tasks by animating still image edits and using AI models (like SAM and DINO) for object segmentation. 3. The model generated edited videos, and then learned to reconstruct the originals from the edited version Meta calls this "video editing via backtranslation" and the results speak for themselves.

Bilawal Sidhu

50,775 views • 1 year ago