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Want to use Depth Anything, but need metric depth rather than relative depth? Thrilled to introduce Prompt Depth Anything, a new paradigm for accurate metric depth estimation with up to 4K resolution. 👉Key Message: Depth foundation models like DA have already internalized rich geometric knowledge of the 3D world... but lack a proper way to elicit it. Inspired by the success of prompting in LLMs, we propose prompting Depth Anything with metric cues to produce metric depth. This method proves to be very effective when using a low-cost lidar (e.g., iPhone's LiDAR), which is widely available, as prompts. We believe the prompt can generalize to other forms as long as scale information is provided. Prompt Depth Anything offers 1⃣A series of models for iPhone lidars. 2⃣4D reconstruction from monocular videos (captured with iPhone). 3⃣Improved generalization ability for robot manipulation, e.g. Training on cans but generalizing on glasses. 4⃣More detailed depth annotations for the ScanNet++ dataset. The first author is our excellent intern Haotong Lin. Paper: Huggingface: Project Page: Code:show more

Bingyi Kang

3,648 subscribers

67,750 次观看 • 1 年前 •via X (Twitter)

健康养生科学技术教育

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Private livecam show

9 条评论

Bingyi Kang 的头像

Bingyi Kang1 年前

This project would not have been possible without the wonderful collaboration of @pengsida, Jingxiao Chen, @songyoupeng, @JiamingSuen, @ericliuof97, Hujun Bao, Jiashi Feng and @XiaoweiZhou5.

Bingyi Kang 的头像

Bingyi Kang1 年前

Check out for some interactive demos there:

Bingyi Kang 的头像

Bingyi Kang1 年前

We thank @huggingface for the generous support in serving our demo. @_akhaliq @pcuenq

Patryk Zoltowski 的头像

Patryk Zoltowski1 年前

Any reason why cannot use depth from front Truedepth camera? They are also available in ARKit (with head tracking config) and from my experience higher quality and bigger resolution (640x480) than those from Lidar. However only provided at 30fps.

Basid 的头像

Basid1 年前

exactly what I needed, good work

Redmond Hosting 的头像

Redmond Hosting1 年前

What about using this to upscale depth from a multi-view depth model like Depth Crafter?

Bingyi Kang 的头像

Bingyi Kang1 年前

The same idea can be applied to this setting. But the models released are only for iPhone's Lidar.

Xiaoyu Xiang 的头像

Xiaoyu Xiang1 年前

Wow, a strong baseline to compare in our upcoming paper!

hantmango 的头像

hantmango1 年前

Don't have Iphone....

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After a year of team work, we're thrilled to introduce Depth Anything 3 (DA3)! 🚀 Aiming for human-like spatial perception, DA3 extends monocular depth estimation to any-view scenarios, including single images, multi-view images, and video. In pursuit of minimal modeling, DA3 reveals two key insights: 💎 A plain transformer (e.g., vanilla DINO) is enough. No specialized architecture. ✨ A single depth-ray representation is enough. No complex 3D tasks. Three series of models have been released: the main DA3 series, a monocular metric estimation series, and a monocular depth estimation series. The core team members, aside from me: Haotong Lin, Sili Chen, Jun Hao Liew, Donny Y. Chen. 👇(1/n) #DepthAnything3

After a year of team work, we're thrilled to introduce Depth Anything 3 (DA3)! 🚀 Aiming for human-like spatial perception, DA3 extends monocular depth estimation to any-view scenarios, including single images, multi-view images, and video. In pursuit of minimal modeling, DA3 reveals two key insights: 💎 A plain transformer (e.g., vanilla DINO) is enough. No specialized architecture. ✨ A single depth-ray representation is enough. No complex 3D tasks. Three series of models have been released: the main DA3 series, a monocular metric estimation series, and a monocular depth estimation series. The core team members, aside from me: Haotong Lin, Sili Chen, Jun Hao Liew, Donny Y. Chen. 👇(1/n) #DepthAnything3

Bingyi Kang

516,195 次观看 • 10 个月前

In collaboration with Intel, our Depth Fusion showcases the power of our LDM3D diffusion model in generating 360° views from text prompts provided by the user. The LDM3D diffusion model generates a 2D RGB image and its corresponding relative depth map providing a complete RGBD representation corresponding to the text prompt. The LDM 3D model is a specialized version of the stable diffusion V 1.4 model that has been modified to fit both image and depth map data.The model was then fine tuned on a subset of the Laion400M data set - large scale image caption data set. The depth maps used to fine tune our model were generated by the DPTBeiT large 512 depth estimation model that provides highly accurate relative depth estimates for each pixel. We take the generated 2D RGB image and depth map and use them to compute a 360° projection using touchdesigner. Touchdesigner is a versatile platform that allows for the creation of immersive and interactive multimedia experiences. Our application harnesses the power of touchdesigner to bring the generated 360° views to life, providing users with a unique and engaging way to experience their text prompts, whether it’s a description of a tranquil forest, a noisy cityscape or a futuristic sci fi world. Our depth fusion can bring these concepts to life in a vivid and immersive detail. - Scottie Fox, VP Engineering Blockade Labs ScottieFox #AI #VR #3D #gamedev #stablediffusion

In collaboration with Intel, our Depth Fusion showcases the power of our LDM3D diffusion model in generating 360° views from text prompts provided by the user. The LDM3D diffusion model generates a 2D RGB image and its corresponding relative depth map providing a complete RGBD representation corresponding to the text prompt. The LDM 3D model is a specialized version of the stable diffusion V 1.4 model that has been modified to fit both image and depth map data.The model was then fine tuned on a subset of the Laion400M data set - large scale image caption data set. The depth maps used to fine tune our model were generated by the DPTBeiT large 512 depth estimation model that provides highly accurate relative depth estimates for each pixel. We take the generated 2D RGB image and depth map and use them to compute a 360° projection using touchdesigner. Touchdesigner is a versatile platform that allows for the creation of immersive and interactive multimedia experiences. Our application harnesses the power of touchdesigner to bring the generated 360° views to life, providing users with a unique and engaging way to experience their text prompts, whether it’s a description of a tranquil forest, a noisy cityscape or a futuristic sci fi world. Our depth fusion can bring these concepts to life in a vivid and immersive detail. - Scottie Fox, VP Engineering Blockade Labs ScottieFox #AI #VR #3D #gamedev #stablediffusion

Blockade Labs

11,458 次观看 • 3 年前

The robot’s “eyes” just received a big upgrade. LingBot-Depth 2.0, a depth-completion model with half the depth error just dropped. 12/16 benchmarks topped. Glass, mirrors, and transparent objects are so easy for us humans, but so hard for robots, because they do not behave like ordinary surfaces in a camera pipeline. A robot that misunderstands a balcony window or a table edge, will have a completely false planning inside a false world. Huge implecation. LingBot-Depth 2.0 takes an RGB image plus a broken depth map from a sensor and then outputs a cleaner depth map and a usable 3D point cloud. Numbers on LingBot-Depth 2.0 - Excels on glass, mirrors & transparent objects — where traditional depth cameras fail - Training data: 3M → 150M (50x scale-up) - 12 out of 16 first-place rankings on depth completion benchmarks - RMSE cut in half: 0.132 → 0.062 on the hardest indoor scenes LingBot-Vision trained on boundaries, because object edges carry the geometry robots need. No human boundary labels are used, which makes this approach easier to scale. The open-sourced LingBot-Vision is the general vision backbone, and LingBot-Depth 2.0 is the depth model built on it.

The robot’s “eyes” just received a big upgrade. LingBot-Depth 2.0, a depth-completion model with half the depth error just dropped. 12/16 benchmarks topped. Glass, mirrors, and transparent objects are so easy for us humans, but so hard for robots, because they do not behave like ordinary surfaces in a camera pipeline. A robot that misunderstands a balcony window or a table edge, will have a completely false planning inside a false world. Huge implecation. LingBot-Depth 2.0 takes an RGB image plus a broken depth map from a sensor and then outputs a cleaner depth map and a usable 3D point cloud. Numbers on LingBot-Depth 2.0 - Excels on glass, mirrors & transparent objects — where traditional depth cameras fail - Training data: 3M → 150M (50x scale-up) - 12 out of 16 first-place rankings on depth completion benchmarks - RMSE cut in half: 0.132 → 0.062 on the hardest indoor scenes LingBot-Vision trained on boundaries, because object edges carry the geometry robots need. No human boundary labels are used, which makes this approach easier to scale. The open-sourced LingBot-Vision is the general vision backbone, and LingBot-Depth 2.0 is the depth model built on it.

Rohan Paul

25,312 次观看 • 2 个月前

Excited to introduce StereoPolicy, led by Evans Han. 📷📷🤖StereoPolicy is an effective way to add geometric cues to modern robot policy models while keeping the strengths of pretrained 2D encoders. ⁉️Why stereo for robot manipulation? Monocular RGB often lacks the depth cues needed for precise manipulation, while RGB-D and point clouds can be noisy or brittle, especially on reflective and transparent objects in real-world deployment. Instead of explicitly reconstructing disparity, depth, or point clouds, StereoPolicy directly fuses synchronized left/right RGB views to learn implicit stereo cues, avoiding extra reconstruction latency that can make real-time manipulation difficult. Project Page:

Excited to introduce StereoPolicy, led by Evans Han. 📷📷🤖StereoPolicy is an effective way to add geometric cues to modern robot policy models while keeping the strengths of pretrained 2D encoders. ⁉️Why stereo for robot manipulation? Monocular RGB often lacks the depth cues needed for precise manipulation, while RGB-D and point clouds can be noisy or brittle, especially on reflective and transparent objects in real-world deployment. Instead of explicitly reconstructing disparity, depth, or point clouds, StereoPolicy directly fuses synchronized left/right RGB views to learn implicit stereo cues, avoiding extra reconstruction latency that can make real-time manipulation difficult. Project Page:

Ruohan Zhang

851,478 次观看 • 4 个月前

Are you tired of the low quality of iPhone lidar scans? I am! And that is why we are bringing this cutting-edge iPhone lidar scan enhancement function into production! With the guidance of normal and depth, the geometry can now reach the next level! Showcases: Please try our KIRI Engine 3.14 iOS version. Thanks to Xuqian and her AGSMesh paper ( which inspired us a lot, and also thanks to Haotong, Sida, and Jiaming for their stunning paper PromptDA ( which makes the depths way better. Of course, thanks to CJ and our intern team, Quanxiang and Ziteng, for helping with the development. #CVPR2025 #3DV2025 #GaussianSplatting #LiDAR

Are you tired of the low quality of iPhone lidar scans? I am! And that is why we are bringing this cutting-edge iPhone lidar scan enhancement function into production! With the guidance of normal and depth, the geometry can now reach the next level! Showcases: Please try our KIRI Engine 3.14 iOS version. Thanks to Xuqian and her AGSMesh paper ( which inspired us a lot, and also thanks to Haotong, Sida, and Jiaming for their stunning paper PromptDA ( which makes the depths way better. Of course, thanks to CJ and our intern team, Quanxiang and Ziteng, for helping with the development. #CVPR2025 #3DV2025 #GaussianSplatting #LiDAR

Chris make some 3D scans

105,842 次观看 • 1 年前

Standards are set to be met.....to guide, and shield us from certain mistakes, and irrational behaviors. As an individual, if you stand for nothing you fall for everything.....so your STANDARD is your WATCHWORD. Allow people stand on the business that pays them. Allow people wear the hair that fits them, the sunglasses that shield them, especially when they go out for "MEDICALS" As beauty is in the eyes of the beholder so is "DEPTH". In a entertainment reality show, where PERSONALITY is the focus, some have decided to mine "DEPTH OIL". Trust me all cards must be dealt, but assassinating the character of a player when you have equal access to play than be a paranoid commentator is crazy work. We went from; No depth Insecure Not all that Dumb girl syndrome And now Sugar baby So to the CEO DEPTH OIL LTD, we plead that you allow the NON-DEPTH breathe!!!!!🥹🥹🥹🥹 And it's NEPO BABY not SUGAR BABY. 🎥: Big Brother Naija MAIN CHARACTER DEDE #DedeAshiogwu #BBNaija

Standards are set to be met.....to guide, and shield us from certain mistakes, and irrational behaviors. As an individual, if you stand for nothing you fall for everything.....so your STANDARD is your WATCHWORD. Allow people stand on the business that pays them. Allow people wear the hair that fits them, the sunglasses that shield them, especially when they go out for "MEDICALS" As beauty is in the eyes of the beholder so is "DEPTH". In a entertainment reality show, where PERSONALITY is the focus, some have decided to mine "DEPTH OIL". Trust me all cards must be dealt, but assassinating the character of a player when you have equal access to play than be a paranoid commentator is crazy work. We went from; No depth Insecure Not all that Dumb girl syndrome And now Sugar baby So to the CEO DEPTH OIL LTD, we plead that you allow the NON-DEPTH breathe!!!!!🥹🥹🥹🥹 And it's NEPO BABY not SUGAR BABY. 🎥: Big Brother Naija MAIN CHARACTER DEDE #DedeAshiogwu #BBNaija

DEDE ASHIOGWU🌻

305,005 次观看 • 1 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

31,261 次观看 • 2 个月前

I've been playing more with this depth map motion transfer workflow, and if you're trying to recreate really specific body movements from a reference video, this works surprisingly well. Here's the workflow: 1. Grab your reference video, then create your character and environment references. 2. Run the video through Depth Anything on fal to generate a depth map: 3. Drop that depth map into Seedance Omni as your motion reference along with your character and environment references. You don't even need the original video. I also tested this against using the original video as the motion reference in Seedance, and there's definitely a noticeable difference. One thing I found, though, is that this works much better for body movement than facial performance since the depth map can't really capture subtle facial motion. I'm running more tests to see if there's a good way to control both body movement and facial performance. If you have any workflows I should try, do share in the comments!

I've been playing more with this depth map motion transfer workflow, and if you're trying to recreate really specific body movements from a reference video, this works surprisingly well. Here's the workflow: 1. Grab your reference video, then create your character and environment references. 2. Run the video through Depth Anything on fal to generate a depth map: 3. Drop that depth map into Seedance Omni as your motion reference along with your character and environment references. You don't even need the original video. I also tested this against using the original video as the motion reference in Seedance, and there's definitely a noticeable difference. One thing I found, though, is that this works much better for body movement than facial performance since the depth map can't really capture subtle facial motion. I'm running more tests to see if there's a good way to control both body movement and facial performance. If you have any workflows I should try, do share in the comments!

Curious Refuge

16,487 次观看 • 1 个月前

$all of this is code Sonnet 5.5 max made this 39s video in 17 hours w/ HyperFrames , prompt below concept: depth is the hierarchy. On a screen you look into, focus picks what matters and going deeper shows more. • One object: a black glass instrument with three windows, all showing the same tide reading, 3.67 m. • Three depths, three levels: the shallowest window shows only the number, the middle one adds range, high water and rate, the deepest adds a trend and twelve readings. • The camera pulls focus from window to window, so focus, not layout, decides what you read. It ends on the engraved line "Depth is the hierarchy". • The design idea: information hierarchy carried by optical depth (glass, refraction, real depth of field) instead of size, weight and position on a flat screen. The starting point was Ferrari Luce's layered display. • Built as one HyperFrames page: three.js glass and metal, live HTML text drawn into the 3D scene through HTML-in-Canvas, thin-lens depth of field, every frame deterministic.$

all of this is code Sonnet 5.5 max made this 39s video in 17 hours w/ HyperFrames , prompt below concept: depth is the hierarchy. On a screen you look into, focus picks what matters and going deeper shows more. • One object: a black glass instrument with three windows, all showing the same tide reading, 3.67 m. • Three depths, three levels: the shallowest window shows only the number, the middle one adds range, high water and rate, the deepest adds a trend and twelve readings. • The camera pulls focus from window to window, so focus, not layout, decides what you read. It ends on the engraved line "Depth is the hierarchy". • The design idea: information hierarchy carried by optical depth (glass, refraction, real depth of field) instead of size, weight and position on a flat screen. The starting point was Ferrari Luce's layered display. • Built as one HyperFrames page: three.js glass and metal, live HTML text drawn into the 3D scene through HTML-in-Canvas, thin-lens depth of field, every frame deterministic.

Miguel Ángel

28,935 次观看 • 2 天前

This is insane!🔥 This is a 720p (2D) video I shot of my kids 15 years ago! I used AI to upscale it, depth-map it, & used the depth map to convert it to 3D SBS, & then converted that to Spatial Video for Apple Vision Pro (MV-HEVC). My son is currently a senior in high school, and my daughter is a sophomore in college. This video has complete spatial depth when viewed in the Apple Vision Pro. Obviously, you can't appreciate that via this post. But it's like I'm there & I can see into this room. Talk about giving you the feels.🥹 This can be done with ANY video. 🤯 I'm working on refining the workflow, as it's a bit cumbersome. I plan to share it, but holy cow! I had to post this.

This is insane!🔥 This is a 720p (2D) video I shot of my kids 15 years ago! I used AI to upscale it, depth-map it, & used the depth map to convert it to 3D SBS, & then converted that to Spatial Video for Apple Vision Pro (MV-HEVC). My son is currently a senior in high school, and my daughter is a sophomore in college. This video has complete spatial depth when viewed in the Apple Vision Pro. Obviously, you can't appreciate that via this post. But it's like I'm there & I can see into this room. Talk about giving you the feels.🥹 This can be done with ANY video. 🤯 I'm working on refining the workflow, as it's a bit cumbersome. I plan to share it, but holy cow! I had to post this.

Blaine Brown

1,468,863 次观看 • 2 年前

Meta presents Sapiens Foundation for Human Vision Models discuss: We present Sapiens, a family of models for four fundamental human-centric vision tasks - 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction. Our models natively support 1K high-resolution inference and are extremely easy to adapt for individual tasks by simply fine-tuning models pretrained on over 300 million in-the-wild human images. We observe that, given the same computational budget, self-supervised pretraining on a curated dataset of human images significantly boosts the performance for a diverse set of human-centric tasks. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic. Our simple model design also brings scalability - model performance across tasks improves as we scale the number of parameters from 0.3 to 2 billion. Sapiens consistently surpasses existing baselines across various human-centric benchmarks. We achieve significant improvements over the prior state-of-the-art on Humans-5K (pose) by 7.6 mAP, Humans-2K (part-seg) by 17.1 mIoU, Hi4D (depth) by 22.4% relative RMSE, and THuman2 (normal) by 53.5% relative angular error.

Meta presents Sapiens Foundation for Human Vision Models discuss: We present Sapiens, a family of models for four fundamental human-centric vision tasks - 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction. Our models natively support 1K high-resolution inference and are extremely easy to adapt for individual tasks by simply fine-tuning models pretrained on over 300 million in-the-wild human images. We observe that, given the same computational budget, self-supervised pretraining on a curated dataset of human images significantly boosts the performance for a diverse set of human-centric tasks. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic. Our simple model design also brings scalability - model performance across tasks improves as we scale the number of parameters from 0.3 to 2 billion. Sapiens consistently surpasses existing baselines across various human-centric benchmarks. We achieve significant improvements over the prior state-of-the-art on Humans-5K (pose) by 7.6 mAP, Humans-2K (part-seg) by 17.1 mIoU, Hi4D (depth) by 22.4% relative RMSE, and THuman2 (normal) by 53.5% relative angular error.

AK

151,614 次观看 • 2 年前

Warming up before coming back to YT. Today: the "clinical look" in web design. Sometimes a design feels too clean. Too perfect. Too digital. It looks polished, but also flat, with no depth, texture, or character. Making things feel less perfect in Figma can actually be pretty hard. In photography, people often switch to vintage lenses to get a softer, less digital look. In web design, we can do something similar by changing the tools we use. Here are some of the tools we use: Effect app - quick way to add depth of field, motion blur, CRT emulation, pixelation, and other effects. DaVinci Resolve / After Effects - better for motion blur, depth of field, lens simulation, and grain. More control, but more complex. Photoshop / Affinity Designer - useful for textures, brushes. Blender / Cinema 4D - great for natural light simulation, complex animation, and almost any visual effect you can imagine. WebGL - increasingly useful for building micro-apps that create a specific look. Try Gemini 3.1 Pro for that. If you like this format, let me know. I’m trying to find something useful for you, but easier to produce than the videos we were making a year ago.

Warming up before coming back to YT. Today: the "clinical look" in web design. Sometimes a design feels too clean. Too perfect. Too digital. It looks polished, but also flat, with no depth, texture, or character. Making things feel less perfect in Figma can actually be pretty hard. In photography, people often switch to vintage lenses to get a softer, less digital look. In web design, we can do something similar by changing the tools we use. Here are some of the tools we use: Effect app - quick way to add depth of field, motion blur, CRT emulation, pixelation, and other effects. DaVinci Resolve / After Effects - better for motion blur, depth of field, lens simulation, and grain. More control, but more complex. Photoshop / Affinity Designer - useful for textures, brushes. Blender / Cinema 4D - great for natural light simulation, complex animation, and almost any visual effect you can imagine. WebGL - increasingly useful for building micro-apps that create a specific look. Try Gemini 3.1 Pro for that. If you like this format, let me know. I’m trying to find something useful for you, but easier to produce than the videos we were making a year ago.

Alex Barashkov

11,989 次观看 • 6 个月前

Alibaba presents MIMO Controllable Character Video Synthesis with Spatial Decomposed Modeling Character video synthesis aims to produce realistic videos of animatable characters within lifelike scenes. As a fundamental problem in the computer vision and graphics community, 3D works typically require multi-view captures for per-case training, which severely limits their applicability of modeling arbitrary characters in a short time. Recent 2D methods break this limitation via pre-trained diffusion models, but they struggle for pose generality and scene interaction. To this end, we propose MIMO, a novel framework which can not only synthesize character videos with controllable attributes (i.e., character, motion and scene) provided by simple user inputs, but also simultaneously achieve advanced scalability to arbitrary characters, generality to novel 3D motions, and applicability to interactive real-world scenes in a unified framework. The core idea is to encode the 2D video to compact spatial codes, considering the inherent 3D nature of video occurrence. Concretely, we lift the 2D frame pixels into 3D using monocular depth estimators, and decompose the video clip to three spatial components (i.e., main human, underlying scene, and floating occlusion) in hierarchical layers based on the 3D depth. These components are further encoded to canonical identity code, structured motion code and full scene code, which are utilized as control signals of synthesis process. The design of spatial decomposed modeling enables flexible user control, complex motion expression, as well as 3D-aware synthesis for scene interactions. Experimental results demonstrate effectiveness and robustness of the proposed method.

Alibaba presents MIMO Controllable Character Video Synthesis with Spatial Decomposed Modeling Character video synthesis aims to produce realistic videos of animatable characters within lifelike scenes. As a fundamental problem in the computer vision and graphics community, 3D works typically require multi-view captures for per-case training, which severely limits their applicability of modeling arbitrary characters in a short time. Recent 2D methods break this limitation via pre-trained diffusion models, but they struggle for pose generality and scene interaction. To this end, we propose MIMO, a novel framework which can not only synthesize character videos with controllable attributes (i.e., character, motion and scene) provided by simple user inputs, but also simultaneously achieve advanced scalability to arbitrary characters, generality to novel 3D motions, and applicability to interactive real-world scenes in a unified framework. The core idea is to encode the 2D video to compact spatial codes, considering the inherent 3D nature of video occurrence. Concretely, we lift the 2D frame pixels into 3D using monocular depth estimators, and decompose the video clip to three spatial components (i.e., main human, underlying scene, and floating occlusion) in hierarchical layers based on the 3D depth. These components are further encoded to canonical identity code, structured motion code and full scene code, which are utilized as control signals of synthesis process. The design of spatial decomposed modeling enables flexible user control, complex motion expression, as well as 3D-aware synthesis for scene interactions. Experimental results demonstrate effectiveness and robustness of the proposed method.

AK

149,079 次观看 • 2 年前

With all due respect to my friend Doomer, the reason they didn’t use selective focus on the Rocky stair shot is because they couldn’t. The tech wasn’t there yet. Rocky was the first film the use a Steadicam. This was groundbreaking tech and changed cinematic language forever. That's the reason the stair scene is so iconic--because audiences had never seen anything like it before. If we were doing that shot today, we’d have a focus puller (called a 1st AC in the USA) manually focusing the lens on a wireless follow focus. Because there’s no other way to pull focus with a fast moving steadi cam following a fast moving subject. With a shallow depth of field Rocky would have been running in and out of focus. But back then they had no choice. So they used the largest depth of field they could get away with so that Rocky stayed in focus the entire time. If they’d had the tech to do selective focus they 100% would have used it--because cinema is all about selective focus. Steadicam inventor Garret Brown actually shot a test with his GF running on the same stairs used in Rocky (see video). And again, he was forced to use a deep depth of field because there was no way to accurately focus the camera while he was moving.

With all due respect to my friend Doomer, the reason they didn’t use selective focus on the Rocky stair shot is because they couldn’t. The tech wasn’t there yet. Rocky was the first film the use a Steadicam. This was groundbreaking tech and changed cinematic language forever. That's the reason the stair scene is so iconic--because audiences had never seen anything like it before. If we were doing that shot today, we’d have a focus puller (called a 1st AC in the USA) manually focusing the lens on a wireless follow focus. Because there’s no other way to pull focus with a fast moving steadi cam following a fast moving subject. With a shallow depth of field Rocky would have been running in and out of focus. But back then they had no choice. So they used the largest depth of field they could get away with so that Rocky stayed in focus the entire time. If they’d had the tech to do selective focus they 100% would have used it--because cinema is all about selective focus. Steadicam inventor Garret Brown actually shot a test with his GF running on the same stairs used in Rocky (see video). And again, he was forced to use a deep depth of field because there was no way to accurately focus the camera while he was moving.

Rambo Van Halen

20,578 次观看 • 10 个月前

In a November 25, 2019 (ONE MONTH BEFORE COVID-19 BEGAN) interview of Dr. Anthony Fauci on GZERO World with Ian Bremmer, titled “Worried Sick | Anthony Fauci In-depth Interview.” “And then the other thing that I aspirationally hope to be able to encounter is the ability to rapidly respond to something brand new, whether it’s a brand new pandemic or as you mentioned, a brand new attack upon us deliberately by bioterror. That’s where we have a ways to go, but we’re making progress in that regard.” - Dr. Anthony Fauci #COVID19 #Fauci

Nick D'Agostino

66,321 次观看 • 2 个月前

So many couples spend years managing the logistics of life together without ever stopping to ask the deeper questions: What does our love mean? Who are we becoming together? And what do we want our next chapter to look like? On June 19 - 22, 2026, Esther and I are gathering couples in Santa Fe for a retreat named The Meaning of Us. Just to be clear, this is not a couple therapy, but an opportunity to: - Gain a shared vocabulary for what you each most deeply want - Understand your relationship as the complex, living thing it actually is - Do the individual work that makes the relational work possible - See your differences as a source of depth rather than friction We'd love to have you join us:

So many couples spend years managing the logistics of life together without ever stopping to ask the deeper questions: What does our love mean? Who are we becoming together? And what do we want our next chapter to look like? On June 19 - 22, 2026, Esther and I are gathering couples in Santa Fe for a retreat named The Meaning of Us. Just to be clear, this is not a couple therapy, but an opportunity to: - Gain a shared vocabulary for what you each most deeply want - Understand your relationship as the complex, living thing it actually is - Do the individual work that makes the relational work possible - See your differences as a source of depth rather than friction We'd love to have you join us:

Dr. Arthur Brooks

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