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Playing Simon Says with Gemma-2b and MediaPipe 

Gemma-2b এবং MediaPipe-এর সাথে সাইমন সেস খেলছে

Simon Says mit Gemma-2b und MediaPipe spielen

Jugando a Simon Says con Gemma-2b y MediaPipe

Jouer Simon Says avec Gemma-2b et MediaPipe

जेम्मा-2बी और मीडियापाइप के साथ साइमन सेज़ बजाना

Gemma-2b と MediaPipe を使用して Simon Says を再生する

Gemma-2b 및 MediaPipe를 사용하여 Simon Says 재생

Jogando Simon Says com Gemma-2b e MediaPipe

Игра в Simon Says с Gemma-2b и MediaPipe

Simon Says'ı Gemma-2b ve MediaPipe ile oynamak

Chơi Simon Says với Gemma-2b và MediaPipe

使用 Gemma-2b 和 MediaPipe 玩“西蒙说”游戏

By Robert Bell, CEO of 2B3D.com "Compassion" is not a word you see in a lot of business plans among top technology and financial firms. It may even be considered something of a liability for most businesses, but I don't see it that way. As the 2B3D team prepares for our upcoming Veterans Day NFT drop, we believe the compassionate aspect of our mission bears repeating: Compassion is our greatest strength, and underpins everything we do. was founded by me as well as my close friend, Andrew Bjarnsen, an Afghan War veteran and combat medic who himself struggles with PTSD. Andrew set out on this mission to help his fellow veterans through the use of virtual reality technology. 2B3D What we are creating at 2B3D (along with our partners at BiocoRx, Mason Labs, and Metabillionaire) is centered around a deep concern for the pain and plight of others. In fact, when we talk about the massive utility and gamification of our NFTs, it is purposely designed in response to the growing need for mental health treatment in a world that needs more empathy all-around- whether in daily 1-on-1 with our neighbors, business partners and fellow citizens, or when we’re thinking about others far removed from our own location and daily experiences. 2B3D is deep in the creation of a first-of-its-kind virtual reality medical environment (VRME) in the metaverse, architected specifically to to deliver free mental health services to military veterans in real-time- and largely to address just about everything that is inadequate about the current status quo for vets seeking these services. We’ll launch this in the first quarter of the new year. Our firm is owned and operated by military veterans, and we intend to head off the growing mental health crisis in America, starting with our at-risk military veterans. We are  deploying new treatments through virtual reality (VRx) that will radically improve the physical and mental health of veterans throughout the nation regardless of their location or distance from big cities. In the last several years, suicide rates have increased, and this burden of self-harm is disproportionately carried by veterans. and we feel something must be done. Veteran suicides rates are vastly outpacing those of the general US population It stands to reason, since resources are strained, the response is too slow. 2B3D’s mission is to build new technologies that can offer solutions. It is our passion. Director Christopher Jones of Public Health Service at the Center for Disease Control and Prevention testified in June that between 2001 to 2019, the rate of suicide In the general population rose nearly 30%, while among veterans the rate increased nearly 36%. What 2B3D is building is a realistic VR medical environment available online 24 hours a day through VR headsets, and in 2D through phones and desktop PCs. Veterans experiencing suicidal ideation (often exacerbated by PTSD symptoms and substance use disorder) will be able to access the 2B3D metaverse for immediate access to suicide prevention counselors, group therapy, meditation spaces, and / treatment. EMDR DBT Our realistic environments will not be just a ‘second-best’ option for suffering veterans, but a cutting-edge solution in an increasingly online society. Veterans who have seen combat or feel isolated are the most vulnerable to thoughts of suicide. 20-30% of veterans who commit suicide which affects divergent thinking, making it nearly impossible to break out of a mental spiral. At those times, seconds literally count. also screen positive for service-related injuries like Post Traumatic Stress Disorder Imagine it's 3 a.m. somewhere in the world where a veteran is suffering a mental health crisis forty-five minutes from the nearest emergency room. They may be considering suicide as an option to ending their pain, and they begin to spiral. Help through 2B3D will be available, always on standby. Rather than leave their home at 3 a.m. to get in a car or find public transportation in search of help, veterans will simply use their VR headset, phone, or computer to access our virtual medical environment equipped with mental health resources. In a safe, virtual space, they also have the option to talk with a live person. Even in the form of avatars, research has shown that this human connection can be enough to prevent a tragedy from occurring. Our funding will be as unique as our service. To that end, we've announced the launch of our Supporter Badge, an NFT collection of 3D PTSD support ribbons that will fund our virtual reality mental health solutions for military veterans. This collection will be available for purchase at this Veterans Day, Nov. 11, 2022. https://mint.2b3d.com By raising funds through the sale of our Supporter Badge NFT collection, we'll be able to provide military veterans with access to proven cognitive therapy modalities in safe and effective VR environments. The public sale price of each 3D ribbon will be $10 dollars. All proceeds go to helping us in our mission to help veterans, and 40% percent of the proceeds will be donated directly to veteran nonprofits and charities also fighting to save veteran lives. Those who are interested in helping veterans do not need experience with NFTs or cryptocurrency to purchase one of our Supporter Badges. They are available for purchase with a credit card on our website. There is no limit to the number of NFTs we can sell to help veterans. For those who love NFTs and helping veterans, the public release of an additional 2,880 unique Collector’s Pass NFTs will also be live on Nov. 11, 2022, with a floor price of .7 ETH each, or approximately $100 until sold out. Every Collector’s Pass is represented as a yet-to-be-revealed 3D object with unique properties like special graphics and rare traits, and future in-game applications. The number of available Collector’s Passes is set at exactly 2,880 - a number representing the 2,880 minutes in the span of two days in which, statistically, 44 veterans will take their own lives. Every minute counts. Join us. Launching Veteran’s Day at . https://mint.2b3d.com ### About Robert Bell Robert Bell is an entrepreneur, investor, and philanthropist who founded 2B3D Inc in 2020 as an all-in-one gaming studio, nonfungible token mint, auction house, and storage system. Bell’s mission was to push the bounds of virtual, augmented and mixed reality technologies as a way to help military veterans suffering from post-traumatic stress disorder. His mission to bring peace and healing to veterans is fueled by play-to-earn games, utility NFTs, and access to exclusive in-game cryptocurrency within a wider decentralized metaverse. Bell is also the founder and CEO of Bell Medical solutions. About 2B3D 2B3D is a military veteran-operated tech company specializing in web3 gaming, virtual reality, NFTs, and mental health solutions in the metaverse. 2B3D is currently developing a metaverse ecosystem using blockchain technology with play-to-earn games, an NFT marketplace and VR showrooms, and VR medical environments for therapy and meeting with healthcare professionals in real-time. The 2880 Genesis Collection NFT will drop on the eleventh day of the eleventh month, in honor of military veterans on Veterans Day. The 2B3D metaverse includes several core projects: VRx | Virtual healthcare with NFT prescriptions and real life professionals. NFTy150 | NFT marketplace, minting option and showroom. Topher's Inferno | Connecting enthusiastic gamers with ambitious developers. RestXP | B2B meeting rooms with a resting crypto reward. So Many Gods | A Play-to Earn, space-themed sci fi looter shooter. Sci-fi To learn more about 2B3D, visit or follow us on Twitter at @2B3Dinc. www.2B3D.com

The Ultimate Utility: 2B3D is Using NFTs to Save Military Veteran Lives

Blake 2B and Blake3 are slightly different algorithms. A while ago, I came up with the question of whether or not Siacoin should migrate to Blake3. This question was posted on . Reddit This question has intrigued me, but what intrigued me more or less was the answer. It would need to be a hardfork, and although Blake3 is considered multiple times faster by others, there are debates in regards to HAIFA construction vs. traditional Merkle trees and so forth. That said, a hardfork of Siacoin would likely be easy given the code is standardized, but according to devs on Reddit, a Blake3 upgrade is the wrong thing to be worried about. I do agree. Is Blake a good choice? However, I still had a curiosity in exploring Blake3. In one of my own projects, I have decided to let the founder integrate Blake3 as its core hashing mechanism. That project is (2nd version), and I think Blake3 would work as the hashing algo of choice for BitBadges. The whitepaper can be seen .  It is worth a read. BitBadges here Another fact I found interesting, is that besides Siacoin, another Blake2b adaptor was . Nano Nano utilizes Blake2b in regards to the digested seed hashing. In regards to Blake3 adaption, Nano and most of these other cryptos would likely need a hard fork over soft fork. Interestingly enough about Blake3, is there have been many adaptations. This includes a PHP Hash Blake3 , and Blake3 itself has a Rust and C implementation. class Blake3 is said to be a merkle tree on the inside, and the speed itself is substantially higher then Blake2b in regards to the they gave. benchmark test Conclusion Blake3 is utilized more towards CPU multithreading, and likely if a cryptocurrency wants the benefit of hashing, but aren't too focused on multithreading or parallelization, then Blake2b is still a decent choice. For me, as a person liking grid and verifiable forms of computing, I had the preference of Blake3. That said, it is nice that it was already available at the time starting development.

2B or to B3? That Is The Question... Blake 2B vs 3 for Siacoin

How AI Models Handle Complex Biomedical Reasoning

Authors:
(1) Xian Liu, Snap Inc., CUHK with Work done during an internship at Snap Inc.;
(2) Jian Ren, Snap Inc. with Corresponding author: jren@snapchat.com;
(3) Aliaksandr Siarohin, Snap Inc.;
(4) Ivan Skorokhodov, Snap Inc.;
(5) Yanyu Li, Snap Inc.;
(6) Dahua Lin, CUHK;
(7) Xihui Liu, HKU;
(8) Ziwei Liu, NTU;
(9) Sergey Tulyakov, Snap Inc. Table of Links Abstract and 1 Introduction 2 Related Work 3 Our Approach and 3.1 Preliminaries and Problem Setting 3.2 Latent Structural Diffusion Model 3.3 Structure-Guided Refiner 4 Human Verse Dataset 5 Experiments 5.1 Main Results 5.2 Ablation Study 6 Discussion and References A Appendix and A.1 Additional Quantitative Results A.2 More Implementation Details and A.3 More Ablation Study Results A.4 More User Study Details A.5 Impact of Random Seed and Model Robustness and A.6 Boarder Impact and Ethical Consideration A.7 More Comparison Results and A.8 Additional Qualitative Results A.9 Licenses 5 EXPERIMENTS Experimental Settings. For the comprehensive evaluation, we divide our comparisons into two settings: 1) Quantitative analysis. All the methods are tested on the same benchmark, using the same prompt with DDIM Scheduler (Song et al., 2020a) for 50 denoising steps to generate the same resolution images of 512 × 512. 2) Qualitative analysis. We generate high-resolution 1024 × 1024 results for each model with the officially provided best configurations, such as the prompt engineering, noise scheduler, and classifier-free guidance (CFG) scale. Note that we use the RGB output of the first-stage Latent Structural Diffusion Model for numerical comparison, while the improved results from the second-stage Structure-Guided Refiner are merely utilized for visual comparison. Datasets. We follow common practices in T2I generation (Yu et al., 2022a) and filter out a human subset from MS-COCO 2014 validation (Lin et al., 2014) for zero-shot evaluation. In particular, offthe-shelf human detector and pose estimator are used to obtain 8, 236 images with clearly-visible humans for evaluation. All the ground truth images are resized and center-cropped to 512 × 512. To guarantee fair comparisons, we train first-stage Latent Structural Diffusion on HumanVerse, which is a subset of public LAION-2B and COYO, to report quantitative metrics. In addition, an internal dataset is adopted to train second-stage Structure-Guided Refiner only for visually pleasing results. Comparison Methods. We compare with two categories of open-source SOTA works: 1) General T2I models, including SD (Rombach et al., 2022) (SD 1.x & 2.x), SDXL (Podell et al., 2023), and IF (DeepFloyd, 2023). 2) Controllable methods with pose condition. Notably, ControlNet (Zhang & Agrawala, 2023) and T2I-Adapter (Mou et al., 2023) can handle multiple structural signals like canny, depth, and normal, where we take their skeleton-conditioned variant for comparison. HumanSD (Ju et al., 2023b) is the most recent work that specializes in pose-guided human generation. Implementation Details. We resize and random-crop the RGB, depth, and normal to the target resolution of each stage. To enforce the model with size and location awareness, the original image height/width and crop coordinates are embedded in a similar way to time embedding (Podell et al., 2023). Our code is developed based on diffusers (von Platen et al., 2022). 1) For the Latent Structural Diffusion, we fine-tune the whole UNet from the pretrained SD-2.0-base to v-prediction (Salimans & Ho, 2022) in 512 × 512 resolution. The DDIMScheduler with improved noise schedule is used for both training and sampling. We train on 128 80G NVIDIA A100 GPUs in a batch size of 2, 048 for one week. 2) For the Structure-Guided Refiner, we choose SDXL-1.0-base as the frozen backbone and fine-tune to ϵ-prediction for high-resolution synthesis of 1024 × 1024. We train on 256 80G NVIDIA A100 GPUs in a batch size of 2, 048 for one week. The overall framework is optimized with AdamW (Kingma & Ba, 2015) in 1e − 5 learning rate, and 0.01 weight decay. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Xian Liu, Snap Inc., CUHK with Work done during an internship at Snap Inc.; (2) Jian Ren, Snap Inc. with Corresponding author: jren@snapchat.com; (3) Aliaksandr Siarohin, Snap Inc.; (4) Ivan Skorokhodov, Snap Inc.; (5) Yanyu Li, Snap Inc.; (6) Dahua Lin, CUHK; (7) Xihui Liu, HKU; (8) Ziwei Liu, NTU; (9) Sergey Tulyakov, Snap Inc. Authors: Authors: (1) Xian Liu, Snap Inc., CUHK with Work done during an internship at Snap Inc.; (2) Jian Ren, Snap Inc. with Corresponding author: jren@snapchat.com; (3) Aliaksandr Siarohin, Snap Inc.; (4) Ivan Skorokhodov, Snap Inc.; (5) Yanyu Li, Snap Inc.; (6) Dahua Lin, CUHK; (7) Xihui Liu, HKU; (8) Ziwei Liu, NTU; (9) Sergey Tulyakov, Snap Inc. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Our Approach and 3.1 Preliminaries and Problem Setting 3 Our Approach and 3.1 Preliminaries and Problem Setting 3.2 Latent Structural Diffusion Model 3.2 Latent Structural Diffusion Model 3.3 Structure-Guided Refiner 3.3 Structure-Guided Refiner 4 Human Verse Dataset 4 Human Verse Dataset 5 Experiments 5 Experiments 5.1 Main Results 5.1 Main Results 5.2 Ablation Study 5.2 Ablation Study 6 Discussion and References 6 Discussion and References A Appendix and A.1 Additional Quantitative Results A Appendix and A.1 Additional Quantitative Results A.2 More Implementation Details and A.3 More Ablation Study Results A.2 More Implementation Details and A.3 More Ablation Study Results A.4 More User Study Details A.4 More User Study Details A.5 Impact of Random Seed and Model Robustness and A.6 Boarder Impact and Ethical Consideration A.5 Impact of Random Seed and Model Robustness and A.6 Boarder Impact and Ethical Consideration A.7 More Comparison Results and A.8 Additional Qualitative Results A.7 More Comparison Results and A.8 Additional Qualitative Results A.9 Licenses A.9 Licenses 5 EXPERIMENTS Experimental Settings. For the comprehensive evaluation, we divide our comparisons into two settings: 1) Quantitative analysis. All the methods are tested on the same benchmark, using the same prompt with DDIM Scheduler (Song et al., 2020a) for 50 denoising steps to generate the same resolution images of 512 × 512. 2) Qualitative analysis. We generate high-resolution 1024 × 1024 results for each model with the officially provided best configurations, such as the prompt engineering, noise scheduler, and classifier-free guidance (CFG) scale. Note that we use the RGB output of the first-stage Latent Structural Diffusion Model for numerical comparison, while the improved results from the second-stage Structure-Guided Refiner are merely utilized for visual comparison. Experimental Settings. Datasets. We follow common practices in T2I generation (Yu et al., 2022a) and filter out a human subset from MS-COCO 2014 validation (Lin et al., 2014) for zero-shot evaluation. In particular, offthe-shelf human detector and pose estimator are used to obtain 8, 236 images with clearly-visible humans for evaluation. All the ground truth images are resized and center-cropped to 512 × 512. To guarantee fair comparisons, we train first-stage Latent Structural Diffusion on HumanVerse, which is a subset of public LAION-2B and COYO, to report quantitative metrics. In addition, an internal dataset is adopted to train second-stage Structure-Guided Refiner only for visually pleasing results. Datasets. Comparison Methods. We compare with two categories of open-source SOTA works: 1) General T2I models, including SD (Rombach et al., 2022) (SD 1.x & 2.x), SDXL (Podell et al., 2023), and IF (DeepFloyd, 2023). 2) Controllable methods with pose condition. Notably, ControlNet (Zhang & Agrawala, 2023) and T2I-Adapter (Mou et al., 2023) can handle multiple structural signals like canny, depth, and normal, where we take their skeleton-conditioned variant for comparison. HumanSD (Ju et al., 2023b) is the most recent work that specializes in pose-guided human generation. Comparison Methods. Implementation Details. We resize and random-crop the RGB, depth, and normal to the target resolution of each stage. To enforce the model with size and location awareness, the original image height/width and crop coordinates are embedded in a similar way to time embedding (Podell et al., 2023). Our code is developed based on diffusers (von Platen et al., 2022). 1) For the Latent Structural Diffusion, we fine-tune the whole UNet from the pretrained SD-2.0-base to v-prediction (Salimans & Ho, 2022) in 512 × 512 resolution. The DDIMScheduler with improved noise schedule is used for both training and sampling. We train on 128 80G NVIDIA A100 GPUs in a batch size of 2, 048 for one week. 2) For the Structure-Guided Refiner, we choose SDXL-1.0-base as the frozen backbone and fine-tune to ϵ-prediction for high-resolution synthesis of 1024 × 1024. We train on 256 80G NVIDIA A100 GPUs in a batch size of 2, 048 for one week. The overall framework is optimized with AdamW (Kingma & Ba, 2015) in 1e − 5 learning rate, and 0.01 weight decay. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

HyperHuman Outperforms ControlNet, SDXL, and Others in Groundbreaking Image Generation Tests

Authors:
(1) Xian Liu, Snap Inc., CUHK with Work done during an internship at Snap Inc.;
(2) Jian Ren, Snap Inc. with Corresponding author: jren@snapchat.com;
(3) Aliaksandr Siarohin, Snap Inc.;
(4) Ivan Skorokhodov, Snap Inc.;
(5) Yanyu Li, Snap Inc.;
(6) Dahua Lin, CUHK;
(7) Xihui Liu, HKU;
(8) Ziwei Liu, NTU;
(9) Sergey Tulyakov, Snap Inc. Table of Links Abstract and 1 Introduction 2 Related Work 3 Our Approach and 3.1 Preliminaries and Problem Setting 3.2 Latent Structural Diffusion Model 3.3 Structure-Guided Refiner 4 Human Verse Dataset 5 Experiments 5.1 Main Results 5.2 Ablation Study 6 Discussion and References A Appendix and A.1 Additional Quantitative Results A.2 More Implementation Details and A.3 More Ablation Study Results A.4 More User Study Details A.5 Impact of Random Seed and Model Robustness and A.6 Boarder Impact and Ethical Consideration A.7 More Comparison Results and A.8 Additional Qualitative Results A.9 Licenses 4 HUMANVERSE DATASET Large-scale datasets with high quality samples, rich annotations, and diverse distribution are crucial for image generation tasks (Schuhmann et al., 2022; Podell et al., 2023), especially in the human domain (Liu et al., 2016;Jiang et al., 2023). To facilitate controllable human generation of high-fidelity, we establish a comprehensive human dataset with extensive annotations named HumanVerse. Please kindly refer to Appendix A.9 for more details about the dataset and annotation resources we use. Dataset Preprocessing. We curate from two principled datasets: LAION-2B-en (Schuhmann et al., 2022) and COYO-700M (Byeon et al., 2022). To isolate human images, we employ YOLOS (Fang et al., 2021) for human detection. Specifically, only those images containing 1 to 3 human bounding boxes are retained, where people should be visible with an area ratio exceeding 15%. We further rule out samples of poor aesthetics (< 4.5) or low resolution (< 200 × 200). This yields a high-quality subset by eliminating blurry and over-small humans. Unlike existing models that mostly train on full-body humans of simple context (Zhang & Agrawala, 2023), our dataset encompasses a wider spectrum, including various backgrounds and partial human regions such as clothing and limbs. 2D Human Poses. 2D human poses (skeleton of joint keypoints), which serve as one of the most flexible and easiest obtainable coarse-level condition signals, are widely used in controllable human generation studies (Ju et al., 2023b). To achieve accurate keypoint annotations, we resort to MMPose (Contributors, 2020) as inference interface and choose ViTPose-H (Xu et al., 2022) as backbone that performs best over several pose estimation benchmarks. In particular, the per-instance bounding box, keypoint coordinates and confidence are labeled, including whole-body skeleton (133 keypoints), body skeleton (17 keypoints), hand (21 keypoints), and facial landmarks (68 keypoints). Depth and Surface-Normal Maps are fine-grained structures that reflect the spatial geometry of images (Wu et al., 2022), which are commonly used in conditional generation (Mou et al., 2023). We apply Omnidata (Eftekhar et al., 2021) for monocular depth and normal. The MiDaS (Ranftl et al., 2022) is further annotated following recent depth-to-image pipelines (Rombach et al., 2022). Outpaint for Accurate Annotations. Diffusion models have shown promising results on image inpainting and outpainting, where the appearance and structure of unseen regions can be hallucinated based on the visible parts. Motivated by this, we propose to outpaint each image for a more holistic view given that most off-the-shelf structure estimators are trained on the “complete” image views. Although the outpainted region may be imperfect with artifacts, it can complement a more comprehensive human structure. To this end, we utilize the powerful SD-Inpaint to outpaint the surrounding areas of the original canvas. These images are further processed by off-the-shelf estimators, where we only use the labeling within the original image region for more accurate annotations. Overall Statistics. In summary, COYO subset contains 90, 948, 474 (91M) images and LAION-2B subset contains 248, 396, 109 (248M) images, which is 18.12% and 20.77% of fullset, respectively. The whole annotation process takes 640 16/32G NVIDIA V100 GPUs for two weeks in parallel. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Xian Liu, Snap Inc., CUHK with Work done during an internship at Snap Inc.; (2) Jian Ren, Snap Inc. with Corresponding author: jren@snapchat.com; (3) Aliaksandr Siarohin, Snap Inc.; (4) Ivan Skorokhodov, Snap Inc.; (5) Yanyu Li, Snap Inc.; (6) Dahua Lin, CUHK; (7) Xihui Liu, HKU; (8) Ziwei Liu, NTU; (9) Sergey Tulyakov, Snap Inc. Authors: Authors: (1) Xian Liu, Snap Inc., CUHK with Work done during an internship at Snap Inc.; (2) Jian Ren, Snap Inc. with Corresponding author: jren@snapchat.com; (3) Aliaksandr Siarohin, Snap Inc.; (4) Ivan Skorokhodov, Snap Inc.; (5) Yanyu Li, Snap Inc.; (6) Dahua Lin, CUHK; (7) Xihui Liu, HKU; (8) Ziwei Liu, NTU; (9) Sergey Tulyakov, Snap Inc. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Our Approach and 3.1 Preliminaries and Problem Setting 3 Our Approach and 3.1 Preliminaries and Problem Setting 3.2 Latent Structural Diffusion Model 3.2 Latent Structural Diffusion Model 3.3 Structure-Guided Refiner 3.3 Structure-Guided Refiner 4 Human Verse Dataset 4 Human Verse Dataset 5 Experiments 5 Experiments 5.1 Main Results 5.1 Main Results 5.2 Ablation Study 5.2 Ablation Study 6 Discussion and References 6 Discussion and References A Appendix and A.1 Additional Quantitative Results A Appendix and A.1 Additional Quantitative Results A.2 More Implementation Details and A.3 More Ablation Study Results A.2 More Implementation Details and A.3 More Ablation Study Results A.4 More User Study Details A.4 More User Study Details A.5 Impact of Random Seed and Model Robustness and A.6 Boarder Impact and Ethical Consideration A.5 Impact of Random Seed and Model Robustness and A.6 Boarder Impact and Ethical Consideration A.7 More Comparison Results and A.8 Additional Qualitative Results A.7 More Comparison Results and A.8 Additional Qualitative Results A.9 Licenses A.9 Licenses 4 HUMANVERSE DATASET Large-scale datasets with high quality samples, rich annotations, and diverse distribution are crucial for image generation tasks (Schuhmann et al., 2022; Podell et al., 2023), especially in the human domain (Liu et al., 2016;Jiang et al., 2023). To facilitate controllable human generation of high-fidelity, we establish a comprehensive human dataset with extensive annotations named HumanVerse. Please kindly refer to Appendix A.9 for more details about the dataset and annotation resources we use. Dataset Preprocessing. We curate from two principled datasets: LAION-2B-en (Schuhmann et al., 2022) and COYO-700M (Byeon et al., 2022). To isolate human images, we employ YOLOS (Fang et al., 2021) for human detection. Specifically, only those images containing 1 to 3 human bounding boxes are retained, where people should be visible with an area ratio exceeding 15%. We further rule out samples of poor aesthetics (< 4.5) or low resolution (< 200 × 200). This yields a high-quality subset by eliminating blurry and over-small humans. Unlike existing models that mostly train on full-body humans of simple context (Zhang & Agrawala, 2023), our dataset encompasses a wider spectrum, including various backgrounds and partial human regions such as clothing and limbs. Dataset Preprocessing. 2D Human Poses. 2D human poses (skeleton of joint keypoints), which serve as one of the most flexible and easiest obtainable coarse-level condition signals, are widely used in controllable human generation studies (Ju et al., 2023b). To achieve accurate keypoint annotations, we resort to MMPose (Contributors, 2020) as inference interface and choose ViTPose-H (Xu et al., 2022) as backbone that performs best over several pose estimation benchmarks. In particular, the per-instance bounding box, keypoint coordinates and confidence are labeled, including whole-body skeleton (133 keypoints), body skeleton (17 keypoints), hand (21 keypoints), and facial landmarks (68 keypoints). 2D Human Poses. Depth and Surface-Normal Maps are fine-grained structures that reflect the spatial geometry of images (Wu et al., 2022), which are commonly used in conditional generation (Mou et al., 2023). We apply Omnidata (Eftekhar et al., 2021) for monocular depth and normal. The MiDaS (Ranftl et al., 2022) is further annotated following recent depth-to-image pipelines (Rombach et al., 2022). Depth and Surface-Normal Maps Outpaint for Accurate Annotations. Diffusion models have shown promising results on image inpainting and outpainting, where the appearance and structure of unseen regions can be hallucinated based on the visible parts. Motivated by this, we propose to outpaint each image for a more holistic view given that most off-the-shelf structure estimators are trained on the “complete” image views. Although the outpainted region may be imperfect with artifacts, it can complement a more comprehensive human structure. To this end, we utilize the powerful SD-Inpaint to outpaint the surrounding areas of the original canvas. These images are further processed by off-the-shelf estimators, where we only use the labeling within the original image region for more accurate annotations. Outpaint for Accurate Annotations. Overall Statistics. In summary, COYO subset contains 90, 948, 474 (91M) images and LAION-2B subset contains 248, 396, 109 (248M) images, which is 18.12% and 20.77% of fullset, respectively. The whole annotation process takes 640 16/32G NVIDIA V100 GPUs for two weeks in parallel. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

HumanVerse: A Richly Annotated Dataset for Realistic, Controllable Human Image Synthesis

*Credit of image: Unsplash (Tian-Shan Range, Wensu, Aksu, China) *Landsat, Sentinel, CBERS, and Amazonia satellites in the context of orbital-level multispectral imaging Nowadays, there are plenty of ways to access and use satellite imagery—here's a rundown of some of the main options I use in my personal projects, all of which are free :-) A (very) Quick Overview of Satellite Options First off, let's talk about the satellites and sensors themselves. There are many options today, each with unique capabilities. For agricultural and environmental analysis (what I do on a daily basis), two of the most commonly used satellite programs are Landsat and Sentinel. Each has been part of multiple missions with evolving sensors over the decades, offering increasingly rich data for various applications. Sentinel Sentinel-2, for example, is part of the European Copernicus program and specializes in multispectral imaging. As of now, there are three satellites in the Sentinel-2 series: Sentinel-2A, Sentinel-2B, and Sentinel-2C (the last one launched very recently, in September 2024, here). (Usually my first choice, as it can combine the best spatial, temporal and spectral resolutions). Landsat Landsat, managed by the U.S. Geological Survey (USGS) under the National Land Imaging Program, has been active since the 1970s. Its most recent mission is Landsat-9, which, like its predecessors, provides multispectral imaging for a variety of uses. With so many satellite programs, it's essential to choose based on your specific project needs. Some satellites focus on particular regions of the planet, while others have more global objectives, which leads me to also include other less conventional options here (on the international scene). Brazilian Satellites: CBERS and Amazonia. If you're interested in national programs, Brazil offers two noteworthy options: CBERS (China-Brazil Earth Resources Satellite): This Sino-Brazilian collaboration has launched several missions. The most recent, CBERS-4A, provides orbital-level multispectral imaging alongside earlier CBERS satellites with different sensors. Amazonia-1: Launched in 2021, this is Brazil's first entirely locally-developed satellite. While it offers multispectral imaging like the others, its spatial resolution is relatively lower. Its main goal is to monitor vast forested areas like the Amazon (as the name suggests). Future missions include Amazonia-1B and Amazonia-2. Of all the options above, I would say that the ideal scenario would be to use all available options to have the largest possible amount of imagery for your area. However, this approach raises at least two important questions to consider: What is the purpose of your analysis? (and whether a higher frequency of images is even necessary)
How will you handle processing so that all the different images are compatible for analysis? Where to Get Satellite Imagery Once you know your project's requirements and the sensors that meet them, there are several ways to access the data. Whether you need occasional imagery for reports or frequent data for large-scale analysis, here are the main platforms and acquisition methods: Official Data Portals Landsat: Earth Explorer
Sentinel: Copernicus Data Space Ecosystem
CBERS and Amazonia: INPE Image Catalog Other options include GloVis, for example. 2. Cloud Platforms Google Earth Engine (GEE): Access a huge catalog of satellite data, including Landsat, Sentinel, CBERS, and many other products. It includes a web-based code editor for quick use and APIs for JavaScript (there is a lot already ready to use in JS, so it's pretty much doable even if you're not familiar with the language—I, personally, am not) and Python.
Amazon Web Services (AWS): Landsat and Sentinel data are available on AWS. Check out the AWS Open Data Registry for details. APIs and Programmatic Access For more technical users, APIs and Python libraries are powerful tools: Sentinel API: Learn more
Landsat API: Learn more
CBERS and Amazonia API: INPE STAC Browser
Python Libraries: Libraries like pystac-client are great because they support multiple catalogs via the SpatioTemporal Asset Catalog (STAC) standard. With one library, you can access all these providers and much more. Third-Party Tools and Software Sentinel Hub: A commercial service offering easy access to Sentinel and other satellite imagery
Earth Data (NASA): A great option for additional datasets, similar to catalogs in GEE
GIS Software: Tools like QGIS and ArcGIS often integrate directly with satellite data repositories. In QGIS, you may need a plugin to fetch and process the data. Be sure to check the plugin's maintenance status for continuity. Final Thoughts The satellite and sensor landscape offers an incredible variety of options, but navigating them, especially in the beginning, can feel overwhelming. The good news? Once you identify your needs and the right sensors (lots of work to be done here!), there are plenty of tools and platforms—most of them free—to access the data and put it to work for your project. *Credit of image: Unsplash ( Tian-Shan Range, Wensu, Aksu, China) *Credit of image: Unsplash ( Tian-Shan Range, Wensu, Aksu, China ) Tian-Shan Range, Wensu, Aksu, China *Landsat, Sentinel, CBERS, and Amazonia satellites in the context of orbital-level multispectral imaging *Landsat, Sentinel, CBERS, and Amazonia satellites in the context of orbital-level multispectral imaging Nowadays, there are plenty of ways to access and use satellite imagery—here's a rundown of some of the main options I use in my personal projects, all of which are free :-) A (very) Quick Overview of Satellite Options A (very) Quick Overview of Satellite Options First off, let's talk about the satellites and sensors themselves. There are many options today, each with unique capabilities. For agricultural and environmental analysis (what I do on a daily basis), two of the most commonly used satellite programs are Landsat and Sentinel. Each has been part of multiple missions with evolving sensors over the decades, offering increasingly rich data for various applications. Sentinel Sentinel-2, for example, is part of the European Copernicus program and specializes in multispectral imaging. As of now, there are three satellites in the Sentinel-2 series: Sentinel-2A, Sentinel-2B, and Sentinel-2C (the last one launched very recently, in September 2024, here ). (Usually my first choice, as it can combine the best spatial, temporal and spectral resolutions). here Landsat Landsat, managed by the U.S. Geological Survey (USGS) under the National Land Imaging Program, has been active since the 1970s. Its most recent mission is Landsat-9, which, like its predecessors, provides multispectral imaging for a variety of uses. With so many satellite programs, it's essential to choose based on your specific project needs. Some satellites focus on particular regions of the planet, while others have more global objectives, which leads me to also include other less conventional options here (on the international scene). Brazilian Satellites: CBERS and Amazonia. If you're interested in national programs, Brazil offers two noteworthy options: Brazilian Satellites: Brazilian Satellites: CBERS (China-Brazil Earth Resources Satellite): This Sino-Brazilian collaboration has launched several missions. The most recent, CBERS-4A, provides orbital-level multispectral imaging alongside earlier CBERS satellites with different sensors. Amazonia-1: Launched in 2021, this is Brazil's first entirely locally-developed satellite. While it offers multispectral imaging like the others, its spatial resolution is relatively lower. Its main goal is to monitor vast forested areas like the Amazon (as the name suggests). Future missions include Amazonia-1B and Amazonia-2. Of all the options above, I would say that the ideal scenario would be to use all available options to have the largest possible amount of imagery for your area. However, this approach raises at least two important questions to consider: ideal use all available options What is the purpose of your analysis? (and whether a higher frequency of images is even necessary) How will you handle processing so that all the different images are compatible for analysis? What is the purpose of your analysis? (and whether a higher frequency of images is even necessary) purpose How will you handle processing so that all the different images are compatible for analysis? processing Where to Get Satellite Imagery Where to Get Satellite Imagery Once you know your project's requirements and the sensors that meet them, there are several ways to access the data. Whether you need occasional imagery for reports or frequent data for large-scale analysis, here are the main platforms and acquisition methods: Official Data Portals Official Data Portals Official Data Portals Landsat: Earth Explorer Sentinel: Copernicus Data Space Ecosystem CBERS and Amazonia: INPE Image Catalog Landsat: Earth Explorer Earth Explorer Sentinel: Copernicus Data Space Ecosystem Copernicus Data Space Ecosystem CBERS and Amazonia: INPE Image Catalog INPE Image Catalog Other options include GloVis , for example. GloVis 2. Cloud Platforms 2. Cloud Platforms Google Earth Engine (GEE): Access a huge catalog of satellite data, including Landsat, Sentinel, CBERS, and many other products. It includes a web-based code editor for quick use and APIs for JavaScript (there is a lot already ready to use in JS, so it's pretty much doable even if you're not familiar with the language—I, personally, am not) and Python. Amazon Web Services (AWS): Landsat and Sentinel data are available on AWS. Check out the AWS Open Data Registry for details. Google Earth Engine (GEE) : Access a huge catalog of satellite data, including Landsat, Sentinel, CBERS, and many other products. It includes a web-based code editor for quick use and APIs for JavaScript (there is a lot already ready to use in JS, so it's pretty much doable even if you're not familiar with the language—I, personally, am not) and Python. Google Earth Engine (GEE) Amazon Web Services (AWS): Landsat and Sentinel data are available on AWS. Check out the AWS Open Data Registry for details. AWS Open Data Registry APIs and Programmatic Access APIs and Programmatic Access APIs and Programmatic Access For more technical users, APIs and Python libraries are powerful tools: Sentinel API: Learn more Landsat API: Learn more CBERS and Amazonia API: INPE STAC Browser Python Libraries: Libraries like pystac-client are great because they support multiple catalogs via the SpatioTemporal Asset Catalog (STAC) standard. With one library, you can access all these providers and much more. Sentinel API : Learn more Sentinel API Landsat API : Learn more Landsat API CBERS and Amazonia API: INPE STAC Browser INPE STAC Browser Python Libraries: Libraries like pystac-client are great because they support multiple catalogs via the SpatioTemporal Asset Catalog (STAC) standard. With one library, you can access all these providers and much more. pystac-client Third-Party Tools and Software Third-Party Tools and Software Third-Party Tools and Software Sentinel Hub: A commercial service offering easy access to Sentinel and other satellite imagery Earth Data (NASA): A great option for additional datasets, similar to catalogs in GEE GIS Software: Tools like QGIS and ArcGIS often integrate directly with satellite data repositories. In QGIS, you may need a plugin to fetch and process the data. Be sure to check the plugin's maintenance status for continuity. Sentinel Hub: A commercial service offering easy access to Sentinel and other satellite imagery Sentinel Hub: Earth Data (NASA) : A great option for additional datasets, similar to catalogs in GEE Earth Data (NASA) GIS Software: Tools like QGIS and ArcGIS often integrate directly with satellite data repositories. In QGIS, you may need a plugin to fetch and process the data. Be sure to check the plugin's maintenance status for continuity. QGIS Final Thoughts The satellite and sensor landscape offers an incredible variety of options, but navigating them, especially in the beginning, can feel overwhelming. The good news? Once you identify your needs and the right sensors (lots of work to be done here!), there are plenty of tools and platforms—most of them free—to access the data and put it to work for your project.

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