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Code and data are now online for CameraHMR, our state-of-the-art parametric 3D human pose and shape (HPS) estimation method that will appear at hashtag#3DV2025. There are 4 key contributions that make it so accurate and robust: 1. To get accurate 3D shape and pose as well as good alignment... to image features, you need to know the focal length of the camera. To solve this, we train HumanFOV to compute the field of view. 2. We introduce CameraHMR, which integrates HumanFOV into HMR2.0 to exploit the estimated focal length. 3. To get accurate pseudo ground truth (pGT) training data, we compute the focal length for images in 4DHumans dataset and modify SMPLify to take this into account. 4. But SMPLify only uses sparse 2D keypoints, which do not capture body shape. So we train a dense surface keypoint detector, DenseKP, on BEDLAM and run it on 4DHumans, resulting in improved body shape. The resulting method is CamSMPLify. We iterate training CameraHMR and running CamSMPLify on the training set initialized with CameraHMR. This results in much improved pGT for 4DHumans and a SOTA single-image HMR method.show more

Michael Black

93,794 subscribers

21,647 просмотров • 1 год назад •via X (Twitter)

Наука и технологии Образование Искусство

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Комментарии: 9

Фото профиля Jerin Philip

Jerin Philip1 год назад

Nice work, currently looking into using it. When is the release of CamSMPLify + HumanFoV + DenseKP expected? What's the hardware requirements to train this end-to-end? 4D-Humans mention 8 A100 GPUs for 7 days, does this require similar?

Фото профиля Michael Black

Michael Black1 год назад

All code will be out before 3DV takes place.

Фото профиля Digital Currency

Digital Currency2 лет назад

From 3D modeling to VR/AR development, our MSc in Metaverse program equips you with the technical skills to excel in the rapidly evolving digital world. Don't miss out—enroll today! #UNIC #MScMetaverse

Фото профиля Duke 'Burrito Haver' Zero

Duke 'Burrito Haver' Zero1 год назад

could you embed a sentiment layer keyed to facial expressions / body language?

Фото профиля Michael Black

Michael Black1 год назад

This method is just about pixels to parameters but, yes, relating these parameters to expressions and body language is something we are interested in. E.g., have a look at our work on generating moving people from audio:

Фото профиля lotsoflittleprojects

lotsoflittleprojects1 год назад

Will this model get folded into Meshcapade?

Фото профиля Michael Black

Michael Black1 год назад

The next Meshcapade release will have many goodies that go beyond CameraHMR. Coming soon!

Фото профиля Matt Jaynes

Matt Jaynes1 год назад

@camenduru Damn! Impressive!

Фото профиля JohnYue122333

JohnYue1223331 год назад

MPI, awesome!

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18,742 просмотров • 3 месяцев назад

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

AK

249,572 просмотров • 2 лет назад

There's a problem with 3D human pose & shape (HPS) estimation methods. You either get good 3D accuracy or good alignment with the image, but not both. Why? The current top methods use the wrong camera model. TokenHMR at #CVPR2024 analyzes the issue and presents a solution. (1/8)

There's a problem with 3D human pose & shape (HPS) estimation methods. You either get good 3D accuracy or good alignment with the image, but not both. Why? The current top methods use the wrong camera model. TokenHMR at #CVPR2024 analyzes the issue and presents a solution. (1/8)

Michael Black

80,462 просмотров • 2 лет назад